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cbcoutinho bd09a1f361 Update index.yaml
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cbcoutinho 08820782be Update index.yaml
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cbcoutinho 936480ed9c Update index.yaml
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cbcoutinho f4a01f37e1 Update index.yaml
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cbcoutinho 76f3878b2a Update index.yaml
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cbcoutinho a7b4cfe672 Update index.yaml
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cbcoutinho bcdf3898b8 Update index.yaml
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cbcoutinho b341fca408 Update index.yaml
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cbcoutinho ac48e43a60 Update index.yaml
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cbcoutinho 85c71802d2 Update index.yaml
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cbcoutinho 7e2e8b2c8c Update index.yaml
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cbcoutinho 183269bf8f Update index.yaml
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cbcoutinho 47b6db5dc1 Update index.yaml
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cbcoutinho 99ee32e691 Update index.yaml
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cbcoutinho ffc64b855e Update index.yaml
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cbcoutinho 2c51b1188b Update index.yaml
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cbcoutinho 4392ae2a11 Update index.yaml
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cbcoutinho fa9fec7269 Update index.yaml
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cbcoutinho 9ac6e741d0 Update index.yaml
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cbcoutinho 174278a9c3 Update index.yaml
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cbcoutinho 419c80f80f Update index.yaml
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cbcoutinho bf916f24aa Update index.yaml
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cbcoutinho f01f64e8a4 Update index.yaml
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cbcoutinho 8064aebb0f Update index.yaml
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cbcoutinho dc1510a485 Update index.yaml
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cbcoutinho 33db76696a Update index.yaml
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cbcoutinho 4f5e4be5ec Update index.yaml
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cbcoutinho 1c3781fa95 Update index.yaml
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cbcoutinho 222fc1618c Update index.yaml
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cbcoutinho 4013c25c68 Update index.yaml
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cbcoutinho 0b475d5acb Update index.yaml
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cbcoutinho 758586d666 Update index.yaml
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cbcoutinho bdaec6a0c5 Update index.yaml
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cbcoutinho 599b665963 Update index.yaml
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cbcoutinho 9f86f86ec7 Update index.yaml
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cbcoutinho c4cfb027f0 Update index.yaml
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cbcoutinho 51c689c6a5 Update index.yaml
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cbcoutinho e01b053f22 Update index.yaml
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cbcoutinho c460f24e0d Update index.yaml
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cbcoutinho 3380aa2f56 Update index.yaml
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cbcoutinho aef852f73e Update index.yaml
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cbcoutinho acc1b9c9e2 Update index.yaml
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cbcoutinho 7d932b153d Update index.yaml
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cbcoutinho 93a23846c4 Update index.yaml
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cbcoutinho 0690088db2 Update index.yaml
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cbcoutinho 08270d9455 Update index.yaml
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cbcoutinho a8ce648566 Update index.yaml
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cbcoutinho e3c7a138ed Update index.yaml
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cbcoutinho 9ed9d570bd Update index.yaml
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cbcoutinho fdc6d1232e Update index.yaml
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cbcoutinho f86fd927e3 Update index.yaml
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cbcoutinho 379bd12e1d Update index.yaml
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cbcoutinho 00c0b15c2d Update index.yaml
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cbcoutinho 17cb4cb25d Update index.yaml
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cbcoutinho e1ffc2809c Update index.yaml
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cbcoutinho 377b1e2332 Update index.yaml
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cbcoutinho 05e196dedc Update index.yaml
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cbcoutinho 389c98cc74 Update index.yaml
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cbcoutinho 32c09c338d Update index.yaml
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cbcoutinho 9f574432be Update index.yaml
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cbcoutinho b29563630e Update index.yaml
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cbcoutinho dd2627edda Update index.yaml
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cbcoutinho 5a74cdca14 Update index.yaml
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cbcoutinho 6151d92158 Update index.yaml
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cbcoutinho ce4536cb2d Update index.yaml
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cbcoutinho 65cf635e71 Update index.yaml
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cbcoutinho ada1f57e59 Update index.yaml
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cbcoutinho f9949c920c Update index.yaml
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cbcoutinho 6d99728d9b Update index.yaml
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cbcoutinho 6bb5e77c7a Update index.yaml
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cbcoutinho 3890bf430f Update index.yaml
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cbcoutinho 53aada6669 Update README and index from chart release 2026-02-03 06:50:40 +00:00
cbcoutinho da6268002e Update index.yaml
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cbcoutinho bace73a6fe Update index.yaml
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cbcoutinho 9ffa11cd62 Update index.yaml
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cbcoutinho e2ba31985a Update index.yaml
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cbcoutinho c2c80d732f Update index.yaml
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cbcoutinho 58f5367b36 Update README and index from chart release 2025-12-26 15:17:47 +00:00
cbcoutinho f45250ad59 Update index.yaml
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189 changed files with 10518 additions and 45796 deletions
-138
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@@ -1,138 +0,0 @@
# Keycloak OAuth Configuration for Nextcloud MCP Server
#
# This configuration uses Keycloak as the OAuth/OIDC identity provider
# while still accessing Nextcloud APIs. Nextcloud's user_oidc app validates
# Keycloak bearer tokens and provisions users automatically.
#
# Architecture: Client → Keycloak (OAuth) → MCP Server → Nextcloud (user_oidc validates) → APIs
#
# This enables ADR-002 authentication patterns without admin credentials!
# ==============================================================================
# OAUTH PROVIDER SELECTION
# ==============================================================================
# OAuth provider: "keycloak" or "nextcloud" (default)
OAUTH_PROVIDER=keycloak
# ==============================================================================
# KEYCLOAK CONFIGURATION
# ==============================================================================
# Keycloak base URL (accessible from MCP server container)
KEYCLOAK_URL=http://keycloak:8080
# Keycloak realm name
KEYCLOAK_REALM=nextcloud-mcp
# OAuth client credentials (from Keycloak realm export or manual configuration)
KEYCLOAK_CLIENT_ID=nextcloud-mcp-server
KEYCLOAK_CLIENT_SECRET=mcp-secret-change-in-production
# OIDC discovery URL (auto-constructed from URL + realm, or specify explicitly)
KEYCLOAK_DISCOVERY_URL=http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration
# ==============================================================================
# NEXTCLOUD CONFIGURATION
# ==============================================================================
# Nextcloud URL (accessible from MCP server container)
# Used for API access - Keycloak tokens are validated by user_oidc app
NEXTCLOUD_HOST=http://app:80
# MCP server URL (for OAuth redirect URIs)
# This is the publicly accessible URL that OAuth clients connect to
NEXTCLOUD_MCP_SERVER_URL=http://localhost:8002
# Public Keycloak issuer URL (accessible from OAuth clients)
# If clients access Keycloak via a different URL than the internal one,
# set this to the public URL for OAuth flows
NEXTCLOUD_PUBLIC_ISSUER_URL=http://localhost:8888
# ==============================================================================
# REFRESH TOKEN STORAGE (ADR-002 Tier 1: Offline Access)
# ==============================================================================
# Enable offline_access scope to get refresh tokens
ENABLE_OFFLINE_ACCESS=true
# Encryption key for storing refresh tokens (generate with instructions below)
# IMPORTANT: Keep this secret! Tokens are encrypted at rest using this key.
#
# Generate a key:
# python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
#
# Example (DO NOT use this in production!):
# TOKEN_ENCRYPTION_KEY=your-base64-encoded-fernet-key-here
# Path to SQLite database for token storage
TOKEN_STORAGE_DB=/app/data/tokens.db
# ==============================================================================
# DOCKER COMPOSE NOTES
# ==============================================================================
# When running via docker-compose, the mcp-keycloak service is pre-configured
# with these environment variables. See docker-compose.yml for the full config.
#
# Start services:
# docker-compose up -d keycloak app mcp-keycloak
#
# View logs:
# docker-compose logs -f mcp-keycloak
#
# Check Keycloak realm:
# curl http://localhost:8888/realms/nextcloud-mcp/.well-known/openid-configuration
#
# Check user_oidc provider:
# docker compose exec app php occ user_oidc:provider keycloak
# ==============================================================================
# KEYCLOAK SETUP VERIFICATION
# ==============================================================================
# 1. Verify Keycloak is running and realm is imported:
# curl http://localhost:8888/realms/nextcloud-mcp/.well-known/openid-configuration
#
# 2. Verify Nextcloud user_oidc provider is configured:
# docker compose exec app php occ user_oidc:provider keycloak
#
# 3. Test OAuth flow manually:
# - Get token from Keycloak:
# curl -X POST "http://localhost:8888/realms/nextcloud-mcp/protocol/openid-connect/token" \
# -d "grant_type=password" \
# -d "client_id=nextcloud-mcp-server" \
# -d "client_secret=mcp-secret-change-in-production" \
# -d "username=admin" \
# -d "password=admin" \
# -d "scope=openid profile email offline_access"
#
# - Use token with Nextcloud API:
# curl -H "Authorization: Bearer <access_token>" \
# http://localhost:8080/ocs/v2.php/cloud/capabilities
#
# 4. Connect MCP client to server:
# - Point your MCP client to http://localhost:8002
# - Complete OAuth flow via Keycloak (credentials: admin/admin)
# - Client should receive access token and be able to call MCP tools
# ==============================================================================
# TROUBLESHOOTING
# ==============================================================================
# If OAuth flow fails:
# - Check that Keycloak is accessible: curl http://localhost:8888
# - Check that user_oidc provider is configured: docker compose exec app php occ user_oidc:provider keycloak
# - Check MCP server logs: docker-compose logs mcp-keycloak
# - Verify redirect URIs match in Keycloak client configuration
#
# If token validation fails:
# - Verify user_oidc has bearer validation enabled (--check-bearer=1)
# - Check Nextcloud logs: docker compose exec app tail -f /var/www/html/data/nextcloud.log
# - Verify Keycloak discovery URL is accessible from Nextcloud container:
# docker compose exec app curl http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration
#
# If offline_access/refresh tokens not working:
# - Verify TOKEN_ENCRYPTION_KEY is set and valid
# - Check token storage database: ls -lah /app/data/tokens.db (inside container)
# - Check that offline_access scope is requested in realm configuration
+1 -1
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@@ -25,7 +25,7 @@ jobs:
github_token: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
changelog_increment_filename: body.md
- name: Release
uses: softprops/action-gh-release@5be0e66d93ac7ed76da52eca8bb058f665c3a5fe # v2.4.2
uses: softprops/action-gh-release@6da8fa9354ddfdc4aeace5fc48d7f679b5214090 # v2.4.1
with:
body_path: "body.md"
tag_name: v${{ env.REVISION }}
+1 -1
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@@ -16,7 +16,7 @@ jobs:
- name: Docker meta
id: meta
uses: docker/metadata-action@318604b99e75e41977312d83839a89be02ca4893 # v5
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5
with:
# list of Docker images to use as base name for tags
images: |
+2 -107
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@@ -14,121 +14,16 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Configure Git
run: |
git config user.name "$GITHUB_ACTOR"
git config user.email "$GITHUB_ACTOR@users.noreply.github.com"
- name: Install Helm
uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # v4.3.1
with:
version: v3.16.0
- name: Add Helm repositories and update dependencies
run: |
helm repo add qdrant https://qdrant.github.io/qdrant-helm
helm repo add ollama https://otwld.github.io/ollama-helm
helm repo update
helm dependency build charts/nextcloud-mcp-server
- name: Run chart-releaser
uses: helm/chart-releaser-action@cae68fefc6b5f367a0275617c9f83181ba54714f # v1.7.0
uses: helm/chart-releaser-action@v1.7.0
env:
CR_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
- name: Update gh-pages with Chart README and Index
run: |
# Get the repository name
REPO_NAME="${GITHUB_REPOSITORY##*/}"
REPO_OWNER="${GITHUB_REPOSITORY%/*}"
# Switch to gh-pages branch
git fetch origin gh-pages
git checkout gh-pages
# Copy Chart README to root
git checkout ${GITHUB_REF#refs/tags/} -- charts/nextcloud-mcp-server/README.md
mv charts/nextcloud-mcp-server/README.md README.md || true
rm -rf charts 2>/dev/null || true
# Create index.html with installation instructions
cat > index.html <<'EOF'
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Nextcloud MCP Server Helm Chart</title>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
max-width: 800px;
margin: 50px auto;
padding: 20px;
line-height: 1.6;
}
code {
background: #f4f4f4;
padding: 2px 6px;
border-radius: 3px;
font-family: "Monaco", "Courier New", monospace;
}
pre {
background: #f4f4f4;
padding: 15px;
border-radius: 5px;
overflow-x: auto;
}
h1, h2 { color: #0082c9; }
a { color: #0082c9; text-decoration: none; }
a:hover { text-decoration: underline; }
</style>
</head>
<body>
<h1>Nextcloud MCP Server Helm Chart</h1>
<p>A Helm chart for deploying the Nextcloud MCP (Model Context Protocol) Server on Kubernetes, enabling AI assistants to interact with your Nextcloud instance.</p>
<h2>Installation</h2>
<p>Add the Helm repository:</p>
<pre><code>helm repo add nextcloud-mcp https://REPO_OWNER.github.io/REPO_NAME/
helm repo update</code></pre>
<p>Install the chart:</p>
<pre><code>helm install nextcloud-mcp nextcloud-mcp/nextcloud-mcp-server \
--set nextcloud.host=https://cloud.example.com \
--set auth.basic.username=myuser \
--set auth.basic.password=mypassword</code></pre>
<h2>Documentation</h2>
<ul>
<li><a href="README.md">Chart README</a> - Full documentation for the Helm chart</li>
<li><a href="https://github.com/REPO_OWNER/REPO_NAME">GitHub Repository</a> - Source code and issues</li>
<li><a href="index.yaml">Helm Repository Index</a> - Chart metadata</li>
</ul>
<h2>Quick Start</h2>
<p>See the <a href="README.md">full documentation</a> for detailed configuration options, examples, and troubleshooting guides.</p>
<hr>
<p><small>Generated by <a href="https://github.com/helm/chart-releaser">chart-releaser</a></small></p>
</body>
</html>
EOF
# Replace placeholders
sed -i "s/REPO_OWNER/$REPO_OWNER/g" index.html
sed -i "s/REPO_NAME/$REPO_NAME/g" index.html
# Commit changes
git add README.md index.html
git commit -m "Update README and index from chart release" || echo "No changes to commit"
git push origin gh-pages
+1 -1
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@@ -20,7 +20,7 @@ jobs:
- name: Checkout
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5
- name: Install uv
uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
uses: astral-sh/setup-uv@85856786d1ce8acfbcc2f13a5f3fbd6b938f9f41 # v7.1.2
- name: Install Python 3.11
run: uv python install 3.11
- name: Build
+3 -7
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@@ -11,16 +11,13 @@ jobs:
steps:
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install the latest version of uv
uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
uses: astral-sh/setup-uv@85856786d1ce8acfbcc2f13a5f3fbd6b938f9f41 # v7.1.2
- name: Check format
run: |
uv run --frozen ruff format --diff
- name: Linting
run: |
uv run --frozen ruff check
- name: Linting
run: |
uv run --frozen ty check -- nextcloud_mcp_server
integration-test:
@@ -52,11 +49,10 @@ jobs:
uses: hoverkraft-tech/compose-action@3846bcd61da338e9eaaf83e7ed0234a12b099b72 # v2.4.1
with:
compose-file: "./docker-compose.yml"
#compose-flags: "--profile qdrant"
up-flags: "--build"
- name: Install the latest version of uv
uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
uses: astral-sh/setup-uv@85856786d1ce8acfbcc2f13a5f3fbd6b938f9f41 # v7.1.2
- name: Install Playwright dependencies
run: |
@@ -85,4 +81,4 @@ jobs:
NEXTCLOUD_USERNAME: "admin"
NEXTCLOUD_PASSWORD: "admin"
run: |
uv run pytest -v --log-cli-level=WARN -m smoke
uv run pytest -v --log-cli-level=INFO
-6
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@@ -5,11 +5,5 @@ __pycache__/
.env.local
.env.*.local
# Git
worktrees/
docker-compose.override.yml
# Generated by pytest used to login users
.nextcloud_oauth_*.json
.playwright-mcp/
-3
View File
@@ -4,6 +4,3 @@
[submodule "third_party/oidc"]
path = third_party/oidc
url = https://github.com/cbcoutinho/oidc
[submodule "third_party/notes"]
path = third_party/notes
url = https://github.com/cbcoutinho/notes
-6
View File
@@ -18,9 +18,3 @@ repos:
entry: uv run ruff format
language: system
types: [python]
- id: ty-check
name: ty-check
language: system
types: [python]
exclude: tests/.*
entry: uv run ty check
-346
View File
@@ -1,349 +1,3 @@
## v0.36.0 (2025-11-15)
### BREAKING CHANGE
- Search algorithms now require Qdrant to be populated.
Vector sync must be enabled and documents indexed for search to work.
### Feat
- Normalize hybrid search RRF scores to 0-1 range
- Enhance vector visualization UI and parallelize search verification
- Add Vector Viz tab to app home page
- Add vector visualization pane with multi-select document types
- Implement custom PCA to remove sklearn dependency
- Add multi-document Protocol with cross-app search support
- Update nc_semantic_search tool with algorithm selection
- Implement unified search algorithm module
### Fix
- Reorder tabs and fix viz pane session access
### Refactor
- Optimize Nextcloud access verification with centralized filtering
- Make all search algorithms query Qdrant payload, not Nextcloud
### Perf
- Exclude vector-sync status polling from distributed tracing
## v0.35.0 (2025-11-15)
### Feat
- Enable SSE transport for mcp service and update test fixtures
## v0.34.2 (2025-11-13)
### Fix
- Use NEXTCLOUD_OIDC_CLIENT_ID/SECRET env vars consistently
## v0.34.1 (2025-11-13)
### Fix
- return all notes when search query is empty
## v0.34.0 (2025-11-13)
### Feat
- Complete Phase 5 - Instrument all 93 MCP tools
- Add instrumentation decorator and apply to notes tools (Phase 5)
- Add OAuth token and database metrics (Phases 3-4)
- Add metrics instrumentation for queue, health, and database operations
## v0.33.1 (2025-11-13)
### Fix
- Move grafana_folder from labels to annotations
## v0.33.0 (2025-11-13)
### Feat
- Add Grafana dashboard and vector sync metric instrumentation
## v0.32.1 (2025-11-12)
### Fix
- add dynamic dimension detection for Ollama embedding models
## v0.32.0 (2025-11-11)
### Feat
- **ollama**: Pull model on startup if not available in ollama
- add dynamic vector sync status updates with htmx polling
- add webhook management UI and BeforeNodeDeletedEvent support
- validate Nextcloud webhook schemas and document findings
### Fix
- improve webapp tab UI with CSS Grid and viewport-filling container
### Refactor
- move webapp from /user/page to /app
- consolidate database storage for webhooks and OAuth tokens
## v0.31.1 (2025-11-10)
### Refactor
- simplify OpenTelemetry tracing configuration
## v0.31.0 (2025-11-10)
### Feat
- skip tracing for health and metrics endpoints
### Fix
- add retry logic for ETag conflicts in category change test
- optimize Notes API pagination with pruneBefore parameter
## v0.30.0 (2025-11-10)
### Feat
- **helm**: Add document chunking configuration
- **vector**: Add configurable chunk size and overlap for document embedding
- **vector**: Support multiple embedding models with auto-generated collection names
### Fix
- Support in-memory Qdrant for CI testing
## v0.29.2 (2025-11-09)
### Fix
- **helm**: Set default strategy to Recreate
## v0.29.1 (2025-11-09)
### Fix
- **observability**: isolate metrics endpoint to dedicated port
## v0.29.0 (2025-11-09)
### Feat
- **helm**: Add observability support with ServiceMonitor and Grafana dashboard
### Fix
- **readiness**: Only check external Qdrant in network mode
## v0.28.0 (2025-11-09)
### Feat
- **observability**: Add comprehensive monitoring with Prometheus and OpenTelemetry
### Fix
- **vector**: Handle missing 'modified' field in notes gracefully
## v0.27.3 (2025-11-09)
### Fix
- **ci**: Use helm dependency build instead of update to use Chart.lock
## v0.27.2 (2025-11-09)
### Fix
- **helm**: update Qdrant dependency condition to match new mode structure
## v0.27.1 (2025-11-09)
### Fix
- **ci**: add Helm repository setup to chart release workflow
## v0.27.0 (2025-11-09)
### Feat
- **helm**: add Qdrant local mode support with three deployment options [skip ci]
- add Qdrant local mode support with in-memory and persistent storage
- implement ADR-009 - refactor semantic search to use generic semantic:read scope
- implement MCP sampling for semantic search RAG (ADR-008)
- add optional vector database and semantic search to helm chart
- add vector sync processing status to /app endpoint
- implement semantic search tool and fix vector sync issues (ADR-007 Phase 3)
- implement vector sync scanner and processor (ADR-007 Phase 2)
### Fix
- implement deletion grace period and vector sync status tool
- remove unnecessary urllib3<2.0 constraint
- integrate vector sync tasks with Starlette lifespan for streamable-http
### Refactor
- migrate vector sync from asyncio.Queue to anyio memory object streams
- update to Qdrant query_points API and fix Playwright Keycloak login
## v0.26.1 (2025-11-08)
### Fix
- **deps**: update dependency mcp to >=1.21,<1.22
## v0.26.0 (2025-11-08)
### Feat
- add real elicitation integration test with python-sdk MCP client
- unify session architecture and enhance login status visibility
### Fix
- Consolidate OAuth callbacks and implement PKCE for all flows
## v0.25.0 (2025-11-05)
### BREAKING CHANGE
- All OAuth deployments must be reconfigured to specify
resource URIs (NEXTCLOUD_MCP_SERVER_URL and NEXTCLOUD_RESOURCE_URI) and
choose between multi-audience or token exchange mode.
### Feat
- Implement ADR-005 unified token verifier to eliminate token passthrough vulnerability
### Fix
- Implement proper OAuth resource parameters and PRM-based discovery
- Simplify token verifier to be RFC 7519 compliant
- Use Keycloak client ID for NEXTCLOUD_RESOURCE_URI in token exchange
- Correct OAuth token audience validation for multi-audience mode
### Refactor
- Eliminate duplicate validation logic in UnifiedTokenVerifier
## v0.24.1 (2025-11-04)
### Fix
- **deps**: update dependency mcp to >=1.20,<1.21
## v0.24.0 (2025-11-04)
### Feat
- add scope protection to OAuth provisioning tools
- enable authorization services for token exchange in Keycloak
- implement scope-based audience mapping and RFC 9728 support
- integrate token exchange into MCP server application
- implement RFC 8693 Standard Token Exchange for Keycloak
- Add userinfo route/page
- add browser-based user info page with separate OAuth flow
- Implement ADR-004 Progressive Consent foundation (partial)
- Complete ADR-004 Progressive Consent OAuth flows implementation
- Implement ADR-004 Progressive Consent foundation components
- Implement ADR-004 Hybrid Flow with comprehensive integration tests
### Fix
- add missing await for get_nextcloud_client in capabilities resource
- use valid Fernet encryption keys in token exchange tests
- accept resource URL in token audience for Nextcloud JWT tokens
- remove token-exchange-nextcloud scope and accept tokens without audience
- move audience mapper from scope to nextcloud-mcp-server client
- move token-exchange-nextcloud from default to optional scopes
- restructure routes to prevent SessionAuthBackend from interfering with FastMCP OAuth
- allow OAuth Bearer tokens on /mcp endpoint by excluding from session auth
- correct OAuth token audience validation using RFC 8707 resource parameter
- remove remaining references to deleted oauth_callback and oauth_token
- remove Hybrid Flow, make Progressive Consent default (ADR-004)
- browser OAuth userinfo endpoint and refresh token rotation
- make ENABLE_PROGRESSIVE_CONSENT consistently opt-in (default false)
- make provisioning checks opt-in (default false)
- Disable Progressive Consent for mcp-oauth to enable Hybrid Flow tests
### Refactor
- integrate token exchange into unified get_client() pattern
## v0.23.0 (2025-11-03)
### Feat
- Auto-configure impersonation role in Keycloak realm import
- Implement dual-tier token exchange (Standard V2 + Legacy V1 impersonation)
- Add Keycloak external IdP integration with custom scopes
- Implement RFC 8693 token exchange for Keycloak (ADR-002 Tier 2)
- Add Keycloak OAuth provider support with refresh token storage
### Fix
- Complete Keycloak external IdP integration with all tests passing
- Complete Keycloak external IdP integration with all tests passing
- Update DCR token_type tests for OIDC app changes
### Refactor
- Remove NEXTCLOUD_OIDC_CLIENT_STORAGE environment variable
- Remove unnecessary user_oidc patch - CORSMiddleware patch is sufficient
- Unify OAuth configuration to be provider-agnostic
## v0.22.7 (2025-10-29)
### Fix
- **helm**: Remove image tag overide
## v0.22.6 (2025-10-29)
### Fix
- **helm**: Update helm chart with extraArgs
## v0.22.5 (2025-10-29)
### Fix
- Update helm chart variables
## v0.22.4 (2025-10-29)
### Fix
- **helm**: Update helm version with release
- **helm**: Update helm version with release
## v0.22.3 (2025-10-29)
### Fix
- **helm**: Update helm version with release
## v0.22.2 (2025-10-29)
### Fix
- **helm**: Update helm version with release
## v0.22.1 (2025-10-29)
### Fix
- Trigger release
## v0.22.0 (2025-10-29)
### Feat
+312 -295
View File
@@ -2,396 +2,413 @@
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Coding Conventions
### async/await Patterns
- **Use anyio + asyncio hybrid** - Both libraries are available
- pytest runs in `anyio` mode (`anyio_mode = "auto"` in pyproject.toml)
- asyncio used in auth modules (refresh_token_storage.py, token_exchange.py, token_broker.py)
- anyio used in calendar.py, client_registration.py, app.py
- Prefer standard async/await syntax without explicit library imports when possible
### Type Hints
- **Use Python 3.10+ union syntax**: `str | None` instead of `Optional[str]`
- **Use lowercase generics**: `dict[str, Any]` instead of `Dict[str, Any]`
- **Type all function signatures** - Parameters and return types
- **No explicit type checker configured** - Ruff handles linting only
### Code Quality
- **Run ruff before committing**:
```bash
uv run ruff check
uv run ruff format
```
- **Ruff configuration** in pyproject.toml (extends select: ["I"] for import sorting)
### Error Handling
- **Use custom decorators**: `@retry_on_429` for rate limiting (see base_client.py)
- **Standard exceptions**: `HTTPStatusError` from httpx, `McpError` for MCP-specific errors
- **Logging patterns**:
- `logger.debug()` for expected 404s and normal operations
- `logger.warning()` for retries and non-critical issues
- `logger.error()` for actual errors
### Testing Patterns
- **Use existing fixtures** from `tests/conftest.py` (2888 lines of test infrastructure)
- **Session-scoped fixtures** handle anyio/pytest-asyncio incompatibility
- **Mocked unit tests** use `mocker.AsyncMock(spec=httpx.AsyncClient)`
- **pytest-timeout**: 180s default per test
- **Mark tests appropriately**: `@pytest.mark.unit`, `@pytest.mark.integration`, `@pytest.mark.oauth`, `@pytest.mark.smoke`
### Architectural Patterns
- **Base classes**: `BaseNextcloudClient` for all API clients
- **Pydantic responses**: All MCP tools return Pydantic models inheriting from `BaseResponse`
- **Decorators**: `@require_scopes`, `@require_provisioning` for access control
- **Context pattern**: `await get_client(ctx)` to access authenticated NextcloudClient (async!)
- **FastMCP decorators**: `@mcp.tool()`, `@mcp.resource()`
- **Token acquisition**: `get_client()` handles both pass-through and token exchange modes
- Pass-through (default): Simple, stateless (ENABLE_TOKEN_EXCHANGE=false)
- Token exchange (opt-in): RFC 8693 delegation (ENABLE_TOKEN_EXCHANGE=true)
### Project Structure
- `nextcloud_mcp_server/client/` - HTTP clients for Nextcloud APIs
- `nextcloud_mcp_server/server/` - MCP tool/resource definitions
- `nextcloud_mcp_server/auth/` - OAuth/OIDC authentication
- `nextcloud_mcp_server/models/` - Pydantic response models
- `tests/` - Layered test suite (unit, smoke, integration, load)
## Development Commands (Quick Reference)
## Development Commands
### Testing
The test suite is organized in layers for fast feedback:
```bash
# Fast feedback (recommended)
uv run pytest tests/unit/ -v # Unit tests (~5s)
uv run pytest -m smoke -v # Smoke tests (~30-60s)
# FAST FEEDBACK (recommended for development)
# Unit tests only - ~5 seconds
uv run pytest tests/unit/ -v
# Integration tests
uv run pytest -m "integration and not oauth" -v # Without OAuth (~2-3min)
uv run pytest -m oauth -v # OAuth only (~3min)
uv run pytest # Full suite (~4-5min)
# Smoke tests - critical path validation - ~30-60 seconds
uv run pytest -m smoke -v
# Coverage
# INTEGRATION TESTS
# Integration tests without OAuth - ~2-3 minutes
uv run pytest -m "integration and not oauth" -v
# Full test suite - ~4-5 minutes
uv run pytest
# OAuth tests only (slowest, requires Playwright) - ~3 minutes
uv run pytest -m oauth -v
# COVERAGE
# Run tests with coverage
uv run pytest --cov
# Specific tests after changes
uv run pytest tests/server/test_mcp.py -k "notes" -v
uv run pytest tests/client/notes/test_notes_api.py -v
# LEGACY COMMANDS (still work)
# Run all integration tests
uv run pytest -m integration -v
# Skip integration tests
uv run pytest -m "not integration" -v
```
**Important**: After code changes, rebuild the correct container:
- Single-user tests: `docker-compose up --build -d mcp`
- OAuth tests: `docker-compose up --build -d mcp-oauth`
- Keycloak tests: `docker-compose up --build -d mcp-keycloak`
### Running the Server
```bash
# Local development
export $(grep -v '^#' .env | xargs)
mcp run --transport sse nextcloud_mcp_server.app:mcp
# Docker development (rebuilds after code changes)
docker-compose up --build -d mcp # Single-user (port 8000)
docker-compose up --build -d mcp-oauth # Nextcloud OAuth (port 8001)
docker-compose up --build -d mcp-keycloak # Keycloak OAuth (port 8002)
```
### Environment Setup
```bash
uv sync # Install dependencies
uv sync --group dev # Install with dev dependencies
```
! Hint: If the tests are failing due to missing environment variables, then usually the correct .env has not been created or not correctly configured yet.
### Load Testing
```bash
# Quick test (default: 10 workers, 30 seconds)
# Run benchmark with default settings (10 workers, 30 seconds)
uv run python -m tests.load.benchmark
# Custom concurrency and duration
uv run python -m tests.load.benchmark -c 20 -d 60
# Quick test with custom concurrency and duration
uv run python -m tests.load.benchmark --concurrency 20 --duration 60
# Export results for analysis
uv run python -m tests.load.benchmark --output results.json --verbose
# Extended load test (50 workers for 5 minutes)
uv run python -m tests.load.benchmark -c 50 -d 300
# Export results to JSON for analysis
uv run python -m tests.load.benchmark -c 20 -d 60 --output results.json
# Test OAuth server on port 8001
uv run python -m tests.load.benchmark --url http://127.0.0.1:8001/mcp
# Verbose mode with detailed logging
uv run python -m tests.load.benchmark -c 10 -d 30 --verbose
```
**Expected Performance**: 50-200 RPS for mixed workload, p50 <100ms, p95 <500ms, p99 <1000ms.
**Load Testing Features:**
- **Mixed workload** simulating realistic MCP usage (40% reads, 20% writes, 15% search, 25% other operations)
- **Real-time progress** bar with live RPS and error counts
- **Detailed metrics**:
- Throughput (requests/second)
- Latency percentiles (p50, p90, p95, p99)
- Per-operation breakdown
- Error rates and types
- **Automatic cleanup** of test data
- **JSON export** for CI/CD integration
- **Server health checks** before starting
## Database Inspection
**Understanding Results:**
- **Requests/Second (RPS)**: Higher is better. Expected baseline: 50-200 RPS for mixed workload
- **Latency**:
- p50 (median): Should be <100ms for most operations
- p95: Should be <500ms
- p99: Should be <1000ms
- **Error Rate**: Should be <1% under normal load
**Credentials**: root/password, nextcloud/password, database: `nextcloud`
**Common Bottlenecks:**
1. Nextcloud backend API response times (most common)
2. Database connection limits
3. HTTP client connection pooling
4. Network I/O between containers
### Code Quality
```bash
# Connect to database
# Format and lint code
uv run ruff check
uv run ruff format
# Type checking
# No explicit type checker configured - this is a Python project using ruff for linting
```
### Running the Server
```bash
# Local development - load environment variables and run
export $(grep -v '^#' .env | xargs)
mcp run --transport sse nextcloud_mcp_server.app:mcp
# Docker development environment with Nextcloud instance
docker-compose up
# After code changes, rebuild and restart the appropriate MCP server container:
# For basic auth changes (most common) - uses admin credentials
docker-compose up --build -d mcp
# For OAuth changes - uses OAuth authentication with JWT tokens
docker-compose up --build -d mcp-oauth
# Build Docker image
docker build -t nextcloud-mcp-server .
```
**Important: MCP Server Containers**
- **`mcp`** (port 8000): Uses basic auth with admin credentials. Use this for most development and testing.
- **`mcp-oauth`** (port 8001): Uses OAuth authentication with JWT tokens. Use this when working on OAuth-specific features or tests.
- JWT tokens are used for testing (faster validation, scopes embedded in token)
- The server can handle both JWT and opaque tokens via the token verifier
### Environment Setup
```bash
# Install dependencies
uv sync
# Install development dependencies
uv sync --group dev
```
### Database Inspection
**Docker Compose Database Credentials:**
- Root user: `root` / password: `password`
- App user: `nextcloud` / password: `password`
- Database: `nextcloud`
**Common Database Commands:**
```bash
# Connect to database as root (most common for inspection)
docker compose exec db mariadb -u root -ppassword nextcloud
# Check OAuth clients
docker compose exec db mariadb -u root -ppassword nextcloud -e \
"SELECT id, name, token_type FROM oc_oidc_clients ORDER BY id DESC LIMIT 10;"
docker compose exec db mariadb -u root -ppassword nextcloud -e "SELECT id, name, token_type FROM oc_oidc_clients ORDER BY id DESC LIMIT 10;"
# Check OAuth client scopes
docker compose exec db mariadb -u root -ppassword nextcloud -e \
"SELECT c.id, c.name, s.scope FROM oc_oidc_clients c LEFT JOIN oc_oidc_client_scopes s ON c.id = s.client_id WHERE c.name LIKE '%MCP%';"
docker compose exec db mariadb -u root -ppassword nextcloud -e "SELECT c.id, c.name, s.scope FROM oc_oidc_clients c LEFT JOIN oc_oidc_client_scopes s ON c.id = s.client_id WHERE c.name LIKE '%MCP%';"
# Check OAuth access tokens
docker compose exec db mariadb -u root -ppassword nextcloud -e \
"SELECT id, client_id, user_id, created_at FROM oc_oidc_access_tokens ORDER BY created_at DESC LIMIT 10;"
docker compose exec db mariadb -u root -ppassword nextcloud -e "SELECT id, client_id, user_id, created_at FROM oc_oidc_access_tokens ORDER BY created_at DESC LIMIT 10;"
```
**Important Tables**:
- `oc_oidc_clients` - OAuth client registrations (DCR)
**Important Tables:**
- `oc_oidc_clients` - OAuth client registrations (DCR clients)
- `oc_oidc_client_scopes` - Client allowed scopes
- `oc_oidc_access_tokens` - Issued access tokens
- `oc_oidc_authorization_codes` - Authorization codes
- `oc_oidc_registration_tokens` - RFC 7592 registration tokens
- `oc_oidc_redirect_uris` - Redirect URIs
- `oc_oidc_registration_tokens` - RFC 7592 registration tokens for client management
- `oc_oidc_redirect_uris` - Redirect URIs for each client
## Architecture Quick Reference
## Architecture Overview
**For detailed architecture, see:**
- `docs/comparison-context-agent.md` - Overall architecture
- `docs/oauth-architecture.md` - OAuth integration patterns
- `docs/ADR-004-progressive-consent.md` - Progressive consent implementation
This is a Python MCP (Model Context Protocol) server that provides LLM integration with Nextcloud. The architecture follows a layered pattern:
**Core Components**:
- `nextcloud_mcp_server/app.py` - FastMCP server entry point
- `nextcloud_mcp_server/client/` - HTTP clients (Notes, Calendar, Contacts, Tables, WebDAV)
- `nextcloud_mcp_server/server/` - MCP tool/resource definitions
- `nextcloud_mcp_server/auth/` - OAuth/OIDC authentication
### Core Components
**Supported Apps**: Notes, Calendar (CalDAV + VTODO tasks), Contacts (CardDAV), Tables, WebDAV, Deck, Cookbook
- **`nextcloud_mcp_server/app.py`** - Main MCP server entry point using FastMCP framework
- **`nextcloud_mcp_server/client/`** - HTTP client implementations for different Nextcloud APIs
- **`nextcloud_mcp_server/server/`** - MCP tool/resource definitions that expose client functionality
- **`nextcloud_mcp_server/controllers/`** - Business logic controllers (e.g., notes search)
**Key Patterns**:
1. `NextcloudClient` orchestrates all app-specific clients
2. `BaseNextcloudClient` provides common HTTP functionality + retry logic
3. MCP tools use context pattern: `get_client(ctx)` → `NextcloudClient`
4. All operations are async using httpx
### Client Architecture
### Progressive Consent Architecture (ADR-004)
- **`NextcloudClient`** - Main orchestrating client that manages all app-specific clients
- **`BaseNextcloudClient`** - Abstract base class providing common HTTP functionality and retry logic
- **App-specific clients**: `NotesClient`, `CalendarClient`, `ContactsClient`, `TablesClient`, `WebDAVClient`
**Important**: Progressive consent is a *mechanism* for granting access, not a feature flag. The architecture is always present in OAuth mode. Whether provisioning tools are available is controlled by `ENABLE_OFFLINE_ACCESS`.
### Server Integration
**What is Progressive Consent?**
- Dual OAuth flow architecture that separates client authentication (Flow 1) from resource provisioning (Flow 2)
- Flow 1: MCP client authenticates directly to IdP with resource scopes (notes:*, calendar:*, etc.)
- Token audience: "mcp-server"
- Client receives resource-scoped token for MCP session
- Flow 2: Server explicitly provisions Nextcloud access via separate login (only when `ENABLE_OFFLINE_ACCESS=true`)
- Server requests: openid, profile, email, offline_access
- Token audience: "nextcloud"
- Server receives refresh token for offline access
- Client never sees this token
- Provides clear separation between session tokens and offline access tokens
Each Nextcloud app has a corresponding server module that:
1. Defines MCP tools using `@mcp.tool()` decorators
2. Defines MCP resources using `@mcp.resource()` decorators
3. Uses the context pattern to access the `NextcloudClient` instance
**Modes:**
- **Pass-through mode** (`ENABLE_OFFLINE_ACCESS=false`, default):
- No Flow 2 provisioning
- Server uses client's token to access Nextcloud (pass-through)
- No provisioning tools available
- Suitable for stateless, client-driven operations
- **Offline access mode** (`ENABLE_OFFLINE_ACCESS=true`):
- Flow 2 provisioning available
- Server stores refresh tokens for background operations
- Provisioning tools available: `provision_nextcloud_access`, `check_logged_in`
- Suitable for background jobs and server-initiated operations
### Supported Nextcloud Apps
**When to use OAuth mode:**
- Multi-user deployments
- Background jobs requiring offline access (with `ENABLE_OFFLINE_ACCESS=true`)
- Enhanced security with separate authorization contexts
- Explicit user control over resource access
- **Notes** - Full CRUD operations and search
- **Calendar** - CalDAV integration with events, recurring events, attendees, and **tasks (VTODO)**
- **Calendar Operations**: List, create, delete calendars
- **Event Operations**: Full CRUD, recurring events, attendees, reminders, bulk operations
- **Task Operations (VTODO)**: Full CRUD for CalDAV tasks with:
- Status tracking (NEEDS-ACTION, IN-PROCESS, COMPLETED, CANCELLED)
- Priority levels (0-9, 1=highest, 9=lowest)
- Due dates, start dates, completion tracking
- Percent complete (0-100%)
- Categories and filtering
- Search across all calendars
- **Note**: Calendar implementation uses caldav library's AsyncDavClient
- **Contacts** - CardDAV integration with address book operations
- **Tables** - Row-level operations on Nextcloud Tables
- **WebDAV** - Complete file system access
**When to use BasicAuth instead:**
- Simple single-user deployments
- Local development and testing
### Key Patterns
**Key features:**
- No scope escalation - client gets exactly what it requests
- User explicitly authorizes via `provision_nextcloud_access` tool
- Clear security boundaries between MCP session and Nextcloud access
1. **Environment-based configuration** - Uses `NextcloudClient.from_env()` to load credentials from environment variables
2. **Async/await throughout** - All operations are async using httpx
3. **Retry logic** - `@retry_on_429` decorator handles rate limiting
4. **Context injection** - MCP context provides access to the authenticated client instance
5. **Modular design** - Each Nextcloud app is isolated in its own client/server pair
## MCP Response Patterns (CRITICAL)
### MCP Response Patterns
**Never return raw `List[Dict]` from MCP tools** - FastMCP mangles them into dicts with numeric string keys.
**CRITICAL: Never return raw `List[Dict]` from MCP tools - always wrap in Pydantic response models**
**Correct Pattern**:
FastMCP serialization issue: raw lists get mangled into dicts with numeric string keys.
**Pattern:**
1. Client methods return `List[Dict]` (raw data)
2. MCP tools convert to Pydantic models and wrap in response object
3. Response models inherit from `BaseResponse`, include `results` field + metadata
**Reference implementations**:
- `nextcloud_mcp_server/models/notes.py:80` - `SearchNotesResponse`
- `nextcloud_mcp_server/models/webdav.py:113` - `SearchFilesResponse`
- `nextcloud_mcp_server/server/{notes,webdav}.py` - Tool examples
**Reference implementations:**
- `SearchNotesResponse` in `nextcloud_mcp_server/models/notes.py:80`
- `SearchFilesResponse` in `nextcloud_mcp_server/models/webdav.py:113`
- Tool examples: `nextcloud_mcp_server/server/{notes,webdav}.py`
**Testing**: Extract `data["results"]` from MCP responses, not `data` directly.
**Testing:** Extract `data["results"]` from MCP responses, not `data` directly.
## MCP Sampling for RAG (ADR-008)
### Testing Structure
**What is MCP Sampling?**
MCP sampling allows servers to request LLM completions from their clients. This enables Retrieval-Augmented Generation (RAG) patterns where the server retrieves context and the client's LLM generates answers.
The test suite follows a layered architecture for fast feedback:
**When to use sampling:**
- Generating natural language answers from retrieved documents
- Synthesizing information from multiple sources
- Creating summaries with citations
**Implementation Pattern** (see ADR-008 for details):
```python
from mcp.types import ModelHint, ModelPreferences, SamplingMessage, TextContent
@mcp.tool()
@require_scopes("notes:read")
async def nc_notes_semantic_search_answer(
query: str, ctx: Context, limit: int = 5, max_answer_tokens: int = 500
) -> SamplingSearchResponse:
# 1. Retrieve documents
search_response = await nc_notes_semantic_search(query, ctx, limit)
# 2. Check for no results (don't waste sampling call)
if not search_response.results:
return SamplingSearchResponse(
query=query,
generated_answer="No relevant documents found.",
sources=[], total_found=0, success=True
)
# 3. Construct prompt with retrieved context
prompt = f"{query}\n\nDocuments:\n{format_sources(search_response.results)}\n\nProvide answer with citations."
# 4. Request LLM completion via sampling
try:
result = await ctx.session.create_message(
messages=[SamplingMessage(role="user", content=TextContent(type="text", text=prompt))],
max_tokens=max_answer_tokens,
temperature=0.7,
model_preferences=ModelPreferences(
hints=[ModelHint(name="claude-3-5-sonnet")],
intelligencePriority=0.8,
speedPriority=0.5,
),
include_context="thisServer",
)
return SamplingSearchResponse(
query=query,
generated_answer=result.content.text,
sources=search_response.results,
model_used=result.model,
stop_reason=result.stopReason,
success=True
)
except Exception as e:
# Fallback: Return documents without generated answer
return SamplingSearchResponse(
query=query,
generated_answer=f"[Sampling unavailable: {e}]\n\nFound {len(search_response.results)} documents.",
sources=search_response.results,
search_method="semantic_sampling_fallback",
success=True
)
```
tests/
├── unit/ # Fast unit tests (~5s total)
│ ├── test_scope_decorator.py
│ └── test_response_models.py
├── smoke/ # Critical path tests (~30-60s)
│ └── test_smoke.py
├── integration/
│ ├── client/ # Direct API layer tests
│ │ ├── notes/
│ │ ├── calendar/
│ │ └── ...
│ └── server/ # MCP tool layer tests
│ ├── oauth/ # OAuth-specific tests (slow, ~3min)
│ │ ├── test_oauth_core.py
│ │ ├── test_scope_authorization.py
│ │ └── ...
│ ├── test_mcp.py
│ └── ...
└── load/ # Performance tests
```
**Key Points**:
- **No server-side LLM**: Server has no API keys, client controls which model is used
- **Graceful degradation**: Tool always returns useful results even if sampling fails
- **User control**: MCP clients SHOULD prompt users to approve sampling requests
- **No results optimization**: Skip sampling call when no documents found
- **Fixed prompts**: Prompts are not user-configurable to avoid injection risks
**Test Markers:**
- `@pytest.mark.unit` - Fast unit tests with mocked dependencies
- `@pytest.mark.integration` - Integration tests requiring Docker containers
- `@pytest.mark.oauth` - OAuth tests requiring Playwright (slowest)
- `@pytest.mark.smoke` - Critical path smoke tests
**Reference**: See `nc_notes_semantic_search_answer` in `nextcloud_mcp_server/server/notes.py:517` and ADR-008 for complete implementation.
**Fixtures** in `tests/conftest.py` - Shared test setup and utilities
- **Important**: Integration tests run against live Docker containers. After making code changes:
- For basic auth tests: rebuild with `docker-compose up --build -d mcp`
- For OAuth tests: rebuild with `docker-compose up --build -d mcp-oauth`
## Testing Best Practices (MANDATORY)
#### Testing Best Practices
- **MANDATORY: Always run tests after implementing features or fixing bugs**
- Run tests to completion before considering any task complete
- If tests require modifications to pass, ask for permission before proceeding
- **Rebuild the correct container** after code changes:
- For basic auth tests (most common): `docker-compose up --build -d mcp`
- For OAuth tests: `docker-compose up --build -d mcp-oauth`
- **Use existing fixtures** from `tests/conftest.py` to avoid duplicate setup work:
- `nc_mcp_client` - MCP client session for tool/resource testing (uses `mcp` container)
- `nc_mcp_oauth_client` - MCP client session for OAuth testing (uses `mcp-oauth` container)
- `nc_client` - Direct NextcloudClient for setup/cleanup operations
- `temporary_note` - Creates and cleans up test notes automatically
- `temporary_addressbook` - Creates and cleans up test address books
- `temporary_contact` - Creates and cleans up test contacts
- **Test specific functionality** after changes:
- For Notes changes: `uv run pytest tests/server/test_mcp.py -k "notes" -v`
- For specific API changes: `uv run pytest tests/client/notes/test_notes_api.py -v`
- For OAuth changes: `uv run pytest tests/server/test_oauth*.py -v` (remember to rebuild `mcp-oauth` container)
- **Avoid creating standalone test scripts** - use pytest with proper fixtures instead
### Always Run Tests
- **Run tests to completion** before considering any task complete
- **Rebuild the correct container** after code changes (see Development Commands above)
- **If tests require modifications**, ask for permission before proceeding
#### Writing Mocked Unit Tests
### Use Existing Fixtures
See `tests/conftest.py` for 2888 lines of test infrastructure:
- `nc_mcp_client` - MCP client for tool/resource testing (uses `mcp` container)
- `nc_mcp_oauth_client` - MCP client for OAuth testing (uses `mcp-oauth` container)
- `nc_client` - Direct NextcloudClient for setup/cleanup
- `temporary_note`, `temporary_addressbook`, `temporary_contact` - Auto-cleanup
### Writing Mocked Unit Tests
For client-layer response parsing tests, use mocked HTTP responses:
For client-layer tests that verify response parsing logic, use mocked HTTP responses instead of real network calls:
**Pattern:**
```python
import httpx
import pytest
from nextcloud_mcp_server.client.notes import NotesClient
from tests.conftest import create_mock_note_response
async def test_notes_api_get_note(mocker):
"""Test that get_note correctly parses the API response."""
# Create mock response using helper functions
mock_response = create_mock_note_response(
note_id=123, title="Test Note", content="Test content",
category="Test", etag="abc123"
note_id=123,
title="Test Note",
content="Test content",
category="Test",
etag="abc123",
)
# Mock the _make_request method
mock_client = mocker.AsyncMock(spec=httpx.AsyncClient)
mock_make_request = mocker.patch.object(
NotesClient, "_make_request", return_value=mock_response
)
client = NotesClient(mocker.AsyncMock(spec=httpx.AsyncClient), "testuser")
# Create client and test
client = NotesClient(mock_client, "testuser")
note = await client.get_note(note_id=123)
# Verify the response was parsed correctly
assert note["id"] == 123
assert note["title"] == "Test Note"
# Verify the correct API endpoint was called
mock_make_request.assert_called_once_with("GET", "/apps/notes/api/v1/notes/123")
```
**Mock helpers in `tests/conftest.py`**: `create_mock_response()`, `create_mock_note_response()`, `create_mock_error_response()`
**Mock Response Helpers in `tests/conftest.py`:**
- `create_mock_response()` - Generic HTTP response builder
- `create_mock_note_response()` - Pre-configured note response
- `create_mock_error_response()` - Error responses (404, 412, etc.)
**When to use**: Response parsing, error handling, request parameter building
**When NOT to use**: CalDAV/CardDAV/WebDAV protocols, OAuth flows, end-to-end MCP testing
**Benefits:**
- ⚡ Fast execution (~0.1s vs minutes for integration tests)
- 🔒 No Docker dependency
- 🎯 Tests focus on response parsing logic
- ♻️ Repeatable and deterministic
### OAuth Testing
OAuth tests use **Playwright browser automation** to complete flows programmatically.
**When to use:**
- Testing client methods that parse JSON responses
- Testing error handling (404, 412, etc.)
- Testing request parameter building
**Test Environment**:
- Three MCP containers: `mcp` (single-user), `mcp-oauth` (Nextcloud OIDC), `mcp-keycloak` (external IdP)
- OAuth tests require `NEXTCLOUD_HOST`, `NEXTCLOUD_USERNAME`, `NEXTCLOUD_PASSWORD` environment variables
- Playwright configuration: `--browser firefox --headed` for debugging
- Install browsers: `uv run playwright install firefox`
**When NOT to use (keep as integration tests):**
- Complex protocol interactions (CalDAV, CardDAV, WebDAV)
- Multi-component workflows (Notes + WebDAV attachments)
- OAuth flows
- End-to-end MCP tool testing
**OAuth fixtures**: `nc_oauth_client`, `nc_mcp_oauth_client`, `alice_oauth_token`, `bob_oauth_token`, etc.
**Reference Implementation:**
- See `tests/client/notes/test_notes_api.py` for complete examples
- Mark unit tests with `pytestmark = pytest.mark.unit`
- Run with: `uv run pytest tests/unit/ tests/client/notes/test_notes_api.py -v`
**Shared OAuth Client**: All test users authenticate using a single OAuth client (created via DCR, deleted at session end via RFC 7592). Matches production behavior.
#### OAuth/OIDC Testing
OAuth integration tests use **automated Playwright browser automation** to complete the OAuth flow programmatically.
**Run OAuth tests**:
**OAuth Testing Setup:**
- **Main fixtures**: `nc_oauth_client`, `nc_mcp_oauth_client` - Use Playwright automation
- **Shared OAuth Client**: All test users authenticate using a single OAuth client
- **Created fresh for each test session** via Dynamic Client Registration (DCR)
- Matches production MCP server behavior (one client, multiple user tokens)
- Each user gets their own unique access token
- **Automatic cleanup**: Client is registered at session start, deleted at session end (RFC 7592)
- Implementation: `shared_oauth_client_credentials` fixture in `tests/conftest.py`
- **Note**: Client deletion may fail due to Nextcloud middleware (logged as warning). This doesn't affect tests.
- **Available fixtures**: `playwright_oauth_token`, `nc_oauth_client`, `nc_mcp_oauth_client`
- **Multi-user fixtures**: `alice_oauth_token`, `bob_oauth_token`, `charlie_oauth_token`, `diana_oauth_token`
- **Requirements**: `NEXTCLOUD_HOST`, `NEXTCLOUD_USERNAME`, `NEXTCLOUD_PASSWORD` environment variables
- Uses `pytest-playwright-asyncio` for async Playwright fixtures
- **Playwright configuration**: Use pytest CLI args like `--browser firefox --headed` to customize
- **Install browsers**: `uv run playwright install firefox` (or `chromium`, `webkit`)
**Example Commands:**
```bash
uv run pytest -m oauth -v # All OAuth tests
# Run all OAuth tests with Playwright automation using Firefox
uv run pytest tests/server/oauth/ --browser firefox -v
# Run specific OAuth test file with visible browser for debugging
uv run pytest tests/server/oauth/test_oauth_core.py --browser firefox --headed -v
# Run with Chromium (default) - use -m oauth marker for all OAuth tests
uv run pytest -m oauth -v
```
### Keycloak OAuth Testing
**Validates ADR-002 architecture** for external identity providers and offline access patterns.
**Test Environment:**
- **Two MCP server containers are available:**
- `mcp` (port 8000): Uses basic auth with admin credentials - for most testing
- `mcp-oauth` (port 8001): Uses OAuth authentication - for OAuth-specific testing
- Start OAuth MCP server: `docker-compose up --build -d mcp-oauth`
- **Important**: When working on OAuth functionality, always rebuild `mcp-oauth` container, not `mcp`
**Architecture**: `MCP Client → Keycloak (OAuth) → MCP Server → Nextcloud user_oidc (validates token) → APIs`
**CI/CD Notes:**
- Playwright tests run in CI/CD environments
- Use Firefox browser in CI: `--browser firefox` (Chromium may have issues with localhost redirects)
**Setup**:
```bash
docker-compose up -d keycloak app mcp-keycloak
curl http://localhost:8888/realms/nextcloud-mcp/.well-known/openid-configuration
docker compose exec app php occ user_oidc:provider keycloak
```
### Configuration Files
**Credentials**: admin/admin (Keycloak realm: `nextcloud-mcp`)
- **`pyproject.toml`** - Python project configuration using uv for dependency management
- **`.env`** (from `env.sample`) - Environment variables for Nextcloud connection
- **`docker-compose.yml`** - Complete development environment with Nextcloud + database
**For detailed Keycloak setup, see**:
- `docs/oauth-setup.md` - OAuth configuration
- `docs/ADR-002-vector-sync-authentication.md` - Offline access architecture
- `docs/audience-validation-setup.md` - Token audience validation
- `docs/keycloak-multi-client-validation.md` - Realm-level validation
## Integration testing with docker
## Integration Testing with Docker
### Nextcloud
**Nextcloud**: `docker compose exec app php occ ...` for occ commands
**MariaDB**: `docker compose exec db mariadb -u [user] -p [password] [database]` for queries
- The `app` container is running nextcloud.
- Use `docker compose exec app php occ ...` to get a list of available commands
**For detailed setup, see**:
- `docs/installation.md` - Installation guide
- `docs/configuration.md` - Configuration options
- `docs/authentication.md` - Authentication modes
- `docs/running.md` - Running the server
### Mariadb
**For additional information regarding MCP during development, see**:
- `../../Software/modelcontextprotocol/` - MCP spec
- `../../Software/python-sdk/` - Python MCP SDK
- The `db` container is running mariadb
- Use `docker compose exec db mariadb -u [user] -p [password] [database]` to execute queries. Check the docker-compose file for credentials
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@@ -1,17 +1,14 @@
FROM ghcr.io/astral-sh/uv:0.9.9-python3.11-alpine@sha256:0faa7934fac1db7f5056f159c1224d144bab864fd2677a4066d25a686ae32edd
FROM ghcr.io/astral-sh/uv:0.9.5-python3.11-alpine@sha256:64ecec379ff82bea84b8a80c0b374f6392bcd54aa52f8c63c12f510f9c0b214d
# Install dependencies
# 1. git (required for caldav dependency from git)
# 2. sqlite for development with token db
RUN apk add --no-cache git sqlite
# Install git (required for caldav dependency from git)
RUN apk add --no-cache git
WORKDIR /app
COPY . .
RUN uv sync --locked --no-dev --no-editable
RUN uv sync --locked --no-dev
ENV PYTHONUNBUFFERED=1
ENV VIRTUAL_ENV=/app/.venv
ENTRYPOINT ["/app/.venv/bin/nextcloud-mcp-server", "--host", "0.0.0.0"]
+686 -145
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@@ -1,201 +1,742 @@
# Nextcloud MCP Server
# Nextcloud MCP Server Helm Chart
[![Docker Image](https://img.shields.io/badge/docker-ghcr.io/cbcoutinho/nextcloud--mcp--server-blue)](https://github.com/cbcoutinho/nextcloud-mcp-server/pkgs/container/nextcloud-mcp-server)
This Helm chart deploys the Nextcloud MCP (Model Context Protocol) Server on a Kubernetes cluster, enabling AI assistants to interact with your Nextcloud instance.
**A production-ready MCP server that connects AI assistants to your Nextcloud instance.**
## Prerequisites
Enable Large Language Models like Claude, GPT, and Gemini to interact with your Nextcloud data through a secure API. Create notes, manage calendars, organize contacts, work with files, and more - all through natural language conversations.
- Kubernetes 1.19+
- Helm 3.0+
- A running Nextcloud instance (accessible from the Kubernetes cluster)
- Nextcloud credentials (username/password for basic auth OR OAuth client for OAuth mode)
This is a **dedicated standalone MCP server** designed for external MCP clients like Claude Code and IDEs. It runs independently of Nextcloud (Docker, VM, Kubernetes, or local) and provides deep CRUD operations across Nextcloud apps.
## Installation
> [!NOTE]
> **Looking for AI features inside Nextcloud?** Nextcloud also provides [Context Agent](https://github.com/nextcloud/context_agent), which powers the Assistant app and runs as an ExApp inside Nextcloud. See [docs/comparison-context-agent.md](docs/comparison-context-agent.md) for a detailed comparison of use cases.
## Quick Start
Get up and running in 60 seconds using Docker:
### Quick Start with Basic Authentication
```bash
# 1. Create a minimal configuration
cat > .env << EOF
NEXTCLOUD_HOST=https://your.nextcloud.instance.com
NEXTCLOUD_USERNAME=your_username
NEXTCLOUD_PASSWORD=your_app_password
EOF
# Add the Helm repository
helm repo add nextcloud-mcp https://cbcoutinho.github.io/nextcloud-mcp-server
helm repo update
# 2. Start the server
docker run -p 127.0.0.1:8000:8000 --env-file .env --rm \
ghcr.io/cbcoutinho/nextcloud-mcp-server:latest
# 3. Test the connection
curl http://127.0.0.1:8000/health/ready
# Install with basic auth (recommended for most users)
helm install nextcloud-mcp nextcloud-mcp/nextcloud-mcp-server \
--set nextcloud.host=https://cloud.example.com \
--set auth.basic.username=myuser \
--set auth.basic.password=mypassword
```
**Next Steps:**
- Create an app password in Nextcloud: Settings → Security → Devices & sessions
- Connect your MCP client (Claude Desktop, IDEs, `mcp dev`, etc.)
- See [docs/installation.md](docs/installation.md) for other deployment options (local, Kubernetes)
### Using a values file
## Key Features
Create a `custom-values.yaml` file:
- **90+ MCP Tools** - Comprehensive API coverage across 8 Nextcloud apps
- **MCP Resources** - Structured data URIs for browsing Nextcloud data
- **Semantic Search (Experimental)** - Optional vector-powered search for Notes (requires Qdrant + Ollama)
- **Document Processing** - OCR and text extraction from PDFs, DOCX, images with progress notifications
- **Flexible Deployment** - Docker, Kubernetes (Helm), VM, or local installation
- **Production-Ready Auth** - Basic Auth with app passwords (recommended) or OAuth2/OIDC (experimental)
- **Multiple Transports** - SSE, HTTP, and streamable-http support
```yaml
nextcloud:
host: https://cloud.example.com
## Supported Apps
auth:
mode: basic
basic:
username: myuser
password: mypassword
| App | Tools | Capabilities |
|-----|-------|--------------|
| **Notes** | 7 | Full CRUD, keyword search, semantic search |
| **Calendar** | 20+ | Events, todos (tasks), recurring events, attendees, availability |
| **Contacts** | 8 | Full CardDAV support, address books |
| **Files (WebDAV)** | 12 | Filesystem access, OCR/document processing |
| **Deck** | 15 | Boards, stacks, cards, labels, assignments |
| **Cookbook** | 13 | Recipe management, URL import (schema.org) |
| **Tables** | 5 | Row operations on Nextcloud Tables |
| **Sharing** | 10+ | Create and manage shares |
| **Semantic Search** | 2+ | Vector search for Notes (experimental, opt-in, requires infrastructure) |
resources:
limits:
cpu: 1000m
memory: 512Mi
requests:
cpu: 100m
memory: 128Mi
```
Want to see another Nextcloud app supported? [Open an issue](https://github.com/cbcoutinho/nextcloud-mcp-server/issues) or contribute a pull request!
Install with your custom values:
## Authentication
```bash
helm install nextcloud-mcp nextcloud-mcp/nextcloud-mcp-server -f custom-values.yaml
```
> [!IMPORTANT]
> **OAuth2/OIDC is experimental** and requires a manual patch to the `user_oidc` app:
> - **Required patch**: Bearer token support ([issue #1221](https://github.com/nextcloud/user_oidc/issues/1221))
> - **Impact**: Without the patch, most app-specific APIs fail with 401 errors
> - **Recommendation**: Use Basic Auth for production until upstream patches are merged
>
> See [docs/oauth-upstream-status.md](docs/oauth-upstream-status.md) for patch status and workarounds.
### OAuth Authentication Mode (Experimental)
**Recommended:** Basic Auth with app-specific passwords provides secure, production-ready authentication. See [docs/authentication.md](docs/authentication.md) for setup details and OAuth configuration.
**Warning:** OAuth mode is experimental and requires patches to the Nextcloud `user_oidc` app. See the [Authentication Guide](https://github.com/cbcoutinho/nextcloud-mcp-server#authentication) for details.
### Authentication Modes
```yaml
nextcloud:
host: https://cloud.example.com
mcpServerUrl: https://mcp.example.com
publicIssuerUrl: https://cloud.example.com
The server supports two authentication modes:
auth:
mode: oauth
oauth:
# Optional: provide pre-registered client credentials
# If not provided, will use Dynamic Client Registration
clientId: "your-client-id"
clientSecret: "your-client-secret"
persistence:
enabled: true
size: 100Mi
**Single-User Mode (BasicAuth):**
- One set of credentials shared by all MCP clients
- Simple setup: username + app password in environment variables
- All clients access Nextcloud as the same user
- Best for: Personal use, development, single-user deployments
ingress:
enabled: true
className: nginx
hosts:
- host: mcp.example.com
paths:
- path: /
pathType: Prefix
tls:
- secretName: nextcloud-mcp-tls
hosts:
- mcp.example.com
```
**Multi-User Mode (OAuth):**
- Each MCP client authenticates separately with their own Nextcloud account
- Per-user scopes and permissions (clients only see tools they're authorized for)
- More secure: tokens expire, credentials never shared with server
- Best for: Teams, multi-user deployments, production environments with multiple users
## Configuration
See [docs/authentication.md](docs/authentication.md) for detailed setup instructions.
### Key Configuration Parameters
## Semantic Search
#### Nextcloud Connection
The server provides an experimental RAG pipeline to enable _Semantic Search_ that enables MCP clients to find information in Nextcloud based on **meaning** rather than just keywords. Instead of matching "machine learning" only when those exact words appear, it understands that "neural networks," "AI models," and "deep learning" are semantically related concepts.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `nextcloud.host` | URL of your Nextcloud instance (required) | `""` |
| `nextcloud.mcpServerUrl` | MCP server URL for OAuth callbacks (OAuth only, optional) | Smart default* |
| `nextcloud.publicIssuerUrl` | Public URL for browser-accessible OAuth authorization endpoint (OAuth only, optional) | Smart default** |
**Smart Defaults:**
- `*mcpServerUrl`: If not set, automatically uses ingress host (if enabled) or `http://localhost:8000` (for port-forward setups)
- `**publicIssuerUrl`: If not set, defaults to `nextcloud.host`. **Only used for authorization endpoints** that browsers must access. All server-to-server endpoints (token, JWKS, introspection, userinfo) use URLs from OIDC discovery without rewriting
#### Authentication
| Parameter | Description | Default |
|-----------|-------------|---------|
| `auth.mode` | Authentication mode: `basic` or `oauth` | `basic` |
| `auth.basic.username` | Nextcloud username (basic auth) | `""` |
| `auth.basic.password` | Nextcloud password (basic auth) | `""` |
| `auth.basic.existingSecret` | Use existing secret for credentials | `""` |
| `auth.oauth.clientId` | OAuth client ID (OAuth mode, optional) | `""` |
| `auth.oauth.clientSecret` | OAuth client secret (OAuth mode, optional) | `""` |
| `auth.oauth.persistence.enabled` | Enable persistent storage for OAuth | `true` |
| `auth.oauth.persistence.size` | Size of OAuth storage PVC | `100Mi` |
#### Data Storage
The `/app/data` directory is used for application data (token databases, Qdrant persistent storage, etc.). It is always mounted as writable to support the read-only root filesystem security context.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `dataStorage.enabled` | Enable persistent storage for `/app/data` | `false` |
| `dataStorage.size` | Size of data storage PVC | `1Gi` |
| `dataStorage.storageClass` | Storage class (leave empty for default) | `""` |
| `dataStorage.accessMode` | Access mode | `ReadWriteOnce` |
| `dataStorage.existingClaim` | Use existing PVC | `""` |
**When to enable persistence:**
- Multi-user basic auth with offline access (stores `tokens.db`)
- Qdrant persistent mode (stores vector database)
- Any feature requiring persistent app data
**When persistence is disabled:** Uses `emptyDir` (non-persistent, data lost on pod restart, but directory remains writable).
#### MCP Server Configuration
| Parameter | Description | Default |
|-----------|-------------|---------|
| `mcp.transport` | Transport mode | `streamable-http` |
| `mcp.port` | Server port (used by both auth modes) | `8000` |
| `mcp.extraArgs` | Additional command-line arguments | `[]` |
The `extraArgs` parameter allows you to pass additional command-line arguments to the MCP server. This is useful for enabling debug logging, enabling specific apps, or other runtime configuration.
**Example:**
- **Keyword search**: Query "car" only finds notes containing "car"
- **Semantic search**: Query "car" also finds notes about "automobile," "vehicle," "sedan," "transportation"
```yaml
mcp:
extraArgs:
- "--log-level"
- "debug"
- "--enable-app"
- "notes"
```
This enables natural language queries and helps discover related content across your Nextcloud notes.
#### Image Configuration
> [!NOTE]
> **Semantic Search is experimental and opt-in:**
> - Disabled by default (`VECTOR_SYNC_ENABLED=false`)
> - Currently supports Notes app only (multi-app support planned)
> - Requires additional infrastructure: vector database + embedding service
> - Answer generation (`nc_semantic_search_answer`) requires MCP client sampling support
>
> See [docs/semantic-search-architecture.md](docs/semantic-search-architecture.md) for architecture details and [docs/configuration.md](docs/configuration.md) for setup instructions.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `image.repository` | Container image repository | `ghcr.io/cbcoutinho/nextcloud-mcp-server` |
| `image.pullPolicy` | Image pull policy | `IfNotPresent` |
## Documentation
**Note:** Image tag is automatically set to the chart's `appVersion` and cannot be overridden.
### Getting Started
- **[Installation](docs/installation.md)** - Docker, Kubernetes, local, or VM deployment
- **[Configuration](docs/configuration.md)** - Environment variables and advanced options
- **[Authentication](docs/authentication.md)** - Basic Auth vs OAuth2/OIDC setup
- **[Running the Server](docs/running.md)** - Start, manage, and troubleshoot
#### Resources
### Features
- **[App Documentation](docs/)** - Notes, Calendar, Contacts, WebDAV, Deck, Cookbook, Tables
- **[Document Processing](docs/configuration.md#document-processing)** - OCR and text extraction setup
- **[Semantic Search Architecture](docs/semantic-search-architecture.md)** - Experimental vector search (Notes only, opt-in)
| Parameter | Description | Default |
|-----------|-------------|---------|
| `resources.limits.cpu` | CPU limit | `1000m` |
| `resources.limits.memory` | Memory limit | `512Mi` |
| `resources.requests.cpu` | CPU request | `100m` |
| `resources.requests.memory` | Memory request | `128Mi` |
### Advanced Topics
- **[OAuth Architecture](docs/oauth-architecture.md)** - How OAuth works (experimental)
- **[OAuth Quick Start](docs/quickstart-oauth.md)** - 5-minute OAuth setup
- **[OAuth Setup Guide](docs/oauth-setup.md)** - Detailed OAuth configuration
- **[Troubleshooting](docs/troubleshooting.md)** - Common issues and solutions
- **[Comparison with Context Agent](docs/comparison-context-agent.md)** - When to use each approach
#### Service
| Parameter | Description | Default |
|-----------|-------------|---------|
| `service.type` | Service type | `ClusterIP` |
| `service.port` | Service port | `8000` |
#### Ingress
| Parameter | Description | Default |
|-----------|-------------|---------|
| `ingress.enabled` | Enable ingress | `false` |
| `ingress.className` | Ingress class name | `""` |
| `ingress.hosts` | Ingress host configuration | See values.yaml |
| `ingress.tls` | Ingress TLS configuration | `[]` |
#### Autoscaling
| Parameter | Description | Default |
|-----------|-------------|---------|
| `autoscaling.enabled` | Enable HPA | `false` |
| `autoscaling.minReplicas` | Minimum replicas | `1` |
| `autoscaling.maxReplicas` | Maximum replicas | `10` |
| `autoscaling.targetCPUUtilizationPercentage` | Target CPU % | `80` |
#### Health Probes
| Parameter | Description | Default |
|-----------|-------------|---------|
| `livenessProbe.httpGet.path` | Liveness probe endpoint | `/health/live` |
| `livenessProbe.initialDelaySeconds` | Initial delay for liveness | `30` |
| `livenessProbe.periodSeconds` | Check interval for liveness | `10` |
| `readinessProbe.httpGet.path` | Readiness probe endpoint | `/health/ready` |
| `readinessProbe.initialDelaySeconds` | Initial delay for readiness | `10` |
| `readinessProbe.periodSeconds` | Check interval for readiness | `5` |
The application exposes HTTP health check endpoints:
- `/health/live` - Liveness probe (checks if application is running)
- `/health/ready` - Readiness probe (checks if application is ready to serve traffic)
#### Document Processing (Optional)
| Parameter | Description | Default |
|-----------|-------------|---------|
| `documentProcessing.enabled` | Enable document processing | `false` |
| `documentProcessing.defaultProcessor` | Default processor | `unstructured` |
| `documentProcessing.unstructured.enabled` | Enable Unstructured.io processor | `false` |
| `documentProcessing.unstructured.apiUrl` | Unstructured API URL | `http://unstructured:8000` |
| `documentProcessing.tesseract.enabled` | Enable Tesseract OCR | `false` |
#### Vector Search & Semantic Capabilities (Optional)
Enable semantic search capabilities with BM25 hybrid search by deploying a vector database (Qdrant) and embedding service (Ollama or OpenAI).
**Semantic Search Configuration:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `semanticSearch.enabled` | Enable semantic search and background vector synchronization | `false` |
| `semanticSearch.scanInterval` | Scan interval in seconds | `3600` |
| `semanticSearch.processorWorkers` | Number of concurrent processor workers | `3` |
| `semanticSearch.queueMaxSize` | Maximum queue size for pending documents | `10000` |
**Document Chunking Configuration:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `documentChunking.chunkSize` | Number of words per chunk for embedding | `512` |
| `documentChunking.chunkOverlap` | Number of overlapping words between chunks | `50` |
**Chunking Strategy:**
- **Small chunks (256-384)**: Better precision for searches, more storage overhead
- **Medium chunks (512-768)**: Balanced approach (recommended for most use cases)
- **Large chunks (1024+)**: Better context preservation, less precise matching
- **Overlap**: Should be 10-20% of chunk size to preserve context across boundaries
**Qdrant Vector Database:**
Qdrant is deployed as a subchart when `qdrant.enabled` is `true`. All configuration values are passed through to the [qdrant/qdrant](https://github.com/qdrant/qdrant-helm) chart.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `qdrant.enabled` | Deploy Qdrant as a subchart | `false` |
| `qdrant.replicaCount` | Number of Qdrant replicas | `1` |
| `qdrant.image.tag` | Qdrant version | `v1.12.5` |
| `qdrant.apiKey` | Optional API key for authentication | `""` |
| `qdrant.persistence.size` | Storage size for vector data | `10Gi` |
| `qdrant.persistence.storageClass` | Storage class | `""` |
| `qdrant.resources.requests.cpu` | CPU request | `200m` |
| `qdrant.resources.requests.memory` | Memory request | `512Mi` |
| `qdrant.resources.limits.cpu` | CPU limit | `1000m` |
| `qdrant.resources.limits.memory` | Memory limit | `2Gi` |
**Ollama Embedding Service:**
Ollama is deployed as a subchart when `ollama.enabled` is `true`. All configuration values are passed through to the [ollama/ollama](https://github.com/otwld/ollama-helm) chart. Alternatively, set `ollama.url` to use an external Ollama instance.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `ollama.enabled` | Deploy Ollama as a subchart | `false` |
| `ollama.url` | External Ollama URL (use with `enabled: false`) | `""` |
| `ollama.embeddingModel` | Embedding model to use | `nomic-embed-text` |
| `ollama.verifySsl` | Verify SSL certificates | `true` |
| `ollama.replicaCount` | Number of Ollama replicas | `1` |
| `ollama.ollama.models.pull` | Models to pull on startup | `["nomic-embed-text"]` |
| `ollama.persistentVolume.enabled` | Enable persistent storage | `true` |
| `ollama.persistentVolume.size` | Storage size for models | `20Gi` |
| `ollama.resources.requests.cpu` | CPU request | `500m` |
| `ollama.resources.requests.memory` | Memory request | `1Gi` |
| `ollama.resources.limits.cpu` | CPU limit | `2000m` |
| `ollama.resources.limits.memory` | Memory limit | `4Gi` |
**OpenAI Embedding Provider (Alternative):**
Use OpenAI or any OpenAI-compatible API instead of Ollama.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `openai.enabled` | Enable OpenAI embedding provider | `false` |
| `openai.apiKey` | OpenAI API key | `""` |
| `openai.existingSecret` | Use existing secret for API key | `""` |
| `openai.secretKey` | Key in secret containing API key | `api-key` |
| `openai.baseUrl` | Custom API endpoint (optional) | `""` |
#### Observability & Monitoring
The chart includes comprehensive observability features including Prometheus metrics, OpenTelemetry tracing, and Grafana dashboards.
**Metrics Configuration:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `observability.metrics.enabled` | Enable Prometheus metrics | `true` |
| `observability.metrics.port` | Metrics port | `9090` |
| `observability.metrics.path` | Metrics endpoint path | `/metrics` |
**Tracing Configuration:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `observability.tracing.enabled` | Enable OpenTelemetry tracing | `false` |
| `observability.tracing.endpoint` | OTLP collector endpoint | `""` |
| `observability.tracing.serviceName` | Service name in traces | `nextcloud-mcp-server` |
| `observability.tracing.samplingRate` | Trace sampling rate (0.0-1.0) | `1.0` |
**Logging Configuration:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `observability.logging.format` | Log format (json or text) | `json` |
| `observability.logging.level` | Log level | `INFO` |
| `observability.logging.includeTraceContext` | Include trace IDs in logs | `true` |
**ServiceMonitor (Prometheus Operator):**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `serviceMonitor.enabled` | Create ServiceMonitor resource | `false` |
| `serviceMonitor.interval` | Scrape interval | `30s` |
| `serviceMonitor.scrapeTimeout` | Scrape timeout | `10s` |
| `serviceMonitor.labels` | Additional labels for ServiceMonitor | `{}` |
**PrometheusRule (Prometheus Operator):**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `prometheusRule.enabled` | Create PrometheusRule with alert rules | `false` |
| `prometheusRule.labels` | Additional labels for PrometheusRule | `{}` |
**Grafana Dashboards:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `dashboards.enabled` | Enable automatic dashboard provisioning | `false` |
| `dashboards.grafanaFolder` | Grafana folder name for dashboards | `Nextcloud MCP` |
| `dashboards.labels` | Additional labels for dashboard ConfigMap | `{}` |
| `dashboards.annotations` | Additional annotations for dashboard ConfigMap | `{}` |
When `dashboards.enabled` is `true`, a ConfigMap with the Grafana dashboard is created with the `grafana_dashboard: "1"` label. This enables automatic discovery by Grafana sidecar containers (commonly used with kube-prometheus-stack).
The dashboard provides comprehensive monitoring including:
- HTTP request metrics (RED pattern: Rate, Errors, Duration)
- MCP tool performance and errors
- Nextcloud API performance by app (notes, calendar, contacts, etc.)
- OAuth token operations and cache hit rates
- External dependency health (Nextcloud, Qdrant, Keycloak, Unstructured API)
- Vector sync processing pipeline (when enabled)
For manual import or more details, see `charts/nextcloud-mcp-server/dashboards/README.md`.
## Examples
### Create a Note
```
AI: "Create a note called 'Meeting Notes' with today's agenda"
→ Uses nc_notes_create_note tool
### Example 1: Basic Auth with Ingress
```yaml
nextcloud:
host: https://cloud.example.com
auth:
mode: basic
basic:
username: admin
password: secure-password
ingress:
enabled: true
className: nginx
annotations:
cert-manager.io/cluster-issuer: letsencrypt-prod
hosts:
- host: mcp.example.com
paths:
- path: /
pathType: Prefix
tls:
- secretName: mcp-tls
hosts:
- mcp.example.com
resources:
limits:
cpu: 2000m
memory: 1Gi
requests:
cpu: 200m
memory: 256Mi
```
### Import Recipes
```
AI: "Import the recipe from https://www.example.com/recipe/chocolate-cake"
→ Uses nc_cookbook_import_recipe tool with schema.org metadata extraction
### Example 2: Using Existing Secrets
#### Basic Auth with Existing Secret
Create a secret manually:
```bash
kubectl create secret generic nextcloud-credentials \
--from-literal=username=myuser \
--from-literal=password=mypassword
```
### Schedule Meetings
```
AI: "Schedule a team meeting for next Tuesday at 2pm"
→ Uses nc_calendar_create_event tool
Then reference it in your values:
```yaml
nextcloud:
host: https://cloud.example.com
auth:
mode: basic
basic:
existingSecret: nextcloud-credentials
usernameKey: username
passwordKey: password
```
### Manage Files
```
AI: "Create a folder called 'Project X' and move all PDFs there"
→ Uses nc_webdav_create_directory and nc_webdav_move tools
#### OAuth with Existing Secret (Pre-registered Client)
If you have a pre-registered OAuth client:
```bash
kubectl create secret generic nextcloud-oauth-creds \
--from-literal=clientId=my-oauth-client-id \
--from-literal=clientSecret=my-oauth-client-secret
```
### Semantic Search (Experimental, Opt-in)
```
AI: "Find notes related to machine learning concepts"
→ Uses nc_semantic_search to find semantically similar notes (requires Qdrant + Ollama setup)
Then reference it in your values:
```yaml
nextcloud:
host: https://cloud.example.com
# mcpServerUrl and publicIssuerUrl are optional!
# If not set, mcpServerUrl defaults to ingress host or localhost
# publicIssuerUrl defaults to nextcloud.host (only used for browser-accessible auth endpoint)
auth:
mode: oauth
oauth:
existingSecret: nextcloud-oauth-creds
clientIdKey: clientId
clientSecretKey: clientSecret
persistence:
enabled: true
ingress:
enabled: true
hosts:
- host: mcp.example.com
paths:
- path: /
pathType: Prefix
tls:
- secretName: mcp-tls
hosts:
- mcp.example.com
```
**Note:** For AI-generated answers with citations, use `nc_semantic_search_answer` (requires MCP client with sampling support).
### Example 3: OAuth with Document Processing and Dynamic Client Registration
## Contributing
This example shows OAuth without pre-registered credentials (using DCR) and optional URL values:
Contributions are welcome!
```yaml
nextcloud:
host: https://cloud.example.com
# mcpServerUrl will automatically use ingress host (https://mcp.example.com)
# publicIssuerUrl will automatically default to nextcloud.host (only used for browser-accessible auth endpoint)
- Report bugs or request features: [GitHub Issues](https://github.com/cbcoutinho/nextcloud-mcp-server/issues)
- Submit improvements: [Pull Requests](https://github.com/cbcoutinho/nextcloud-mcp-server/pulls)
- Development guidelines: [CLAUDE.md](CLAUDE.md)
auth:
mode: oauth
oauth:
# No clientId/clientSecret - will use Dynamic Client Registration!
persistence:
enabled: true
storageClass: fast-ssd
size: 200Mi
## Security
documentProcessing:
enabled: true
defaultProcessor: unstructured
unstructured:
enabled: true
apiUrl: http://unstructured-api:8000
strategy: hi_res
languages: eng,deu,fra
[![MseeP.ai Security Assessment](https://mseep.net/pr/cbcoutinho-nextcloud-mcp-server-badge.png)](https://mseep.ai/app/cbcoutinho-nextcloud-mcp-server)
ingress:
enabled: true
className: nginx
hosts:
- host: mcp.example.com
paths:
- path: /
pathType: Prefix
```
This project takes security seriously:
- Production-ready Basic Auth with app-specific passwords
- OAuth2/OIDC support (experimental, requires upstream patches)
- Per-user access tokens
- No credential storage in OAuth mode
- Regular security assessments
### Example 4: High Availability with Autoscaling
Found a security issue? Please report it privately to the maintainers.
```yaml
replicaCount: 2
autoscaling:
enabled: true
minReplicas: 2
maxReplicas: 20
targetCPUUtilizationPercentage: 70
targetMemoryUtilizationPercentage: 80
resources:
limits:
cpu: 2000m
memory: 1Gi
requests:
cpu: 500m
memory: 512Mi
affinity:
podAntiAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 100
podAffinityTerm:
labelSelector:
matchExpressions:
- key: app.kubernetes.io/name
operator: In
values:
- nextcloud-mcp-server
topologyKey: kubernetes.io/hostname
```
### Example 5: Semantic Search with Qdrant and Ollama
Deploy with vector search capabilities using embedded Qdrant and Ollama:
```yaml
nextcloud:
host: https://cloud.example.com
auth:
mode: basic
basic:
username: admin
password: secure-password
# Enable semantic search
semanticSearch:
enabled: true
scanInterval: 1800 # Scan every 30 minutes
processorWorkers: 5
# Deploy Qdrant as a subchart
qdrant:
enabled: true
persistence:
size: 20Gi
storageClass: fast-ssd
resources:
requests:
cpu: 500m
memory: 1Gi
limits:
cpu: 2000m
memory: 4Gi
# Deploy Ollama as a subchart
ollama:
enabled: true
embeddingModel: nomic-embed-text
persistentVolume:
size: 30Gi
storageClass: standard
resources:
requests:
cpu: 1000m
memory: 2Gi
limits:
cpu: 4000m
memory: 8Gi
```
Or use an external Ollama instance:
```yaml
semanticSearch:
enabled: true
qdrant:
enabled: true
# Use external Ollama instead of deploying subchart
ollama:
enabled: false
url: "http://ollama.ai-services.svc.cluster.local:11434"
embeddingModel: nomic-embed-text
```
Or use OpenAI for embeddings:
```yaml
semanticSearch:
enabled: true
qdrant:
enabled: true
# Use OpenAI instead of Ollama
openai:
enabled: true
apiKey: "sk-..."
# Or use existing secret:
# existingSecret: openai-api-key
# secretKey: api-key
```
## Upgrading
### To upgrade an existing deployment:
```bash
# Update the repository
helm repo update
# Upgrade with your custom values
helm upgrade nextcloud-mcp nextcloud-mcp/nextcloud-mcp-server -f custom-values.yaml
```
### To upgrade with new values:
```bash
helm upgrade nextcloud-mcp nextcloud-mcp/nextcloud-mcp-server \
--set resources.limits.memory=1Gi
```
## Uninstalling
```bash
helm uninstall nextcloud-mcp
```
**Note:** This will delete all resources including PVCs. If you want to preserve OAuth client data, backup the PVC before uninstalling.
## Troubleshooting
### Check pod status
```bash
kubectl get pods -l app.kubernetes.io/name=nextcloud-mcp-server
```
### View logs
```bash
kubectl logs -l app.kubernetes.io/name=nextcloud-mcp-server --tail=100 -f
```
### Check health endpoints
The application exposes health check endpoints for monitoring:
```bash
# Port forward to the service
kubectl port-forward svc/nextcloud-mcp 8000:8000
# Check liveness (if app is running)
curl http://localhost:8000/health/live
# Check readiness (if app is ready to serve traffic)
curl http://localhost:8000/health/ready
```
**Example responses:**
Liveness (always returns 200 if running):
```json
{
"status": "alive",
"mode": "basic"
}
```
Readiness (returns 200 if ready, 503 if not ready):
```json
{
"status": "ready",
"checks": {
"nextcloud_configured": "ok",
"auth_mode": "basic",
"auth_configured": "ok"
}
}
```
### Common Issues
1. **Connection refused to Nextcloud**
- Verify `nextcloud.host` is accessible from the Kubernetes cluster
- For OAuth mode: Ensure MCP server can reach OIDC discovery endpoints (token, JWKS, introspection, userinfo URLs)
- Check network policies and firewall rules
- Note: Do not use internal Docker hostnames (like `http://app:80`) for `nextcloud.host` - use externally resolvable URLs
2. **Authentication failures**
- For basic auth: verify username/password are correct
- For OAuth: check that OIDC app is properly configured
3. **OAuth persistence issues**
- Verify PVC is bound: `kubectl get pvc`
- Check storage class exists: `kubectl get storageclass`
4. **Resource constraints**
- Increase memory limits if seeing OOM errors
- Adjust CPU requests based on load
## Security Considerations
1. **Secrets Management**: Consider using external secret management (e.g., Sealed Secrets, External Secrets Operator)
2. **TLS**: Always use TLS/HTTPS for production deployments
3. **Network Policies**: Restrict network access to necessary services only
4. **RBAC**: Review and customize ServiceAccount permissions as needed
5. **App Passwords**: For basic auth, use Nextcloud app passwords instead of main account passwords
## Support
- GitHub Issues: https://github.com/cbcoutinho/nextcloud-mcp-server/issues
- Documentation: https://github.com/cbcoutinho/nextcloud-mcp-server#readme
## License
This project is licensed under the AGPL-3.0 License. See [LICENSE](./LICENSE) for details.
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=cbcoutinho/nextcloud-mcp-server&type=Date)](https://www.star-history.com/#cbcoutinho/nextcloud-mcp-server&Date)
## References
- [Model Context Protocol](https://github.com/modelcontextprotocol)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- [Nextcloud](https://nextcloud.com/)
This chart is licensed under AGPL-3.0, consistent with the Nextcloud MCP Server project.
-18
View File
@@ -1,18 +0,0 @@
diff --git a/lib/private/AppFramework/Middleware/Security/CORSMiddleware.php b/lib/private/AppFramework/Middleware/Security/CORSMiddleware.php
index 4453f5a7d4b..f1ca9b48d21 100644
--- a/lib/private/AppFramework/Middleware/Security/CORSMiddleware.php
+++ b/lib/private/AppFramework/Middleware/Security/CORSMiddleware.php
@@ -73,6 +73,13 @@ class CORSMiddleware extends Middleware {
$user = array_key_exists('PHP_AUTH_USER', $this->request->server) ? $this->request->server['PHP_AUTH_USER'] : null;
$pass = array_key_exists('PHP_AUTH_PW', $this->request->server) ? $this->request->server['PHP_AUTH_PW'] : null;
+ // Allow Bearer token authentication for CORS requests
+ // Bearer tokens are stateless and don't require CSRF protection
+ $authorizationHeader = $this->request->getHeader('Authorization');
+ if (!empty($authorizationHeader) && str_starts_with($authorizationHeader, 'Bearer ')) {
+ return;
+ }
+
// Allow to use the current session if a CSRF token is provided
if ($this->request->passesCSRFCheck()) {
return;
@@ -2,30 +2,4 @@
set -euox pipefail
echo "Installing and configuring notes app for testing..."
# Check if development notes app is mounted at /opt/apps/notes
if [ -d /opt/apps/notes ]; then
echo "Development notes app found at /opt/apps/notes"
# Remove any existing notes app in apps (from app store or old symlink)
if [ -e /var/www/html/apps/notes ]; then
echo "Removing existing notes in apps..."
rm -rf /var/www/html/apps/notes
fi
# Create symlink from apps to the mounted development version
# Per Nextcloud docs: apps outside server root need symlinks in server root
echo "Creating symlink: apps/notes -> /opt/apps/notes"
ln -sf /opt/apps/notes /var/www/html/apps/notes
echo "Enabling notes app from /opt/apps (development mode via symlink)"
php /var/www/html/occ app:enable notes
elif [ -d /var/www/html/apps/notes ]; then
echo "notes app directory found in apps (already installed)"
php /var/www/html/occ app:enable notes
else
echo "notes app not found, installing from app store..."
php /var/www/html/occ app:install notes
php /var/www/html/occ app:enable notes
fi
php /var/www/html/occ app:enable notes
@@ -31,10 +31,8 @@ else
fi
# Configure OIDC Identity Provider with dynamic client registration enabled
php /var/www/html/occ config:app:set oidc dynamic_client_registration --value='true' # NOTE: String
php /var/www/html/occ config:app:set oidc dynamic_client_registration --value='true'
php /var/www/html/occ config:app:set oidc proof_key_for_code_exchange --value=true --type=boolean
php /var/www/html/occ config:app:set oidc allow_user_settings --value='enabled'
php /var/www/html/occ config:app:set oidc default_token_type --value='jwt'
php /var/www/html/occ config:app:set oidc default_resource_identifier --value='http://localhost:8080'
echo "OIDC app installed and configured successfully"
@@ -9,13 +9,5 @@ php /var/www/html/occ app:enable user_oidc
# Configure user_oidc to validate bearer tokens from the OIDC Identity Provider
php /var/www/html/occ config:system:set user_oidc oidc_provider_bearer_validation --value=true --type=boolean
php /var/www/html/occ config:system:set user_oidc httpclient.allowselfsigned --value=true --type=boolean
# Allow Nextcloud to connect to local/internal servers (required for external IdP mode)
# This enables user_oidc to fetch JWKS from internal Keycloak container
php /var/www/html/occ config:system:set allow_local_remote_servers --value=true --type=boolean
# Note: The user_oidc app_api session flag patch is NOT required when using the
# CORSMiddleware Bearer token patch (20-apply-cors-bearer-token-patch.sh).
# The CORSMiddleware patch fixes the root cause by allowing Bearer tokens to bypass
# CORS/CSRF checks at the framework level.
patch -u /var/www/html/custom_apps/user_oidc/lib/User/Backend.php -i /docker-entrypoint-hooks.d/post-installation/0001-Fix-Bearer-token-authentication-causing-session-logo.patch
@@ -1,100 +0,0 @@
#!/bin/bash
#
# Configure user_oidc to accept bearer tokens from Keycloak
#
# This script sets up Keycloak as an external OIDC provider for Nextcloud.
# It enables bearer token validation, allowing the MCP server to use Keycloak
# tokens to access Nextcloud APIs without admin credentials.
#
set -e
echo "===================================================================="
echo "Configuring user_oidc provider for Keycloak..."
echo "===================================================================="
# Wait for Keycloak to be ready and realm to be available
echo "Waiting for Keycloak realm to be available..."
MAX_RETRIES=30
RETRY_COUNT=0
while [ $RETRY_COUNT -lt $MAX_RETRIES ]; do
if curl -sf http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration > /dev/null 2>&1; then
echo "✓ Keycloak realm is ready"
break
fi
echo " Waiting for Keycloak... (attempt $((RETRY_COUNT + 1))/$MAX_RETRIES)"
sleep 5
RETRY_COUNT=$((RETRY_COUNT + 1))
done
if [ $RETRY_COUNT -eq $MAX_RETRIES ]; then
echo "⚠ Warning: Keycloak not available after $MAX_RETRIES attempts"
echo " Keycloak provider will not be configured"
echo " You can configure it manually using:"
echo " docker compose exec app php occ user_oidc:provider keycloak \\"
echo " --clientid='nextcloud' \\"
echo " --clientsecret='nextcloud-secret-change-in-production' \\"
echo " --discoveryuri='http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration' \\"
echo " --check-bearer=1 \\"
echo " --bearer-provisioning=1 \\"
echo " --unique-uid=1"
exit 0
fi
# Check if provider already exists
if php /var/www/html/occ user_oidc:provider keycloak 2>/dev/null | grep -q "Identifier"; then
echo " Keycloak provider already exists, updating configuration..."
# Update existing provider
php /var/www/html/occ user_oidc:provider keycloak \
--clientid="nextcloud" \
--clientsecret="nextcloud-secret-change-in-production" \
--discoveryuri="http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration" \
--check-bearer=1 \
--bearer-provisioning=1 \
--unique-uid=1 \
--mapping-uid="sub" \
--mapping-display-name="name" \
--mapping-email="email" \
--scope="openid profile email offline_access"
echo "✓ Updated Keycloak provider configuration"
else
echo " Creating new Keycloak provider..."
# Create new provider
php /var/www/html/occ user_oidc:provider keycloak \
--clientid="nextcloud" \
--clientsecret="nextcloud-secret-change-in-production" \
--discoveryuri="http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration" \
--check-bearer=1 \
--bearer-provisioning=1 \
--unique-uid=1 \
--mapping-uid="sub" \
--mapping-display-name="name" \
--mapping-email="email" \
--scope="openid profile email offline_access"
echo "✓ Created Keycloak provider"
fi
# Display provider details
echo ""
echo "Keycloak provider configuration:"
php /var/www/html/occ user_oidc:provider keycloak
echo ""
echo "===================================================================="
echo "✓ Keycloak provider configured successfully"
echo "===================================================================="
echo ""
echo "Key features enabled:"
echo " • Bearer token validation (--check-bearer=1)"
echo " • Automatic user provisioning (--bearer-provisioning=1)"
echo " • Unique user IDs (--unique-uid=1)"
echo " • Offline access scope (for refresh tokens)"
echo ""
echo "MCP server can now use Keycloak tokens to access Nextcloud APIs"
echo "without admin credentials (ADR-002 architecture)."
echo ""
@@ -1,64 +0,0 @@
#!/bin/bash
#
# Apply upstream CORSMiddleware Bearer token authentication patch
#
# This patch allows Bearer tokens to bypass CORS/CSRF checks, fixing
# authentication issues with app-specific APIs (Notes, Calendar, etc.)
# when using OAuth/OIDC Bearer tokens.
#
# Upstream PR: https://github.com/nextcloud/server/pull/55878
# Commit: 8fb5e77db82 (fix(cors): Allow Bearer token authentication)
#
set -e
PATCH_FILE="/docker-entrypoint-hooks.d/patches/cors-bearer-token.patch"
TARGET_FILE="/var/www/html/lib/private/AppFramework/Middleware/Security/CORSMiddleware.php"
echo "===================================================================="
echo "Applying CORSMiddleware Bearer token authentication patch..."
echo "===================================================================="
# Check if patch file exists
if [ ! -f "$PATCH_FILE" ]; then
echo "⚠ Warning: Patch file not found: $PATCH_FILE"
echo " Skipping CORS Bearer token patch"
exit 0
fi
# Check if target file exists
if [ ! -f "$TARGET_FILE" ]; then
echo "⚠ Warning: Target file not found: $TARGET_FILE"
echo " Skipping CORS Bearer token patch"
exit 0
fi
# Check if already patched
if grep -q "Allow Bearer token authentication for CORS requests" "$TARGET_FILE"; then
echo "✓ CORSMiddleware already patched for Bearer token support"
exit 0
fi
echo "Applying patch to CORSMiddleware.php..."
# Apply the patch
cd /var/www/html
if patch -p1 --dry-run < "$PATCH_FILE" > /dev/null 2>&1; then
patch -p1 < "$PATCH_FILE"
echo "✓ Patch applied successfully"
else
echo "⚠ Warning: Patch failed to apply (may already be applied or file changed)"
echo " This is expected if using a Nextcloud version that already includes the fix"
exit 0
fi
echo ""
echo "===================================================================="
echo "✓ CORSMiddleware Bearer token patch applied"
echo "===================================================================="
echo ""
echo "Benefits:"
echo " • Bearer tokens now work with app-specific APIs (Notes, Calendar, etc.)"
echo " • OAuth/OIDC authentication works without CORS errors"
echo " • Stateless API authentication is properly supported"
echo ""
@@ -1,3 +0,0 @@
#!/bin/bash
php /var/www/html/occ config:app:set --value false firstrunwizard wizard_enabled
-1
View File
@@ -1 +0,0 @@
charts/
-9
View File
@@ -1,9 +0,0 @@
dependencies:
- name: qdrant
repository: https://qdrant.github.io/qdrant-helm
version: 1.15.5
- name: ollama
repository: https://otwld.github.io/ollama-helm
version: 1.34.0
digest: sha256:d51c97d05be2614b751c0dd7267ef7dc959eff5ebef859c5f895c5c554b7a874
generated: "2025-11-09T17:08:02.86648061Z"
+2 -15
View File
@@ -2,8 +2,8 @@ apiVersion: v2
name: nextcloud-mcp-server
description: A Helm chart for Nextcloud MCP Server - enables AI assistants to interact with Nextcloud
type: application
version: 0.36.0
appVersion: "0.36.0"
version: 0.1.0
appVersion: "0.21.0"
keywords:
- nextcloud
- mcp
@@ -21,16 +21,3 @@ home: https://github.com/cbcoutinho/nextcloud-mcp-server
sources:
- https://github.com/cbcoutinho/nextcloud-mcp-server
icon: https://raw.githubusercontent.com/nextcloud/server/master/core/img/logo/logo.svg
annotations:
# Grafana dashboard support
grafana_dashboard: "true"
grafana_dashboard_folder: "Nextcloud MCP"
dependencies:
- name: qdrant
version: "1.15.5"
repository: https://qdrant.github.io/qdrant-helm
condition: qdrant.networkMode.deploySubchart
- name: ollama
version: "1.34.0"
repository: https://otwld.github.io/ollama-helm
condition: ollama.enabled
+35 -14
View File
@@ -99,11 +99,11 @@ ingress:
|-----------|-------------|---------|
| `nextcloud.host` | URL of your Nextcloud instance (required) | `""` |
| `nextcloud.mcpServerUrl` | MCP server URL for OAuth callbacks (OAuth only, optional) | Smart default* |
| `nextcloud.publicIssuerUrl` | Public issuer URL for OAuth (OAuth only, optional) | Smart default** |
| `nextcloud.publicIssuerUrl` | Public URL for browser-accessible OAuth authorization endpoint (OAuth only, optional) | Smart default** |
**Smart Defaults:**
- `*mcpServerUrl`: If not set, automatically uses ingress host (if enabled) or `http://localhost:8000` (for port-forward setups)
- `**publicIssuerUrl`: If not set, automatically defaults to `nextcloud.host` (which works when both clients and MCP server access Nextcloud at the same URL)
- `**publicIssuerUrl`: If not set, defaults to `nextcloud.host`. **Only used for authorization endpoints** that browsers must access. All server-to-server endpoints (token, JWKS, introspection, userinfo) use URLs from OIDC discovery without rewriting
#### Authentication
@@ -118,6 +118,25 @@ ingress:
| `auth.oauth.persistence.enabled` | Enable persistent storage for OAuth | `true` |
| `auth.oauth.persistence.size` | Size of OAuth storage PVC | `100Mi` |
#### Data Storage
The `/app/data` directory is used for application data (token databases, Qdrant persistent storage, etc.). It is always mounted as writable to support the read-only root filesystem security context.
| Parameter | Description | Default |
|-----------|-------------|---------|
| `dataStorage.enabled` | Enable persistent storage for `/app/data` | `false` |
| `dataStorage.size` | Size of data storage PVC | `1Gi` |
| `dataStorage.storageClass` | Storage class (leave empty for default) | `""` |
| `dataStorage.accessMode` | Access mode | `ReadWriteOnce` |
| `dataStorage.existingClaim` | Use existing PVC | `""` |
**When to enable persistence:**
- Multi-user basic auth with offline access (stores `tokens.db`)
- Qdrant persistent mode (stores vector database)
- Any feature requiring persistent app data
**When persistence is disabled:** Uses `emptyDir` (non-persistent, data lost on pod restart, but directory remains writable).
#### MCP Server Configuration
| Parameter | Description | Default |
@@ -208,16 +227,16 @@ The application exposes HTTP health check endpoints:
#### Vector Search & Semantic Capabilities (Optional)
Enable semantic search capabilities by deploying a vector database (Qdrant) and embedding service (Ollama or OpenAI).
Enable semantic search capabilities with BM25 hybrid search by deploying a vector database (Qdrant) and embedding service (Ollama or OpenAI).
**Vector Sync Configuration:**
**Semantic Search Configuration:**
| Parameter | Description | Default |
|-----------|-------------|---------|
| `vectorSync.enabled` | Enable background vector synchronization | `false` |
| `vectorSync.scanInterval` | Scan interval in seconds | `3600` |
| `vectorSync.processorWorkers` | Number of concurrent processor workers | `3` |
| `vectorSync.queueMaxSize` | Maximum queue size for pending documents | `10000` |
| `semanticSearch.enabled` | Enable semantic search and background vector synchronization | `false` |
| `semanticSearch.scanInterval` | Scan interval in seconds | `3600` |
| `semanticSearch.processorWorkers` | Number of concurrent processor workers | `3` |
| `semanticSearch.queueMaxSize` | Maximum queue size for pending documents | `10000` |
**Document Chunking Configuration:**
@@ -427,7 +446,7 @@ nextcloud:
host: https://cloud.example.com
# mcpServerUrl and publicIssuerUrl are optional!
# If not set, mcpServerUrl defaults to ingress host or localhost
# publicIssuerUrl defaults to nextcloud.host
# publicIssuerUrl defaults to nextcloud.host (only used for browser-accessible auth endpoint)
auth:
mode: oauth
@@ -459,7 +478,7 @@ This example shows OAuth without pre-registered credentials (using DCR) and opti
nextcloud:
host: https://cloud.example.com
# mcpServerUrl will automatically use ingress host (https://mcp.example.com)
# publicIssuerUrl will automatically default to nextcloud.host
# publicIssuerUrl will automatically default to nextcloud.host (only used for browser-accessible auth endpoint)
auth:
mode: oauth
@@ -537,8 +556,8 @@ auth:
username: admin
password: secure-password
# Enable vector sync
vectorSync:
# Enable semantic search
semanticSearch:
enabled: true
scanInterval: 1800 # Scan every 30 minutes
processorWorkers: 5
@@ -576,7 +595,7 @@ ollama:
Or use an external Ollama instance:
```yaml
vectorSync:
semanticSearch:
enabled: true
qdrant:
@@ -592,7 +611,7 @@ ollama:
Or use OpenAI for embeddings:
```yaml
vectorSync:
semanticSearch:
enabled: true
qdrant:
@@ -689,7 +708,9 @@ Readiness (returns 200 if ready, 503 if not ready):
1. **Connection refused to Nextcloud**
- Verify `nextcloud.host` is accessible from the Kubernetes cluster
- For OAuth mode: Ensure MCP server can reach OIDC discovery endpoints (token, JWKS, introspection, userinfo URLs)
- Check network policies and firewall rules
- Note: Do not use internal Docker hostnames (like `http://app:80`) for `nextcloud.host` - use externally resolvable URLs
2. **Authentication failures**
- For basic auth: verify username/password are correct
@@ -1,161 +0,0 @@
# Grafana Dashboards
This directory contains example Grafana dashboards for monitoring the Nextcloud MCP Server.
## Dashboards
### nextcloud-mcp-server.json
All-in-one Operations Dashboard with comprehensive monitoring across all system components.
#### Overview Row
High-level metrics for quick health assessment:
- **Request Rate** (stat): Total requests per second
- **Error Rate** (stat): Percentage of 5xx errors with color thresholds
- **P95 Latency** (stat): 95th percentile request latency
- **Active Requests** (stat): Current in-flight requests
#### HTTP Metrics (RED Pattern)
Core request/error/duration metrics:
- **Request Rate by Endpoint** (timeseries): RPS breakdown by endpoint
- **Error Rate by Status Code** (timeseries): Error rates for 4xx/5xx codes
- **Latency Percentiles** (timeseries): P50, P95, P99 latency trends
- **Status Code Distribution** (piechart): Percentage breakdown of all status codes
#### MCP Tools Row
MCP-specific tool performance:
- **Top Tools by Call Volume** (bargauge): Top 10 most-called tools
- **Tool Error Rate** (timeseries): Error rates per tool
- **Tool Execution Duration** (timeseries): P95 latency by tool
#### Nextcloud API Row
Backend API performance metrics:
- **API Calls by App** (timeseries): Request rate per Nextcloud app (notes, calendar, contacts, etc.)
- **API Latency by App** (timeseries): P95 latency per app
- **API Retries by Reason** (timeseries): Retry patterns (429, timeout, connection errors)
- **API Error Rate** (stat): Overall API error percentage
#### OAuth & Authentication Row
OAuth token operations and caching:
- **Token Validations** (timeseries): Success/failure rates for token validation
- **Token Exchange Operations** (timeseries): RFC 8693 token exchange operations
- **Token Cache Hit Rate** (stat): Percentage of cache hits (color-coded: red<50%, yellow<80%, green≥80%)
- **Refresh Token Operations** (timeseries): Refresh token storage operations by type
#### Dependencies & Health Row
External dependency status monitoring:
- **Nextcloud Health** (stat): UP/DOWN status with color coding
- **Qdrant Health** (stat): Vector database health status
- **Keycloak Health** (stat): Identity provider health status
- **Unstructured API Health** (stat): Document processing API status
- **Health Check Duration** (timeseries): Health check latency by dependency
- **Database Operation Latency** (timeseries): P95 latency for DB operations (SQLite, Qdrant)
#### Vector Sync Row (when enabled)
Document processing pipeline metrics:
- **Documents Processed Rate** (timeseries): Processing throughput by status (success/failure)
- **Processing Queue Depth** (gauge): Current queue size with thresholds (yellow>50, red>100)
- **Qdrant Operations** (timeseries): Vector database operations by type
- **Document Processing Duration** (timeseries): P95 processing latency
## Importing to Grafana
### Manual Import
1. Open Grafana UI
2. Navigate to Dashboards → Import
3. Upload `nextcloud-mcp-server.json`
4. Select your Prometheus data source
5. Click "Import"
### Automated Import (Helm Chart)
The Helm chart now supports automatic dashboard provisioning via Grafana sidecar pattern.
#### Option 1: Using Helm Chart (Recommended)
Enable dashboard provisioning in your Helm values:
```yaml
# values.yaml for nextcloud-mcp-server chart
dashboards:
enabled: true
grafanaFolder: "Nextcloud MCP" # Folder name in Grafana
labels: {} # Additional labels if needed
```
Then deploy or upgrade:
```bash
helm upgrade --install nextcloud-mcp nextcloud-mcp-server \
--set dashboards.enabled=true
```
The dashboard will be automatically imported by Grafana if the sidecar is configured
to watch for ConfigMaps with label `grafana_dashboard: "1"`.
#### Option 2: Using kube-prometheus-stack
If using kube-prometheus-stack with Grafana sidecar enabled, the dashboard will be
automatically discovered and imported. Ensure your Grafana deployment has:
```yaml
# kube-prometheus-stack values
grafana:
sidecar:
dashboards:
enabled: true
label: grafana_dashboard
folder: /tmp/dashboards
provider:
foldersFromFilesStructure: true
```
#### Option 3: Manual ConfigMap Creation
For other Grafana setups, create a ConfigMap manually:
```bash
kubectl create configmap nextcloud-mcp-dashboard \
--from-file=nextcloud-mcp-server.json \
-n monitoring
# Add sidecar discovery label
kubectl label configmap nextcloud-mcp-dashboard \
grafana_dashboard=1 \
-n monitoring
# Add folder annotation (annotations support spaces, unlike labels)
kubectl annotate configmap nextcloud-mcp-dashboard \
grafana_folder="Nextcloud MCP" \
-n monitoring
```
## Dashboard Variables
The dashboard includes four template variables for dynamic filtering:
- **datasource**: Select your Prometheus data source
- **namespace**: Filter metrics by Kubernetes namespace (supports "All")
- **pod**: Filter by specific pod(s) - multi-select enabled (supports "All")
- **interval**: Query interval for rate calculations (1m, 5m, 10m, 30m, 1h - default: 5m)
## Customization
You can customize the dashboard by:
1. Adjusting refresh rate (default: 30s)
2. Modifying time range (default: last 6 hours)
3. Adding new panels for specific metrics
4. Adjusting thresholds in existing panels
## Metrics Reference
All metrics are documented in `/docs/observability.md`. Key metric prefixes:
- `mcp_http_*` - HTTP server metrics
- `mcp_tool_*` - MCP tool invocation metrics
- `mcp_nextcloud_api_*` - Nextcloud API call metrics
- `mcp_oauth_*` - OAuth token validation metrics
- `mcp_vector_sync_*` - Vector database sync metrics
- `mcp_db_*` - Database operation metrics
File diff suppressed because it is too large Load Diff
@@ -69,57 +69,6 @@ Your Nextcloud MCP Server has been deployed in {{ .Values.auth.mode }} authentic
{{- end }}
{{- end }}
{{- if .Values.vectorSync.enabled }}
5. Vector Search & Semantic Capabilities:
- Vector Sync: Enabled
- Scan Interval: {{ .Values.vectorSync.scanInterval }}s
- Processor Workers: {{ .Values.vectorSync.processorWorkers }}
{{- if .Values.qdrant.enabled }}
- Qdrant: Deployed as subchart ({{ .Release.Name }}-qdrant:6333)
{{- else }}
- Qdrant: Not deployed (configure external instance)
{{- end }}
{{- if .Values.ollama.enabled }}
- Ollama: Deployed as subchart ({{ .Release.Name }}-ollama:11434)
- Embedding Model: {{ .Values.ollama.embeddingModel }}
{{- else if .Values.ollama.url }}
- Ollama: Using external instance at {{ .Values.ollama.url }}
- Embedding Model: {{ .Values.ollama.embeddingModel }}
{{- else if .Values.openai.enabled }}
- OpenAI: Enabled for embeddings
{{- else }}
- WARNING: No embedding provider configured (Ollama or OpenAI required)
{{- end }}
Check vector sync status:
kubectl --namespace {{ .Release.Namespace }} exec -it deploy/{{ include "nextcloud-mcp-server.fullname" . }} -- curl -s http://localhost:{{ include "nextcloud-mcp-server.port" . }}/user/page | grep "Vector Sync"
{{- end }}
{{- if .Values.dashboards.enabled }}
6. Grafana Dashboards:
- Dashboard provisioning: Enabled
- ConfigMap: {{ include "nextcloud-mcp-server.fullname" . }}-dashboard
- Grafana Folder: {{ .Values.dashboards.grafanaFolder }}
The dashboard will be automatically imported by Grafana if the sidecar is configured
to watch for ConfigMaps with label "grafana_dashboard: 1".
To manually import the dashboard:
kubectl --namespace {{ .Release.Namespace }} get configmap {{ include "nextcloud-mcp-server.fullname" . }}-dashboard -o jsonpath='{.data.nextcloud-mcp-server\.json}' | jq . > dashboard.json
Then import dashboard.json via Grafana UI (Dashboards → Import).
{{- else }}
6. Grafana Dashboards:
- Dashboard provisioning: Disabled
- To enable automatic dashboard provisioning, set: dashboards.enabled=true
Manual import option:
The dashboard JSON is available in the chart at charts/nextcloud-mcp-server/dashboards/nextcloud-mcp-server.json
{{- end }}
For more information and documentation:
- GitHub: https://github.com/cbcoutinho/nextcloud-mcp-server
- Documentation: https://github.com/cbcoutinho/nextcloud-mcp-server#readme
@@ -95,28 +95,21 @@ Create the name of the PVC to use for OAuth storage
{{- end }}
{{/*
Create the name of the PVC to use for Qdrant local persistent storage
*/}}
{{- define "nextcloud-mcp-server.qdrantPvcName" -}}
{{- if .Values.qdrant.localPersistence.existingClaim }}
{{- .Values.qdrant.localPersistence.existingClaim }}
{{- else }}
{{- include "nextcloud-mcp-server.fullname" . }}-qdrant-data
{{- end }}
{{- end }}
{{/*
Return the MCP server port
Return the appropriate MCP server port based on auth mode
*/}}
{{- define "nextcloud-mcp-server.port" -}}
{{- if eq .Values.auth.mode "oauth" }}
{{- .Values.auth.oauth.port }}
{{- else }}
{{- .Values.mcp.port }}
{{- end }}
{{- end }}
{{/*
Return the image tag (always uses chart appVersion)
Return the image tag
*/}}
{{- define "nextcloud-mcp-server.imageTag" -}}
{{- .Chart.AppVersion }}
{{- .Values.image.tag | default .Chart.AppVersion }}
{{- end }}
{{/*
@@ -1,25 +0,0 @@
{{- if .Values.dashboards.enabled }}
apiVersion: v1
kind: ConfigMap
metadata:
name: {{ include "nextcloud-mcp-server.fullname" . }}-dashboard
namespace: {{ .Release.Namespace }}
labels:
{{- include "nextcloud-mcp-server.labels" . | nindent 4 }}
{{- with .Values.dashboards.labels }}
{{- toYaml . | nindent 4 }}
{{- end }}
# Grafana sidecar discovery label
grafana_dashboard: "1"
annotations:
{{- with .Values.dashboards.annotations }}
{{- toYaml . | nindent 4 }}
{{- end }}
# Grafana folder name (annotations support spaces, unlike labels)
{{- if .Values.dashboards.grafanaFolder }}
grafana_folder: {{ .Values.dashboards.grafanaFolder | quote }}
{{- end }}
data:
nextcloud-mcp-server.json: |-
{{ .Files.Get "dashboards/nextcloud-mcp-server.json" | indent 4 }}
{{- end }}
@@ -5,8 +5,6 @@ metadata:
labels:
{{- include "nextcloud-mcp-server.labels" . | nindent 4 }}
spec:
strategy:
type: Recreate
{{- if not .Values.autoscaling.enabled }}
replicas: {{ .Values.replicaCount }}
{{- end }}
@@ -48,21 +46,15 @@ spec:
- "{{ .Values.mcp.transport }}"
{{- if eq .Values.auth.mode "oauth" }}
- "--oauth"
- "--port"
- "{{ .Values.auth.oauth.port }}"
- "--oauth-token-type"
- "{{ .Values.auth.oauth.tokenType }}"
{{- end }}
{{- with .Values.mcp.extraArgs }}
{{- toYaml . | nindent 12 }}
{{- end }}
ports:
- name: http
containerPort: {{ include "nextcloud-mcp-server.port" . }}
protocol: TCP
{{- if .Values.observability.metrics.enabled }}
- name: metrics
containerPort: {{ .Values.observability.metrics.port }}
protocol: TCP
{{- end }}
env:
# Nextcloud connection
- name: NEXTCLOUD_HOST
@@ -85,6 +77,8 @@ spec:
value: {{ include "nextcloud-mcp-server.mcpServerUrl" . | quote }}
- name: NEXTCLOUD_PUBLIC_ISSUER_URL
value: {{ include "nextcloud-mcp-server.publicIssuerUrl" . | quote }}
- name: NEXTCLOUD_OIDC_CLIENT_STORAGE
value: "/app/.oauth/nextcloud_oauth_client.json"
- name: NEXTCLOUD_OIDC_SCOPES
value: {{ .Values.auth.oauth.scopes | quote }}
{{- if .Values.auth.oauth.clientId }}
@@ -147,90 +141,6 @@ spec:
value: {{ .Values.documentProcessing.custom.types | quote }}
{{- end }}
{{- end }}
# Vector Sync
- name: VECTOR_SYNC_ENABLED
value: {{ .Values.vectorSync.enabled | quote }}
{{- if .Values.vectorSync.enabled }}
- name: VECTOR_SYNC_SCAN_INTERVAL
value: {{ .Values.vectorSync.scanInterval | quote }}
- name: VECTOR_SYNC_PROCESSOR_WORKERS
value: {{ .Values.vectorSync.processorWorkers | quote }}
- name: VECTOR_SYNC_QUEUE_MAX_SIZE
value: {{ .Values.vectorSync.queueMaxSize | quote }}
{{- end }}
# Document Chunking (always set, used by vector sync processor)
- name: DOCUMENT_CHUNK_SIZE
value: {{ .Values.documentChunking.chunkSize | quote }}
- name: DOCUMENT_CHUNK_OVERLAP
value: {{ .Values.documentChunking.chunkOverlap | quote }}
# Qdrant Vector Database
{{- if eq .Values.qdrant.mode "network" }}
# Network mode: Use dedicated Qdrant service
{{- if .Values.qdrant.networkMode.deploySubchart }}
- name: QDRANT_URL
value: "http://{{ .Release.Name }}-qdrant:6333"
{{- else if .Values.qdrant.networkMode.externalUrl }}
- name: QDRANT_URL
value: {{ .Values.qdrant.networkMode.externalUrl | quote }}
{{- end }}
{{- if or .Values.qdrant.networkMode.apiKey .Values.qdrant.networkMode.existingSecret }}
- name: QDRANT_API_KEY
valueFrom:
secretKeyRef:
name: {{ .Values.qdrant.networkMode.existingSecret | default (printf "%s-qdrant" .Release.Name) }}
key: {{ .Values.qdrant.networkMode.secretKey }}
{{- end }}
{{- else if eq .Values.qdrant.mode "persistent" }}
# Persistent local mode: File-based storage
- name: QDRANT_LOCATION
value: {{ .Values.qdrant.localPersistence.dataPath | quote }}
{{- else }}
# In-memory mode (default): Ephemeral storage
- name: QDRANT_LOCATION
value: ":memory:"
{{- end }}
- name: QDRANT_COLLECTION
value: {{ .Values.qdrant.collection | quote }}
# Ollama Embedding Service
{{- if or .Values.ollama.enabled .Values.ollama.url }}
- name: OLLAMA_BASE_URL
value: {{ .Values.ollama.url | default (printf "http://%s-ollama:11434" .Release.Name) | quote }}
- name: OLLAMA_EMBEDDING_MODEL
value: {{ .Values.ollama.embeddingModel | quote }}
- name: OLLAMA_VERIFY_SSL
value: {{ .Values.ollama.verifySsl | quote }}
{{- end }}
# OpenAI Embedding Provider (alternative to Ollama)
{{- if .Values.openai.enabled }}
- name: OPENAI_API_KEY
valueFrom:
secretKeyRef:
name: {{ .Values.openai.existingSecret | default (printf "%s-openai" (include "nextcloud-mcp-server.fullname" .)) }}
key: {{ .Values.openai.secretKey }}
{{- if .Values.openai.baseUrl }}
- name: OPENAI_BASE_URL
value: {{ .Values.openai.baseUrl | quote }}
{{- end }}
{{- end }}
# Observability
- name: METRICS_ENABLED
value: {{ .Values.observability.metrics.enabled | quote }}
- name: METRICS_PORT
value: {{ .Values.observability.metrics.port | quote }}
{{- if .Values.observability.tracing.enabled }}
- name: OTEL_EXPORTER_OTLP_ENDPOINT
value: {{ .Values.observability.tracing.endpoint | quote }}
- name: OTEL_SERVICE_NAME
value: {{ .Values.observability.tracing.serviceName | quote }}
- name: OTEL_TRACES_SAMPLER_ARG
value: {{ .Values.observability.tracing.samplingRate | quote }}
{{- end }}
- name: LOG_FORMAT
value: {{ .Values.observability.logging.format | quote }}
- name: LOG_LEVEL
value: {{ .Values.observability.logging.level | quote }}
- name: LOG_INCLUDE_TRACE_CONTEXT
value: {{ .Values.observability.logging.includeTraceContext | quote }}
{{- with .Values.extraEnv }}
{{- toYaml . | nindent 12 }}
{{- end }}
@@ -251,10 +161,6 @@ spec:
- name: oauth-storage
mountPath: /app/.oauth
{{- end }}
{{- if and (eq .Values.qdrant.mode "persistent") .Values.qdrant.localPersistence.enabled }}
- name: qdrant-data
mountPath: /app/data
{{- end }}
{{- with .Values.volumeMounts }}
{{- toYaml . | nindent 12 }}
{{- end }}
@@ -266,11 +172,6 @@ spec:
persistentVolumeClaim:
claimName: {{ include "nextcloud-mcp-server.oauthPvcName" . }}
{{- end }}
{{- if and (eq .Values.qdrant.mode "persistent") .Values.qdrant.localPersistence.enabled }}
- name: qdrant-data
persistentVolumeClaim:
claimName: {{ include "nextcloud-mcp-server.qdrantPvcName" . }}
{{- end }}
{{- with .Values.volumes }}
{{- toYaml . | nindent 8 }}
{{- end }}
@@ -1,11 +0,0 @@
{{- if and .Values.openai.enabled (not .Values.openai.existingSecret) }}
apiVersion: v1
kind: Secret
metadata:
name: {{ include "nextcloud-mcp-server.fullname" . }}-openai
labels:
{{- include "nextcloud-mcp-server.labels" . | nindent 4 }}
type: Opaque
data:
{{ .Values.openai.secretKey }}: {{ .Values.openai.apiKey | b64enc | quote }}
{{- end }}
@@ -1,92 +0,0 @@
{{- if and .Values.observability.metrics.enabled .Values.prometheusRule.enabled }}
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
name: {{ include "nextcloud-mcp-server.fullname" . }}
namespace: {{ .Release.Namespace }}
labels:
{{- include "nextcloud-mcp-server.labels" . | nindent 4 }}
{{- with .Values.prometheusRule.labels }}
{{- toYaml . | nindent 4 }}
{{- end }}
spec:
groups:
- name: nextcloud-mcp-server.critical
interval: 30s
rules:
- alert: NextcloudMCPServerDown
expr: up{job="{{ include "nextcloud-mcp-server.fullname" . }}"} == 0
for: 5m
labels:
severity: critical
annotations:
summary: "Nextcloud MCP Server is down"
description: "{{ `{{` }} $labels.pod {{ `}}` }} has been down for more than 5 minutes."
- alert: NextcloudMCPHighErrorRate
expr: |
sum(rate(mcp_http_requests_total{status_code=~"5..", job="{{ include "nextcloud-mcp-server.fullname" . }}"}[5m]))
/ sum(rate(mcp_http_requests_total{job="{{ include "nextcloud-mcp-server.fullname" . }}"}[5m])) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High error rate on Nextcloud MCP Server"
description: "Error rate is {{ `{{` }} printf \"%.2f%%\" (mul $value 100) {{ `}}` }} (threshold: 5%)"
- alert: NextcloudMCPHighLatency
expr: |
histogram_quantile(0.95,
sum(rate(mcp_http_request_duration_seconds_bucket{job="{{ include "nextcloud-mcp-server.fullname" . }}"}[5m])) by (le, endpoint)
) > 1
for: 5m
labels:
severity: critical
annotations:
summary: "High latency on Nextcloud MCP Server"
description: "P95 latency is {{ `{{` }} printf \"%.2fs\" $value {{ `}}` }} on {{ `{{` }} $labels.endpoint {{ `}}` }} (threshold: 1s)"
- alert: NextcloudMCPDependencyDown
expr: mcp_dependency_health{job="{{ include "nextcloud-mcp-server.fullname" . }}"} == 0
for: 2m
labels:
severity: critical
annotations:
summary: "Nextcloud MCP dependency is down"
description: "Dependency {{ `{{` }} $labels.dependency {{ `}}` }} has been down for more than 2 minutes."
- name: nextcloud-mcp-server.warning
interval: 30s
rules:
- alert: NextcloudMCPTokenValidationErrors
expr: |
sum(rate(mcp_oauth_token_validations_total{result="error", job="{{ include "nextcloud-mcp-server.fullname" . }}"}[10m]))
/ sum(rate(mcp_oauth_token_validations_total{job="{{ include "nextcloud-mcp-server.fullname" . }}"}[10m])) > 0.01
for: 10m
labels:
severity: warning
annotations:
summary: "High token validation error rate"
description: "Token validation error rate is {{ `{{` }} printf \"%.2f%%\" (mul $value 100) {{ `}}` }} (threshold: 1%)"
- alert: NextcloudMCPVectorSyncQueueHigh
expr: mcp_vector_sync_queue_size{job="{{ include "nextcloud-mcp-server.fullname" . }}"} > 100
for: 15m
labels:
severity: warning
annotations:
summary: "Vector sync queue is high"
description: "Vector sync queue size is {{ `{{` }} $value {{ `}}` }} (threshold: 100)"
- alert: NextcloudMCPQdrantSlowQueries
expr: |
histogram_quantile(0.95,
sum(rate(mcp_db_operation_duration_seconds_bucket{db="qdrant", job="{{ include "nextcloud-mcp-server.fullname" . }}"}[10m])) by (le)
) > 0.5
for: 10m
labels:
severity: warning
annotations:
summary: "Qdrant queries are slow"
description: "P95 Qdrant query latency is {{ `{{` }} printf \"%.2fs\" $value {{ `}}` }} (threshold: 0.5s)"
{{- end }}
@@ -15,21 +15,3 @@ spec:
requests:
storage: {{ .Values.auth.oauth.persistence.size }}
{{- end }}
---
{{- if and (eq .Values.qdrant.mode "persistent") .Values.qdrant.localPersistence.enabled (not .Values.qdrant.localPersistence.existingClaim) }}
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: {{ include "nextcloud-mcp-server.fullname" . }}-qdrant-data
labels:
{{- include "nextcloud-mcp-server.labels" . | nindent 4 }}
spec:
accessModes:
- {{ .Values.qdrant.localPersistence.accessMode }}
{{- if .Values.qdrant.localPersistence.storageClass }}
storageClassName: {{ .Values.qdrant.localPersistence.storageClass }}
{{- end }}
resources:
requests:
storage: {{ .Values.qdrant.localPersistence.size }}
{{- end }}
@@ -15,11 +15,5 @@ spec:
targetPort: http
protocol: TCP
name: http
{{- if .Values.observability.metrics.enabled }}
- port: {{ .Values.observability.metrics.port }}
targetPort: metrics
protocol: TCP
name: metrics
{{- end }}
selector:
{{- include "nextcloud-mcp-server.selectorLabels" . | nindent 4 }}
@@ -1,32 +0,0 @@
{{- if and .Values.observability.metrics.enabled .Values.serviceMonitor.enabled }}
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: {{ include "nextcloud-mcp-server.fullname" . }}
namespace: {{ .Release.Namespace }}
labels:
{{- include "nextcloud-mcp-server.labels" . | nindent 4 }}
{{- with .Values.serviceMonitor.labels }}
{{- toYaml . | nindent 4 }}
{{- end }}
spec:
selector:
matchLabels:
{{- include "nextcloud-mcp-server.selectorLabels" . | nindent 6 }}
endpoints:
- port: metrics
path: {{ .Values.observability.metrics.path }}
interval: {{ .Values.serviceMonitor.interval }}
scrapeTimeout: {{ .Values.serviceMonitor.scrapeTimeout }}
scheme: http
relabelings:
# Add namespace label
- sourceLabels: [__meta_kubernetes_namespace]
targetLabel: namespace
# Add pod label
- sourceLabels: [__meta_kubernetes_pod_name]
targetLabel: pod
# Add service label
- sourceLabels: [__meta_kubernetes_service_name]
targetLabel: service
{{- end }}
+7 -204
View File
@@ -8,7 +8,8 @@ replicaCount: 1
image:
repository: ghcr.io/cbcoutinho/nextcloud-mcp-server
pullPolicy: IfNotPresent
# Image tag is automatically set to chart appVersion
# Overrides the image tag whose default is the chart appVersion.
tag: ""
imagePullSecrets: []
nameOverride: ""
@@ -60,6 +61,8 @@ auth:
# OAuth2/OIDC settings (experimental)
oauth:
# Port for OAuth MCP server (default: 8001)
port: 8001
# OAuth token type: "jwt" or "opaque"
tokenType: "jwt"
# Pre-registered OAuth client ID (optional, ignored if existingSecret is set)
@@ -94,11 +97,8 @@ auth:
mcp:
# Transport mode (default: streamable-http for SSE)
transport: "streamable-http"
# Port for MCP server (both basic auth and OAuth modes)
# Port for basic auth mode
port: 8000
# Additional command-line arguments to pass to nextcloud-mcp-server
# Example: ["--log-level", "debug", "--enable-app", "notes"]
extraArgs: []
# Document processing configuration (optional)
documentProcessing:
@@ -168,60 +168,11 @@ securityContext:
runAsNonRoot: true
runAsUser: 1000
# Observability Configuration
observability:
# Prometheus metrics
metrics:
enabled: true
port: 9090
path: /metrics
# OpenTelemetry tracing
tracing:
enabled: false
endpoint: "" # e.g., "http://opentelemetry-collector:4317"
serviceName: "nextcloud-mcp-server"
samplingRate: 1.0
# Logging configuration
logging:
format: json # "json" or "text"
level: INFO
includeTraceContext: true
# Prometheus ServiceMonitor (requires Prometheus Operator)
serviceMonitor:
enabled: false
interval: 30s
scrapeTimeout: 10s
labels: {}
# Additional labels for ServiceMonitor (e.g., for Prometheus selector)
# Example: { prometheus: kube-prometheus }
# Prometheus alert rules (requires Prometheus Operator)
prometheusRule:
enabled: false
labels: {}
# Additional labels for PrometheusRule (e.g., for Prometheus selector)
# Example: { prometheus: kube-prometheus }
# Grafana dashboards (requires Grafana with sidecar enabled)
dashboards:
# Enable automatic dashboard provisioning via ConfigMap
enabled: false
# Grafana folder name where dashboards will be imported
# The grafana-sidecar looks for ConfigMaps with label "grafana_dashboard: 1"
# and reads the folder name from annotation "grafana_folder" (supports spaces)
grafanaFolder: "Nextcloud MCP"
# Additional labels for dashboard ConfigMap
# These will be added alongside the required "grafana_dashboard: 1" label
labels: {}
# Additional annotations for dashboard ConfigMap
annotations: {}
service:
type: ClusterIP
port: 8000
# For OAuth mode, you may want to expose both ports
oauthPort: 8001
annotations: {}
ingress:
@@ -315,151 +266,3 @@ extraEnvFrom: []
# name: my-configmap
# - secretRef:
# name: my-secret
# Vector Sync Configuration
# Background synchronization of Nextcloud content into vector database for semantic search
vectorSync:
# Enable background vector synchronization
enabled: false
# Scan interval in seconds (how often to check for changes)
scanInterval: 3600
# Number of concurrent processor workers
processorWorkers: 3
# Maximum queue size for documents pending indexing
queueMaxSize: 10000
# Document Chunking Configuration
# Controls how documents are split into chunks before embedding
# Only relevant when vectorSync.enabled is true
documentChunking:
# Number of words per chunk (default: 512)
# Smaller chunks (256-384): Better for precise searches, more chunks to store
# Medium chunks (512-768): Balanced approach (recommended for most use cases)
# Larger chunks (1024+): Better for context, less precise matching
chunkSize: 512
# Number of overlapping words between chunks (default: 50)
# Recommended: 10-20% of chunkSize for context preservation across boundaries
# Must be less than chunkSize
chunkOverlap: 50
# Qdrant Vector Database Configuration
# Three deployment modes available:
# 1. Local In-Memory: Fast, ephemeral, zero-config (mode: "memory")
# 2. Local Persistent: File-based, survives restarts (mode: "persistent")
# 3. Network: Dedicated Qdrant service, production-ready (mode: "network")
qdrant:
# Qdrant mode: "memory", "persistent", or "network"
# - memory: In-memory storage (:memory:) - default, zero config, data lost on restart
# - persistent: Local file storage - data persists across restarts, suitable for small/medium deployments
# - network: Dedicated Qdrant service (see networkMode below)
mode: "memory"
# Collection name for vector data
collection: "nextcloud_content"
# Local persistent mode configuration (only used when mode: "persistent")
localPersistence:
# Enable persistent volume for local Qdrant data
enabled: true
# Storage class (leave empty for default)
storageClass: ""
accessMode: ReadWriteOnce
# Size for local Qdrant storage
size: 1Gi
# Path where Qdrant data is stored (relative to /app/data)
# Default: /app/data/qdrant
dataPath: "/app/data/qdrant"
# Use existing PVC
existingClaim: ""
# Network mode configuration (only used when mode: "network")
networkMode:
# Deploy Qdrant as a subchart (if true) or use external Qdrant (if false)
deploySubchart: false
# External Qdrant URL (used when deploySubchart: false)
# Example: "http://qdrant.default.svc.cluster.local:6333"
externalUrl: ""
# Optional API key for Qdrant authentication
apiKey: ""
# Use existing secret for API key
existingSecret: ""
secretKey: "api-key"
# Qdrant subchart configuration (only used when mode: "network" and networkMode.deploySubchart: true)
# All values are passed through to the qdrant/qdrant chart.
# See https://github.com/qdrant/qdrant-helm for full configuration options.
subchart:
# Number of Qdrant replicas
replicaCount: 1
image:
# Qdrant version
tag: v1.12.5
config:
cluster:
# Enable distributed cluster mode
enabled: false
# Persistent storage for vector data
persistence:
size: 10Gi
storageClass: ""
accessModes:
- ReadWriteOnce
# Resource limits and requests
resources:
requests:
cpu: 200m
memory: 512Mi
limits:
cpu: 1000m
memory: 2Gi
# Ollama Embedding Service
# Deployed as a subchart when enabled. All values are passed through to the ollama/ollama chart.
# See https://github.com/otwld/ollama-helm for full configuration options.
ollama:
# Enable Ollama subchart deployment
# Set to true to deploy Ollama as a subchart, or false to use an external Ollama instance
enabled: false
# External Ollama URL (use this if you have Ollama deployed elsewhere)
# When set, use enabled: false to prevent deploying the subchart
# Example: "http://ollama.default.svc.cluster.local:11434"
url: ""
# Embedding model to use
embeddingModel: "nomic-embed-text"
# Verify SSL certificates when connecting to Ollama
verifySsl: true
# Number of Ollama replicas (only used when subchart is deployed)
replicaCount: 1
# Ollama configuration (only used when subchart is deployed)
ollama:
# Models to automatically pull on startup
models:
pull:
- nomic-embed-text
# Persistent storage for models (only used when subchart is deployed)
persistentVolume:
enabled: true
size: 20Gi
storageClass: ""
# Resource limits and requests (only used when subchart is deployed)
resources:
requests:
cpu: 500m
memory: 1Gi
limits:
cpu: 2000m
memory: 4Gi
# OpenAI-compatible Embedding Provider
# Alternative to Ollama for embedding generation. Can be used with OpenAI or any compatible API.
openai:
# Enable OpenAI embedding provider
enabled: false
# OpenAI API key (only used if existingSecret is not set)
apiKey: ""
# Name of existing secret containing the API key
existingSecret: ""
# Key in the secret that contains the API key
secretKey: "api-key"
# Optional custom API endpoint (e.g., for Azure OpenAI or local compatible services)
baseUrl: ""
+13 -176
View File
@@ -3,7 +3,7 @@ services:
# https://hub.docker.com/_/mariadb
db:
# Note: Check the recommend version here: https://docs.nextcloud.com/server/latest/admin_manual/installation/system_requirements.html#server
image: docker.io/library/mariadb:lts@sha256:6b848cb24fbbd87429917f6c4422ac53c343e85692eb0fef86553e99e4f422f3
image: docker.io/library/mariadb:lts@sha256:ae6119716edac6998ae85508431b3d2e666530ddf4e94c61a10710caec9b0f71
restart: always
command: --transaction-isolation=READ-COMMITTED
volumes:
@@ -17,24 +17,23 @@ services:
# Note: Redis is an external service. You can find more information about the configuration here:
# https://hub.docker.com/_/redis
redis:
image: docker.io/library/redis:alpine@sha256:28c9c4d7596949a24b183eaaab6455f8e5d55ecbf72d02ff5e2c17fe72671d31
image: docker.io/library/redis:alpine@sha256:59b6e694653476de2c992937ebe1c64182af4728e54bb49e9b7a6c26614d8933
restart: always
app:
image: docker.io/library/nextcloud:32.0.1@sha256:5b043f7ea2f609d5ff5635f475c30d303bec17775a5c3f7fa435e3818e669120
image: docker.io/library/nextcloud:32.0.1@sha256:1e4eae55eebe094cae6f9e7b6e0b4bccf4a4fe7b7e6f6f8f57010994b3b2ee42
restart: always
ports:
- 0.0.0.0:8080:80
depends_on:
- redis
- db
- keycloak
volumes:
- nextcloud:/var/www/html
- ./app-hooks:/docker-entrypoint-hooks.d:ro
- ./app-hooks/post-installation:/docker-entrypoint-hooks.d/post-installation:ro
# Mount OIDC development directory outside /var/www/html to avoid rsync conflicts
# The post-installation hook will register /opt/apps as an additional app directory
#- ./third_party:/opt/apps:ro
- ./third_party/oidc:/opt/apps/oidc:ro
environment:
- NEXTCLOUD_TRUSTED_DOMAINS=app
- NEXTCLOUD_ADMIN_USER=admin
@@ -44,21 +43,16 @@ services:
- MYSQL_USER=nextcloud
- MYSQL_HOST=db
- REDIS_HOST=redis
healthcheck:
test: ["CMD-SHELL", "curl -Ss http://localhost/status.php | grep '\"installed\":true' || exit 1"]
interval: 10s
timeout: 30s
retries: 30
recipes:
image: docker.io/library/nginx:alpine@sha256:b3c656d55d7ad751196f21b7fd2e8d4da9cb430e32f646adcf92441b72f82b14
image: docker.io/library/nginx:alpine@sha256:9dacca6749f2215cc3094f641c5b6662f7791e66a57ed034e806a7c48d51c18f
restart: always
volumes:
- ./tests/fixtures/test_recipe.html:/usr/share/nginx/html/test_recipe.html:ro
- ./tests/fixtures/nginx.conf:/etc/nginx/nginx.conf:ro
unstructured:
image: downloads.unstructured.io/unstructured-io/unstructured-api:latest@sha256:54282d3a25f33fd6cf69bc45b3d37770f213593f58b6dfe5e85fe546376b2807
image: downloads.unstructured.io/unstructured-io/unstructured-api:latest@sha256:a43ab55898599157fb0e0e097dabb8ecdd1d8e3df1ae5b67c6e15a136b171a6c
restart: always
ports:
- 127.0.0.1:8002:8000
@@ -69,195 +63,38 @@ services:
mcp:
build: .
command: ["--transport", "streamable-http"]
restart: always
depends_on:
app:
condition: service_healthy
- app
ports:
- 127.0.0.1:8000:8000
volumes:
- mcp-data:/app/data
environment:
- NEXTCLOUD_HOST=http://app:80
- NEXTCLOUD_USERNAME=admin
- NEXTCLOUD_PASSWORD=admin
- NEXTCLOUD_PUBLIC_ISSUER_URL=http://localhost:8080
# Vector sync configuration (ADR-007)
- VECTOR_SYNC_ENABLED=true
- VECTOR_SYNC_SCAN_INTERVAL=10
- VECTOR_SYNC_PROCESSOR_WORKERS=1
#- LOG_FORMAT=json
# Qdrant configuration (three modes):
# 1. Network mode: Set QDRANT_URL=http://qdrant:6333 (requires qdrant service)
# 2. In-memory mode: Set QDRANT_LOCATION=:memory: (default if nothing set)
# 3. Persistent local: Set QDRANT_LOCATION=/app/data/qdrant (stored in mcp-data volume)
#- QDRANT_LOCATION=/app/data/qdrant # In-memory mode used if not set
#- QDRANT_URL=http://qdrant:6333 # Uncomment for network mode
#- QDRANT_API_KEY=${QDRANT_API_KEY:-my_secret_api_key} # Only for network mode
# Observability
#- OTEL_SERVICE_NAME=nextcloud-mcp-docker-compose
#- OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4317
# Collection naming: Auto-generated as {deployment-id}-{model-name}
# - Deployment ID: OTEL_SERVICE_NAME (if set) or hostname (fallback)
# - Model name: OLLAMA_EMBEDDING_MODEL
# - Example: "nextcloud-mcp-server-nomic-embed-text"
# - Changing models creates new collection (requires re-embedding)
# - Set QDRANT_COLLECTION to override auto-generation:
#- QDRANT_COLLECTION=nextcloud_content
# Ollama configuration (optional - uses SimpleEmbeddingProvider if not set)
# - OLLAMA_BASE_URL=http://ollama:11434
# - OLLAMA_EMBEDDING_MODEL=nomic-embed-text # Changing this creates new collection
# - OLLAMA_VERIFY_SSL=false
# Document chunking configuration (for vector embeddings)
# Tune these based on your embedding model and content type
# - DOCUMENT_CHUNK_SIZE=512 # Words per chunk (default: 512)
# - DOCUMENT_CHUNK_OVERLAP=50 # Overlapping words (default: 50, recommended: 10-20% of chunk size)
mcp-oauth:
build: .
command: ["--transport", "streamable-http", "--oauth", "--port", "8001", "--oauth-token-type", "jwt"]
restart: always
depends_on:
app:
condition: service_healthy
- app
ports:
- 127.0.0.1:8001:8001
environment:
# Generic OIDC configuration (integrated mode - Nextcloud OIDC app)
# OIDC_DISCOVERY_URL not set - defaults to NEXTCLOUD_HOST/.well-known/openid-configuration
# OIDC_CLIENT_ID not set - uses Dynamic Client Registration (DCR)
- NEXTCLOUD_HOST=http://app:80
- NEXTCLOUD_MCP_SERVER_URL=http://localhost:8001
- NEXTCLOUD_RESOURCE_URI=http://localhost:8080 # ADR-005: Nextcloud resource identifier for audience validation
- NEXTCLOUD_PUBLIC_ISSUER_URL=http://localhost:8080
- NEXTCLOUD_OIDC_CLIENT_STORAGE=/app/.oauth/nextcloud_oauth_client.json
- NEXTCLOUD_OIDC_SCOPES=openid profile email notes:read notes:write calendar:read calendar:write contacts:read contacts:write cookbook:read cookbook:write deck:read deck:write tables:read tables:write files:read files:write sharing:read sharing:write todo:read todo:write
# Refresh token storage (ADR-002 Tier 1)
- ENABLE_OFFLINE_ACCESS=true
- TOKEN_ENCRYPTION_KEY=ESF1BvEQdGYsCluwMx9Cxvw3uh5pFowPH7Rg_nIliyo=
- TOKEN_STORAGE_DB=/app/data/tokens.db
# ADR-005: Multi-audience mode (default - ENABLE_TOKEN_EXCHANGE=false)
# Tokens must contain BOTH MCP and Nextcloud audiences
# No token exchange needed - tokens work for both MCP auth and Nextcloud APIs
# NO admin credentials - using OAuth with Dynamic Client Registration (DCR)
# Client credentials registered via RFC 7591 and stored in volume
# No USERNAME/PASSWORD - will use OAuth with Dynamic Client Registration
# Client credentials will be registered and stored in volume on first startup
# JWT token type is used for testing (faster validation, scopes embedded in token)
volumes:
- oauth-client-storage:/app/.oauth
- oauth-tokens:/app/data
keycloak:
image: quay.io/keycloak/keycloak:26.4.5@sha256:653852bfdea2be6e958b9e90a976eff1c6de34edd55f2f679bdc48ef16bc528e
command:
- "start-dev"
- "--import-realm"
- "--hostname=http://localhost:8888"
- "--hostname-strict=false"
- "--hostname-backchannel-dynamic=true"
- "--features=preview" # Enable Legacy V1 token exchange (supports both Standard V2 and Legacy V1)
ports:
- 127.0.0.1:8888:8080
environment:
- KC_BOOTSTRAP_ADMIN_USERNAME=admin
- KC_BOOTSTRAP_ADMIN_PASSWORD=admin
volumes:
- ./keycloak/realm-export.json:/opt/keycloak/data/import/realm.json:ro
healthcheck:
test: ["CMD-SHELL", "exec 3<>/dev/tcp/localhost/8080 && echo -e 'GET /realms/nextcloud-mcp HTTP/1.1\\r\\nHost: localhost\\r\\nConnection: close\\r\\n\\r\\n' >&3 && cat <&3 | grep -q 'HTTP/1.1 200'"]
interval: 10s
timeout: 5s
retries: 30
mcp-keycloak:
build: .
command: ["--transport", "streamable-http", "--oauth", "--port", "8002"]
restart: always
depends_on:
keycloak:
condition: service_healthy
app:
condition: service_started
ports:
- 127.0.0.1:8002:8002
environment:
# Generic OIDC configuration (external IdP mode - Keycloak)
# Provider auto-detected from OIDC_DISCOVERY_URL issuer
# Using internal Docker hostname for discovery to get consistent issuer
- OIDC_DISCOVERY_URL=http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration
- OIDC_CLIENT_ID=nextcloud-mcp-server
- OIDC_CLIENT_SECRET=mcp-secret-change-in-production
- OIDC_JWKS_URI=http://keycloak:8080/realms/nextcloud-mcp/protocol/openid-connect/certs
# Nextcloud API endpoint (for accessing APIs with validated token)
- NEXTCLOUD_HOST=http://app:80
- NEXTCLOUD_MCP_SERVER_URL=http://localhost:8002
- NEXTCLOUD_RESOURCE_URI=nextcloud # ADR-005: Keycloak uses client IDs as audiences, not URLs
- NEXTCLOUD_PUBLIC_ISSUER_URL=http://localhost:8888/realms/nextcloud-mcp
# Refresh token storage (ADR-002 Tier 1 & 2)
- ENABLE_OFFLINE_ACCESS=true
- TOKEN_ENCRYPTION_KEY=ESF1BvEQdGYsCluwMx9Cxvw3uh5pFowPH7Rg_nIliyo=
- TOKEN_STORAGE_DB=/app/data/tokens.db
# ADR-005: Token exchange mode (RFC 8693)
# Exchange MCP tokens (aud: nextcloud-mcp-server) for Nextcloud tokens (aud: http://localhost:8080)
# Provides strict audience separation between MCP session and Nextcloud API access
- ENABLE_TOKEN_EXCHANGE=true
- TOKEN_EXCHANGE_CACHE_TTL=300 # Cache exchanged tokens for 5 minutes (default)
# OAuth scopes (optional - uses defaults if not specified)
- NEXTCLOUD_OIDC_SCOPES=openid profile email offline_access notes:read notes:write calendar:read calendar:write contacts:read contacts:write cookbook:read cookbook:write deck:read deck:write tables:read tables:write files:read files:write sharing:read sharing:write todo:read todo:write
# NO admin credentials - using external IdP OAuth only!
volumes:
- keycloak-tokens:/app/data
- keycloak-oauth-storage:/app/.oauth
qdrant:
image: qdrant/qdrant:v1.15.5@sha256:0fb8897412abc81d1c0430a899b9a81eb8328aa634e7242d1bc804c1fe8fe863
restart: always
ports:
- 127.0.0.1:6333:6333 # REST API
- 127.0.0.1:6334:6334 # gRPC (optional)
volumes:
- qdrant-data:/qdrant/storage
environment:
- QDRANT__SERVICE__API_KEY=${QDRANT_API_KEY:-my_secret_api_key}
healthcheck:
test: ["CMD-SHELL", "test -f /qdrant/.qdrant-initialized"]
interval: 10s
timeout: 5s
retries: 10
profiles:
- qdrant
open-webui:
image: ghcr.io/open-webui/open-webui:main
environment:
- OLLAMA_BASE_URL=https://ollama.internal.coutinho.io
ports:
- 127.0.0.1:3000:8080
volumes:
- open-webui:/app/backend/data
profiles:
- open-webui
volumes:
nextcloud:
db:
oauth-client-storage:
oauth-tokens:
keycloak-tokens:
keycloak-oauth-storage:
qdrant-data:
mcp-data:
open-webui:
-964
View File
@@ -1,964 +0,0 @@
# ADR-002: Vector Database Background Sync Authentication
> **⚠️ DEPRECATED**: This ADR has been superseded by [ADR-004: MCP Server as OAuth Client for Offline Access](./ADR-004-mcp-application-oauth.md).
>
> **Reason for Deprecation**: This ADR fundamentally misunderstood the MCP protocol's authentication architecture. The MCP server receives tokens from clients but cannot initiate OAuth flows or store refresh tokens, making the proposed solutions ineffective for true offline access. ADR-004 provides the correct architectural pattern where the MCP server acts as its own OAuth client.
## Status
~~Accepted - Tier 2 (Token Exchange with Delegation) Implemented~~
**Superseded by ADR-004** - The token exchange implementation exists but doesn't solve the offline access problem.
**Important**: Service account tokens (old Tier 1) have been rejected as they violate OAuth "act on-behalf-of" principles by creating Nextcloud user accounts for the MCP server.
## Context
To enable semantic search capabilities, the MCP server needs to index user content (notes, files, calendar events) into a vector database. This requires a background sync worker that:
1. **Runs independently** of user requests (periodic or continuous operation)
2. **Accesses multiple users' content** to build a comprehensive search index
3. **Respects user permissions** - only index content users have access to
4. **Operates in OAuth mode** - where the MCP server doesn't have traditional admin credentials
### Current OAuth Architecture
The MCP server currently operates in two authentication modes:
1. **BasicAuth Mode**: Uses username/password credentials (typically admin account)
2. **OAuth Mode**: Single OAuth client, multiple user tokens
- Users authenticate via OAuth flow
- Each request includes user's access token
- Server creates per-request `NextcloudClient` with user's bearer token
- No tokens are stored server-side
### The Challenge
Background workers need long-lived authentication to:
- Index content continuously/periodically
- Process multiple users' data in batch operations
- Operate when users are not actively making requests
However, in OAuth mode:
- User access tokens are ephemeral (exist only during request)
- MCP server doesn't store user credentials
- Admin credentials defeat the purpose of OAuth
We need an OAuth-native solution that maintains security while enabling background operations.
## Decision
We will implement a **tiered OAuth authentication strategy** for background operations in OAuth mode. When OAuth authentication is not configured or available, the background sync feature is not available.
**Note**: This ADR applies only to **OAuth mode**. In BasicAuth mode (single-user deployments), credentials are already available via environment variables, and background operations work without additional configuration.
### OAuth "Act On-Behalf-Of" Principle
**Core Requirement**: The MCP server must NEVER create its own user identity in Nextcloud when operating in OAuth mode.
**Valid Patterns**:
-**Foreground operations**: Use user's access token from MCP request (currently implemented)
-**Background operations**: Token exchange to impersonate/delegate as user (requires provider support)
-**Service account**: Creates independent identity in Nextcloud (violates OAuth principles)
**Why This Matters**:
1. **Audit Trail**: All operations must be attributable to the actual user, not a service account
2. **Stateless Server**: MCP server should not have persistent identity/state in Nextcloud
3. **Security Model**: Avoid creating "admin by another name" with broad cross-user permissions
4. **OAuth Design**: OAuth tokens represent user authorization, not server authorization
**If Token Exchange Not Available**:
- Background operations simply cannot happen in OAuth mode
- This is correct behavior - not a limitation to work around
- Don't create service accounts as "workaround" - this defeats OAuth's purpose
- Use BasicAuth mode if background operations are critical to your deployment
### Tier 1: Token Exchange with Impersonation (RFC 8693) ⚠️ **NOT IMPLEMENTED**
**Better Security** - Requires provider support for user impersonation
- Service account exchanges token to impersonate specific users
- Each background operation runs as the target user
- Uses `requested_subject` parameter in token exchange
- Per-user permission enforcement at API level
**Requirements**:
- OIDC provider supports RFC 8693 token exchange
- Provider supports user impersonation (rare - requires Legacy Keycloak V1 with preview features)
- Service account has impersonation permissions
**Status**: ⚠️ Not implemented - Keycloak Standard V2 doesn't support impersonation
**Reference**: See `docs/oauth-impersonation-findings.md` for investigation details
### Tier 2: Token Exchange with Delegation (RFC 8693) ✅ **IMPLEMENTED**
**Best Security** - Requires provider support for delegation with `act` claim
- Service account exchanges token on behalf of users (delegation, not impersonation)
- Token includes `act` claim showing service account as actor
- API sees both the user (`sub`) and actor (`act`) in token
- Full audit trail of delegated operations
- **Implementation**: `KeycloakOAuthClient.exchange_token_for_user()` (keycloak_oauth.py:397-495)
- **Testing**: Manual test in `tests/manual/test_token_exchange.py`
- **Limitation**: Keycloak doesn't support `act` claim yet - [Issue #38279](https://github.com/keycloak/keycloak/issues/38279)
**Requirements**:
- OIDC provider supports RFC 8693 token exchange
- Provider supports delegation with `act` claim (very rare)
- Proper token exchange permissions configured
**Current Implementation**: Internal-to-internal token exchange with audience modification (without `act` claim)
### ❌ Will Not Implement
**1. Service Account with Independent Identity (client_credentials)**
- **Status**: Previously proposed as Tier 1, now rejected
- **Why Invalid**: Creates Nextcloud user account for MCP server (e.g., `service-account-nextcloud-mcp-server`)
- **Problems**:
- **Violates OAuth "act on-behalf-of" principle**: Actions attributed to service account instead of real user
- **Breaks audit trail**: Can't determine which user initiated the action
- **Creates stateful server identity**: MCP server has persistent identity/data in Nextcloud
- **Security risk**: Service account becomes "admin by another name" with broad cross-user permissions
- **User provisioning side effect**: Nextcloud's `user_oidc` app auto-provisions service account as real user
- **Code Status**: Implementation exists (`KeycloakOAuthClient.get_service_account_token()`) but marked with warnings
- **Alternative**: If service account pattern truly needed, use BasicAuth mode instead of OAuth mode
- **Reference**: See commit c12df98 for detailed analysis of why this approach was rejected
**2. Offline Access with Refresh Tokens**
- **MCP Protocol Architecture**: FastMCP SDK manages OAuth where MCP Client handles refresh tokens
- **Security Model**: Refresh tokens must never be shared between client and server (OAuth best practice)
- **Technical Impossibility**: MCP Server has no access to refresh tokens from the OAuth callback
- **Alternative**: Token exchange provides similar benefits without violating OAuth security model
**3. Admin Credentials Fallback**
- **Out of Scope**: This ADR focuses on OAuth mode only
- **Not Appropriate**: Admin credentials bypass OAuth security model
- **BasicAuth Mode**: For single-user deployments needing background operations, use BasicAuth mode instead
### Key Architectural Principles
1. **Capability Detection**: Automatically detect which OAuth methods are supported
2. **Dual-Phase Authorization**:
- Sync worker indexes with service credentials
- User requests verify access with user's OAuth token
3. **Defense in Depth**: Vector database is search accelerator, not security boundary
4. **Separation of Concerns**: Sync credentials ≠ Request credentials
## Implementation Details
### 1. Token Exchange with Impersonation (Tier 1) ✅ IMPLEMENTED (Legacy V1 only)
**Status**: Implemented and working with Keycloak Legacy V1 (`--features=preview`). Requires additional permission configuration. Recommended for advanced use cases only.
**When to Use**: When you need the exchanged token to have the exact same identity as the target user (sub claim changes). This provides the cleanest separation but requires preview features.
#### 1.1 Impersonation Flow
```python
async def exchange_token_for_user(
subject_token: str,
target_user_id: str,
audience: str | None = None,
scopes: list[str] | None = None,
) -> dict:
"""Exchange service token to impersonate specific user.
Requires Keycloak Legacy V1 (--features=preview) and impersonation permissions.
The returned token will have the target_user_id as the 'sub' claim.
"""
data = {
"grant_type": "urn:ietf:params:oauth:grant-type:token-exchange",
"subject_token": subject_token,
"subject_token_type": "urn:ietf:params:oauth:token-type:access_token",
"requested_token_type": "urn:ietf:params:oauth:token-type:access_token",
"requested_subject": target_user_id, # ← KEY: Impersonate this user
}
if audience:
data["audience"] = audience
if scopes:
data["scope"] = " ".join(scopes)
response = await self._http_client.post(
self.token_endpoint,
data=data,
auth=(self.client_id, self.client_secret),
)
response.raise_for_status()
return response.json()
```
**Implementation Requirements**:
- ✅ Keycloak Legacy V1 with `--features=preview` flag
- ✅ Impersonation role granted to service account (see configuration below)
- ❌ NOT supported in Keycloak Standard V2 (rejects `requested_subject` parameter)
- ⚠️ Very few OIDC providers support user impersonation via token exchange
**Empirical Testing (2025-11-02)**:
Tested impersonation with `requested_subject` parameter against Keycloak 26.4.2:
**Test Command**: `uv run python tests/manual/test_impersonation.py`
**Keycloak Standard V2 Result**:
```
HTTP/1.1 400 Bad Request
{
"error": "invalid_request",
"error_description": "Parameter 'requested_subject' is not supported for standard token exchange"
}
```
**Confirmation**: Keycloak explicitly rejects `requested_subject` in Standard V2, confirming this feature is unsupported. The error message is unambiguous - this parameter is not available in the current production token exchange implementation.
**Keycloak Legacy V1 Result - Initial Test** (with `--features=preview`):
```
HTTP/1.1 403 Forbidden
{
"error": "access_denied",
"error_description": "Client not allowed to exchange"
}
Keycloak logs:
reason="subject not allowed to impersonate"
impersonator="service-account-nextcloud-mcp-server"
requested_subject="admin"
```
**Analysis**: Legacy V1 **accepts** the `requested_subject` parameter (error changed from "not supported" to "not allowed"), indicating the feature is present but requires permission configuration.
**Configuration Steps to Enable Impersonation**:
1. **Enable Keycloak preview features** (in docker-compose.yml):
```yaml
command:
- "start-dev"
- "--features=preview" # Required for Legacy V1 token exchange
```
2. **Grant impersonation role to service account** (using Keycloak CLI):
```bash
docker compose exec keycloak /opt/keycloak/bin/kcadm.sh config credentials \
--server http://localhost:8080 \
--realm master \
--user admin \
--password admin
docker compose exec keycloak /opt/keycloak/bin/kcadm.sh add-roles \
-r nextcloud-mcp \
--uusername service-account-nextcloud-mcp-server \
--cclientid realm-management \
--rolename impersonation
```
**Keycloak Legacy V1 Result - After Permission Grant**:
```
✅ Token exchange with impersonation SUCCEEDED!
📊 Response details:
Issued token type: urn:ietf:params:oauth:token-type:access_token
Token type: Bearer
Expires in: 300s
📋 Token claims analysis:
Subject (sub): 47c3ba5a-9104-45e0-b84e-0e39ab942c9c (admin user)
Preferred username: admin
Client ID (azp): nextcloud-mcp-server
✅ IMPERSONATION VERIFIED:
Original sub: service-account-nextcloud-mcp-server
New sub: 47c3ba5a-9104-45e0-b84e-0e39ab942c9c
➡️ The subject claim CHANGED - impersonation worked!
```
**Nextcloud API Validation**:
The impersonated token successfully authenticated with Nextcloud APIs, confirming the token is valid and properly represents the target user.
**Implementation Status**: Impersonation **IS IMPLEMENTED** and working with Keycloak Legacy V1. The implementation has been tested and verified to work correctly when properly configured.
**Production Considerations**:
- ⚠️ Requires preview features (`--features=preview`) - not production-ready
- ⚠️ Requires Legacy V1 token exchange (may be deprecated in future Keycloak versions)
- ⚠️ Requires manual CLI configuration for each service account
- ⚠️ More complex permission model compared to delegation
**When to Use Tier 1 (Impersonation)**:
- ✅ You need the exchanged token to have the exact same identity as the target user
- ✅ You want the cleanest separation (sub claim changes completely)
- ✅ Your environment can support preview features
- ✅ You have operational processes to manage impersonation permissions
**Recommendation**: For most use cases, use Tier 2 (Delegation) instead. It provides equivalent "act on-behalf-of" capability using production-ready Standard V2 token exchange. Use Tier 1 only when you specifically need identity impersonation.
**Test Scripts**:
- `tests/manual/test_impersonation.py` - Complete impersonation test with validation
- `tests/manual/configure_impersonation.py` - Automated permission configuration helper
- **See**: `docs/oauth-impersonation-findings.md` for detailed investigation
### 2. Token Exchange with Delegation (Tier 2) ✅ IMPLEMENTED (Standard V2)
**Status**: Implemented and working with Keycloak Standard V2 (production-ready). This is the **recommended** approach for most use cases.
**When to Use**: When you need "act on-behalf-of" functionality with production-ready features. The service account maintains its identity (sub claim unchanged) but acts on behalf of the user. Fully supported in Keycloak Standard V2 without preview features.
#### 2.1 Capability Detection
```python
async def check_token_exchange_support(discovery_url: str) -> bool:
"""Check if OIDC provider supports RFC 8693 token exchange"""
async with httpx.AsyncClient() as client:
response = await client.get(discovery_url)
discovery = response.json()
# Check for token exchange grant type
grant_types = discovery.get("grant_types_supported", [])
return "urn:ietf:params:oauth:grant-type:token-exchange" in grant_types
```
#### 2.2 Delegation Token Exchange
```python
async def exchange_for_user_token(
service_token: str,
target_user_id: str,
audience: str,
scopes: list[str]
) -> str:
"""Exchange service token for user-scoped token via RFC 8693"""
async with httpx.AsyncClient() as client:
response = await client.post(
token_endpoint,
data={
"grant_type": "urn:ietf:params:oauth:grant-type:token-exchange",
"subject_token": service_token,
"subject_token_type": "urn:ietf:params:oauth:token-type:access_token",
"requested_token_type": "urn:ietf:params:oauth:token-type:access_token",
"audience": audience, # Target resource server (e.g., "nextcloud")
"scope": " ".join(scopes)
},
auth=(client_id, client_secret)
)
if response.status_code != 200:
logger.warning(f"Token exchange failed: {response.status_code}")
raise TokenExchangeNotSupportedError()
return response.json()["access_token"]
```
**Implementation**: `KeycloakOAuthClient.exchange_token_for_user()` (keycloak_oauth.py:397-495)
**Note**: Full delegation with `act` claim requires provider support that is currently very rare. Keycloak tracking: [Issue #38279](https://github.com/keycloak/keycloak/issues/38279)
### 3. Comparison: When to Use Each Tier
| Feature | Tier 1: Impersonation | Tier 2: Delegation (Recommended) |
|---------|----------------------|-----------------------------------|
| **Status** | ✅ Implemented (Legacy V1) | ✅ Implemented (Standard V2) |
| **Token Identity** | Target user (`sub` changes) | Service account (`sub` unchanged) |
| **Keycloak Version** | Legacy V1 (`--features=preview`) | Standard V2 (production-ready) |
| **Setup Complexity** | High (manual permissions) | Low (automatic) |
| **Production Ready** | ⚠️ Preview features required | ✅ Fully production-ready |
| **Permission Grant** | Manual CLI per service account | Automatic via token exchange |
| **Audit Trail** | Shows as target user | Shows as service account acting for user |
| **Token Claims** | `sub: user-id` | `sub: service-account-id` |
| **Provider Support** | Rare (Keycloak Legacy V1 only) | Common (Keycloak, Auth0, Okta) |
| **Use Case** | Need exact user identity | Standard OAuth workflows |
| **Recommendation** | Advanced use only | **Default choice** |
**Decision Guide**:
- ✅ **Use Tier 2 (Delegation)** for:
- Production deployments
- Standard OAuth workflows
- Clear audit trails (service account visible)
- Maximum provider compatibility
- ⚠️ **Use Tier 1 (Impersonation)** only if:
- You specifically need exact user identity (sub claim must match)
- You can accept preview/experimental features
- You have operational processes for permission management
- Your IdP supports `requested_subject` parameter
### 4. Sync Worker with Tiered Authentication
```python
# nextcloud_mcp_server/sync_worker.py
class VectorSyncWorker:
"""Background worker for indexing content into vector database"""
def __init__(self):
self.auth_method = None
self.oauth_client = None # KeycloakOAuthClient or similar
self.vector_service = None
async def initialize(self):
"""Detect and configure authentication method"""
from nextcloud_mcp_server.auth.keycloak_oauth import KeycloakOAuthClient
try:
self.oauth_client = KeycloakOAuthClient.from_env()
await self.oauth_client.discover()
# Verify service account access (Tier 1)
service_token = await self.oauth_client.get_service_account_token()
logger.info("✓ Service account token acquired")
# Check if token exchange is supported (Tier 2/3)
if await check_token_exchange_support(self.oauth_client.discovery_url):
self.auth_method = "token_exchange_delegation"
logger.info(
"✓ Token exchange supported (RFC 8693) - will use delegation for user-scoped operations"
)
else:
self.auth_method = "service_account"
logger.info(
" Token exchange not supported - using service account token for all operations"
)
except Exception as e:
logger.error(f"Failed to initialize OAuth authentication: {e}")
raise RuntimeError(
"OAuth authentication is required for background sync. "
"Either configure OIDC_CLIENT_ID/OIDC_CLIENT_SECRET with service account enabled, "
"or use BasicAuth mode for single-user deployments."
) from e
async def get_user_client(self, user_id: str) -> NextcloudClient:
"""Get authenticated client for user based on auth method"""
if self.auth_method == "token_exchange_delegation":
# Tier 2/3: Get service token and exchange for user-scoped token
service_token_data = await self.oauth_client.get_service_account_token()
user_token_data = await self.oauth_client.exchange_token_for_user(
subject_token=service_token_data["access_token"],
target_user_id=user_id,
audience="nextcloud",
scopes=["notes:read", "files:read", "calendar:read"]
)
return NextcloudClient.from_token(
base_url=nextcloud_host,
token=user_token_data["access_token"],
username=user_id
)
elif self.auth_method == "service_account":
# Tier 1: Use service account token directly (no user scoping)
service_token_data = await self.oauth_client.get_service_account_token()
return NextcloudClient.from_token(
base_url=nextcloud_host,
token=service_token_data["access_token"],
username="service-account"
)
raise RuntimeError(f"Unknown auth method: {self.auth_method}")
async def sync_user_content(self, user_id: str):
"""Index a user's content into vector database"""
try:
# Get authenticated client for this user
client = await self.get_user_client(user_id)
# Sync notes
notes = await client.notes.list_notes()
for note in notes:
embedding = await self.vector_service.embed(note.content)
await self.vector_service.upsert(
collection="nextcloud_content",
id=f"note_{note.id}",
vector=embedding,
metadata={
"user_id": user_id,
"content_type": "note",
"note_id": note.id,
"title": note.title,
"category": note.category
}
)
logger.info(f"Synced {len(notes)} notes for user: {user_id}")
except Exception as e:
logger.error(f"Failed to sync user {user_id}: {e}")
async def run(self):
"""Main sync loop"""
await self.initialize()
while True:
try:
# Get list of users to sync
# Implementation depends on how you track authenticated users
# Options:
# - Audit logs of MCP authentication events
# - MCP session history
# - Configured user list
# - If using service account with broad permissions: list all users
user_ids = await self.get_active_users()
logger.info(f"Syncing content for {len(user_ids)} users")
for user_id in user_ids:
await self.sync_user_content(user_id)
logger.info("Sync complete, sleeping...")
await asyncio.sleep(300) # 5 minutes
except Exception as e:
logger.error(f"Sync failed: {e}")
await asyncio.sleep(60) # Retry after 1 minute
```
### 4. User Request Verification (Dual-Phase Authorization)
```python
@mcp.tool()
@require_scopes("notes:read")
async def nc_notes_semantic_search(
query: str,
ctx: Context,
limit: int = 10
) -> SemanticSearchResponse:
"""Semantic search with permission verification"""
# Get user's OAuth client (uses their access token from request)
user_client = get_client(ctx)
username = user_client.username
# Phase 1: Vector search (fast, may include false positives)
embedding = await vector_service.embed(query)
candidate_results = await qdrant.search(
collection_name="nextcloud_content",
query_vector=embedding,
query_filter={
"must": [
{
"should": [
{"key": "user_id", "match": {"value": username}},
{"key": "shared_with", "match": {"any": [username]}}
]
},
{"key": "content_type", "match": {"value": "note"}}
]
},
limit=limit * 2 # Get extra candidates
)
# Phase 2: Verify access via Nextcloud API (authoritative)
verified_results = []
for candidate in candidate_results:
note_id = candidate.payload["note_id"]
try:
# This uses user's OAuth token - will fail if no access
note = await user_client.notes.get_note(note_id)
verified_results.append({
"note": note,
"score": candidate.score
})
if len(verified_results) >= limit:
break
except HTTPStatusError as e:
if e.response.status_code == 403:
# User doesn't have access - skip silently
logger.debug(f"Filtered out note {note_id} for {username}")
continue
raise
return SemanticSearchResponse(results=verified_results)
```
### 5. Security Implementation
#### 5.1 Service Account Credentials Protection
```python
# Store OAuth client credentials securely
# NEVER commit to source control
# Option 1: Environment variables (for development)
export OIDC_CLIENT_ID="nextcloud-mcp-server"
export OIDC_CLIENT_SECRET="<secure-secret>"
# Option 2: Secrets manager (for production)
import boto3
secrets = boto3.client('secretsmanager')
secret = secrets.get_secret_value(SecretId='nextcloud-mcp-oauth')
client_secret = json.loads(secret['SecretString'])['client_secret']
# Option 3: Encrypted storage (for self-hosted)
from nextcloud_mcp_server.auth.refresh_token_storage import RefreshTokenStorage
storage = RefreshTokenStorage.from_env()
await storage.initialize()
# Client credentials are encrypted at rest using Fernet
client_data = await storage.get_oauth_client()
```
#### 5.2 Token Lifecycle Management
```python
async def manage_service_token_lifecycle():
"""Cache and refresh service account tokens"""
# Cache service token (avoid repeated requests)
cached_token = None
token_expires_at = 0
async def get_fresh_service_token() -> str:
nonlocal cached_token, token_expires_at
now = time.time()
# Return cached token if still valid (with 5-minute buffer)
if cached_token and now < (token_expires_at - 300):
return cached_token
# Request new token
token_data = await oauth_client.get_service_account_token()
cached_token = token_data["access_token"]
token_expires_at = now + token_data.get("expires_in", 3600)
logger.info("Service account token refreshed")
return cached_token
return get_fresh_service_token
```
#### 5.3 Audit Logging
```python
async def audit_log(
event: str,
user_id: str,
resource_type: str,
resource_id: str,
auth_method: str
):
"""Log sync operations for audit trail"""
await audit_db.execute(
"INSERT INTO audit_logs VALUES (?, ?, ?, ?, ?, ?, ?)",
(
int(time.time()),
event, # "index_note", "index_file"
user_id,
resource_type,
resource_id,
auth_method,
socket.gethostname()
)
)
```
### 6. Configuration
#### 6.1 Environment Variables
```bash
# OAuth Configuration (Required for Background Sync in OAuth Mode)
# Requires external OIDC provider with client_credentials support
OIDC_DISCOVERY_URL=http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration
OIDC_CLIENT_ID=nextcloud-mcp-server
OIDC_CLIENT_SECRET=<secure-secret>
NEXTCLOUD_HOST=http://app:80
# Tier selection is automatic:
# - Tier 1 (service_account): Always available if client has service account enabled
# - Tier 2/3 (token_exchange): Used if provider supports RFC 8693 token exchange
# Vector Database
QDRANT_URL=http://qdrant:6333
QDRANT_API_KEY=<api-key>
# Sync Configuration
SYNC_INTERVAL_SECONDS=300
SYNC_BATCH_SIZE=100
# Note: For BasicAuth mode (single-user), background sync uses NEXTCLOUD_USERNAME/NEXTCLOUD_PASSWORD
# This ADR focuses on OAuth mode only
```
#### 6.2 Keycloak Configuration (for Token Exchange)
**Client Settings** (`nextcloud-mcp-server`):
```json
{
"clientId": "nextcloud-mcp-server",
"serviceAccountsEnabled": true,
"authorizationServicesEnabled": false,
"attributes": {
"token.exchange.grant.enabled": "true",
"client.token.exchange.standard.enabled": "true"
}
}
```
**Service Account Roles**:
- Assign appropriate Nextcloud roles/scopes to the service account
- Configure token exchange permissions
#### 6.3 Docker Compose
```yaml
services:
mcp-sync:
build: .
command: ["python", "-m", "nextcloud_mcp_server.sync_worker"]
environment:
- NEXTCLOUD_HOST=http://app:80
# External OIDC provider (Keycloak)
- OIDC_DISCOVERY_URL=http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration
- OIDC_CLIENT_ID=nextcloud-mcp-server
- OIDC_CLIENT_SECRET=${OIDC_CLIENT_SECRET}
# Vector database
- QDRANT_URL=http://qdrant:6333
- QDRANT_API_KEY=${QDRANT_API_KEY}
volumes:
- sync-data:/app/data # For OAuth client credential storage
depends_on:
- app
- keycloak
- qdrant
volumes:
sync-data: # Persistent storage for encrypted OAuth client credentials
```
## Consequences
### Benefits
1. **OAuth-Native Authentication**
- Leverages standard OAuth flows (offline_access, token exchange)
- No reliance on admin passwords in production
- Compatible with enterprise OIDC providers
2. **User-Level Permissions**
- Each user's content indexed with their own credentials
- Respects sharing, permissions, and access controls
- Full audit trail of which user's token was used
3. **Security**
- Tokens encrypted at rest
- Short-lived access tokens (refreshed as needed)
- Token rotation support
- Defense in depth with dual-phase authorization
4. **Flexibility**
- Automatic capability detection
- Graceful degradation through authentication tiers
- Works with varying OIDC provider capabilities
5. **Operational**
- Background sync independent of user activity
- Efficient batch processing
- Clear separation of sync vs request credentials
### Limitations
1. **Complexity**
- Multiple authentication paths to maintain
- Token storage and encryption infrastructure
- More moving parts than simple admin auth
2. **User Experience**
- `offline_access` scope may require additional consent
- Users must authenticate at least once for indexing
- New users not automatically indexed
3. **OIDC Provider Dependency**
- Token exchange requires RFC 8693 support (rare)
- Refresh token rotation varies by provider
- Some providers may not support offline_access
4. **Operational Overhead**
- Token database maintenance
- Monitoring token expiration
- Handling revoked tokens gracefully
### Security Considerations
#### Threat Model
**Threat 1: Token Storage Breach**
- **Mitigation**: Encryption at rest using Fernet
- **Mitigation**: Secure key management (secrets manager)
- **Mitigation**: Minimal token lifetime
- **Detection**: Audit logs for unusual access patterns
**Threat 2: Token Replay**
- **Mitigation**: Short-lived access tokens (refreshed frequently)
- **Mitigation**: Token rotation on each refresh
- **Mitigation**: Revocation support
**Threat 3: Privilege Escalation**
- **Mitigation**: Dual-phase authorization (vector DB + Nextcloud API)
- **Mitigation**: Sync worker uses same scopes as user requests
- **Mitigation**: Per-user token isolation
**Threat 4: Vector Database Poisoning**
- **Mitigation**: User requests always verify via Nextcloud API
- **Mitigation**: Vector DB is cache/accelerator, not source of truth
- **Mitigation**: Sync operations audited per user
#### Security Best Practices
1. **OAuth Client Secret Management**
```bash
# Store in secrets manager (Vault, AWS Secrets Manager, etc.)
# Or use environment variable with restricted permissions
# For self-hosted: Use encrypted storage
# OAuth client credentials stored in SQLite with Fernet encryption
# Encryption key: TOKEN_ENCRYPTION_KEY environment variable
# Generate encryption key:
python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
```
2. **Service Account Token Lifecycle**
- Cache service tokens to minimize requests (with expiry buffer)
- Automatically refresh expired tokens
- Use short-lived tokens (provider default, typically 1 hour)
- Monitor token request rates and failures
3. **Database Permissions (for Client Credential Storage)**
```bash
# Restrict database file permissions
chmod 600 /app/data/tokens.db
chown mcp-server:mcp-server /app/data/tokens.db
```
4. **Monitoring and Alerting**
- Alert on token exchange failures
- Monitor for unusual access patterns
- Track service account token usage
- Audit sync operations per user (if delegation supported)
### Future Enhancements
1. **Token Revocation Handling**
- Webhook endpoint for token revocation events
- Periodic validation of stored tokens
- Graceful handling of revoked tokens
2. **Selective Sync**
- Allow users to opt-in/opt-out of indexing
- Per-content-type sync preferences
- Privacy controls for sensitive content
3. **Multi-Tenant Token Storage**
- Separate token databases per tenant
- Key rotation per tenant
- Tenant isolation
4. **Token Lifecycle Management**
- Automatic cleanup of expired tokens
- Token usage analytics
- Token health dashboard
5. **Alternative OAuth Flows**
- Device flow for headless sync
- Resource owner password credentials (ROPC) as fallback
- SAML assertion grants
## Alternatives Considered
### Alternative 1: Admin BasicAuth Only
**Approach**: Background worker always uses admin credentials
**Pros**:
- Simple implementation
- No token storage complexity
- Works with any authentication backend
**Cons**:
- Violates principle of least privilege
- Single powerful credential
- No per-user audit trail
- Bypasses OAuth entirely
**Decision**: Rejected for production use; kept as fallback only
### Alternative 2: Client Credentials Grant Only
**Approach**: Service account with broad read permissions
**Pros**:
- OAuth-native pattern
- No user token storage
- Standard OAuth flow
**Cons**:
- Requires client_credentials support (may not be available)
- Still needs broad cross-user permissions
- Not well-suited for multi-user indexing
**Decision**: Rejected; token exchange is better fit for multi-user scenario
### Alternative 3: Per-User Access Token Storage
**Approach**: Store user access tokens (not refresh tokens)
**Pros**:
- Simpler than refresh token flow
- No token refresh logic needed
**Cons**:
- Access tokens are short-lived (1-24 hours)
- Requires frequent re-authentication
- Poor user experience
- Sync gaps when tokens expire
**Decision**: Rejected; refresh tokens provide better UX
### Alternative 4: On-Demand Indexing Only
**Approach**: Index content when user searches (no background worker)
**Pros**:
- Uses user's request token
- No background auth needed
- Simpler architecture
**Cons**:
- Very slow first search
- Poor user experience
- Incomplete index
- Can't pre-compute embeddings
**Decision**: Rejected; background indexing is essential for semantic search
### Alternative 5: Nextcloud App Tokens
**Approach**: Generate app-specific passwords for each user
**Pros**:
- Nextcloud-native feature
- User-controlled revocation
- Scoped per-application
**Cons**:
- Requires user interaction to create
- May not support programmatic creation
- Still requires secure storage
- Not standard OAuth
**Decision**: Rejected; not automatable for background worker
## Related Decisions
- ADR-001: Enhanced Note Search (establishes need for vector search)
- [Future] ADR-003: Vector Database Selection
- [Future] ADR-004: Embedding Model Strategy
## References
- [RFC 8693: OAuth 2.0 Token Exchange](https://datatracker.ietf.org/doc/html/rfc8693)
- [RFC 6749: OAuth 2.0 - Refresh Tokens](https://datatracker.ietf.org/doc/html/rfc6749#section-1.5)
- [OpenID Connect Core - Offline Access](https://openid.net/specs/openid-connect-core-1_0.html#OfflineAccess)
- [OWASP: OAuth Security Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/OAuth2_Cheat_Sheet.html)
- [RFC 8707: Resource Indicators for OAuth 2.0](https://datatracker.ietf.org/doc/html/rfc8707)
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Excellent and incredibly thorough work on ADR-004. It outlines a robust, secure, and modern approach to federated authentication that aligns with industry best practices. The Progressive Consent architecture with dual OAuth flows is the right direction for a system with these requirements.
Here is a review of the current implementation in light of the architecture proposed in the ADR.
### High-Level Assessment
The project is in a good state, with a clear vision for its authentication architecture. The current implementation provides a backward-compatible "Hybrid Flow" while also containing the scaffolding for the target "Progressive Consent" flow. The hybrid flow is well-tested, which is a great foundation.
The following points are intended to help bridge the gap between the current implementation and the final vision outlined in ADR-004.
### Critical Security Review
#### 1. Missing Token Audience (`aud`) Validation
This is the most critical issue. The `require_scopes` decorator currently checks for scopes but does not validate the `audience` (`aud` claim) of the incoming JWT.
* **Risk:** This creates a "confused deputy" vulnerability. An access token issued for a different application could be used to access the MCP server, as long as the scope names happen to match.
* **ADR Reference:** The ADR correctly identifies this and proposes an `MCPTokenVerifier` that validates `aud: "mcp-server"`.
* **Recommendation:** Implement the audience validation as a central part of your token verification middleware. An incoming token should be rejected immediately if its audience is not `mcp-server`. This check should happen before any tool-specific scope checks.
### Architecture and Implementation Review
#### 2. Progressive Consent Flow is Untested
The code for the Progressive Consent flow (behind the `ENABLE_PROGRESSIVE_CONSENT` flag) exists in `oauth_routes.py` and `oauth_tools.py`. However, there are no integration tests to validate it.
* **Risk:** Given the complexity of OAuth flows, it's likely there are bugs in the untested implementation.
* **Recommendation:** Create a new test file, `test_adr004_progressive_flow.py`, that uses Playwright to test the dual-flow architecture end-to-end:
1. **Flow 1:** A test MCP client authenticates directly with the IdP to get an `mcp-server` token.
2. **Provisioning Check:** The test verifies that calling a Nextcloud tool fails with a `ProvisioningRequiredError`.
3. **Flow 2:** The test calls the `provision_nextcloud_access` tool and automates the second OAuth flow to grant the server offline access.
4. **Tool Execution:** The test verifies that Nextcloud tools can now be successfully called.
#### 3. Inconsistent Authorization URL Generation
There is duplicated and inconsistent logic for generating the IdP authorization URL.
* **Location 1:** `oauth_tools.py` in `generate_oauth_url_for_flow2` hardcodes the authorization endpoint path.
* **Location 2:** `oauth_routes.py` in `oauth_authorize_nextcloud` correctly uses the OIDC discovery document to find the `authorization_endpoint`.
* **Risk:** The hardcoded path is brittle and will break with IdPs that use different endpoint paths (like Keycloak).
* **Recommendation:** Consolidate this logic. The `provision_nextcloud_access` tool should not build the URL itself. Instead, it should return a URL pointing to the MCP server's own `/oauth/authorize-nextcloud` endpoint. This endpoint (which you've already created as `oauth_authorize_nextcloud` in `oauth_routes.py`) can then be the single source of truth for generating the IdP redirect.
#### 4. Poor User Experience due to Missing Token Refresh
The `/oauth/token` endpoint does not implement the `refresh_token` grant type. This means that when the client's `mcp-server` access token expires (e.g., after one hour), the user must go through the entire browser-based login flow again.
* **Risk:** This creates a frustrating user experience, especially for long-lived desktop clients.
* **ADR Reference:** A proper Flow 1 should result in the MCP client receiving both an access token and a refresh token from the IdP.
* **Recommendation:**
1. Ensure the IdP is configured to issue refresh tokens to the MCP client for Flow 1.
2. The MCP client should securely store this refresh token.
3. The client should use the refresh token to get new `mcp-server` access tokens directly from the IdP, without involving the MCP server or the user. The MCP server should not be involved in the client's session management with the IdP.
### Summary
The project is on the right track. The ADR is a solid plan, and the initial implementation is a good starting point.
My recommendations in order of priority are:
1. **Implement Audience Validation** to close the security gap.
2. **Add Integration Tests** for the Progressive Consent flow.
3. **Refactor the client-side token refresh** to improve user experience.
4. **Consolidate the URL generation** logic to fix the inconsistency.
Addressing these points will align the implementation with the excellent vision in ADR-004 and result in a secure, robust, and user-friendly system.
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# ADR-006: Progressive Consent via URL Elicitation (SEP-1036)
**Status**: Partially Implemented (Interim Workaround)
**Date**: 2025-01-05 (Updated: 2025-01-07)
**Related**: [SEP-1036](https://github.com/modelcontextprotocol/specification/pull/887), ADR-004
**Depends On**: ADR-005 (token validation)
## Context
### What is Progressive Consent?
**Progressive consent is a mechanism, not a feature**. It describes HOW users grant the MCP server access to Nextcloud resources through OAuth elicitation. The server can operate in two modes:
1. **Pass-through mode (ENABLE_OFFLINE_ACCESS=false)**:
- No refresh tokens requested or stored
- Server passes through client's access token to Nextcloud
- No provisioning tools available
- Suitable for stateless, client-driven operations
2. **Offline access mode (ENABLE_OFFLINE_ACCESS=true)**:
- Server requests `offline_access` scope and stores refresh tokens
- Enables background operations and server-initiated API calls
- Provisioning tools available (`provision_nextcloud_access`, `check_logged_in`)
- Requires explicit user consent via OAuth Flow 2
**Single-user mode (BasicAuth)** doesn't use progressive consent at all - credentials are directly available.
### Current User Experience Issues
The current offline access provisioning flow (ADR-004) requires users to manually visit OAuth URLs returned by MCP tools. This creates a poor user experience:
1. User calls `provision_nextcloud_access` tool
2. Tool returns a URL as text in the response
3. User must manually copy URL and open in browser
4. No indication when provisioning is complete
5. User must retry the original operation manually
### SEP-1036: URL Mode Elicitation
The MCP specification now supports **URL mode elicitation** ([SEP-1036](https://github.com/modelcontextprotocol/specification/pull/887)), which enables servers to:
- Request out-of-band user interactions via secure URLs
- Handle sensitive operations like OAuth flows without exposing credentials to the client
- Provide progress tracking for async operations
- Return errors that automatically trigger elicitation flows
**Key benefits for progressive consent**:
- **Automatic URL Opening**: Client opens URL in browser automatically (with user consent)
- **Progress Tracking**: Server can notify client when provisioning is complete
- **Error-Triggered Flows**: Server can return `ElicitationRequired` error to trigger provisioning
- **Better UX**: User doesn't manually copy/paste URLs
### Current Implementation Limitations
The current progressive consent flow in `nextcloud_mcp_server/server/oauth_tools.py`:
```python
@mcp.tool(name="provision_nextcloud_access")
async def tool_provision_access(ctx: Context) -> ProvisioningResult:
"""Returns OAuth URL as text - user must manually open it."""
return ProvisioningResult(
success=True,
authorization_url=auth_url, # User must copy this
message="Please visit the authorization URL..."
)
```
**Problems**:
1. Manual URL handling (copy/paste)
2. No progress tracking
3. No automatic retry after provisioning
4. Tool call required just to get URL
5. No client integration (URL just displayed as text)
## Decision
We will **migrate progressive consent from manual tools to URL mode elicitation**, leveraging SEP-1036 for better user experience and OAuth security.
### New Architecture: Elicitation-Driven Consent
Instead of explicit tools, use **automatic elicitation** triggered by authorization errors:
```
User → Calls Nextcloud Tool → Server Checks Provisioning
↓ Not Provisioned
Error: ElicitationRequired
Client Shows Consent UI
↓ User Accepts
Client Opens OAuth URL
User Completes OAuth
Server Sends Progress Update
Original Tool Call Auto-Retries
```
### Mode 1: Elicitation-Required Error (Primary)
When a tool requires provisioning, return an **ElicitationRequired error** (-32000):
```python
# In any Nextcloud tool decorated with @require_provisioning
@mcp.tool()
@require_provisioning # New decorator
async def nc_notes_list_notes(ctx: Context):
"""List notes - auto-triggers provisioning if needed."""
# If not provisioned, decorator returns ElicitationRequired error
# If provisioned, continues normally
client = await get_client(ctx)
return await client.notes.list_notes()
```
**Error response structure**:
```json
{
"jsonrpc": "2.0",
"id": 1,
"error": {
"code": -32000,
"message": "Nextcloud access provisioning required",
"data": {
"elicitations": [
{
"mode": "url",
"elicitationId": "550e8400-e29b-41d4-a716-446655440000",
"url": "https://mcp.example.com/oauth/provision?id=550e8400...",
"message": "Grant the MCP server access to your Nextcloud account to continue."
}
]
}
}
}
```
**Client behavior**:
1. Receives error with elicitation
2. Shows consent UI: "App wants to access Nextcloud. Open authorization page?"
3. On user acceptance, opens URL in browser
4. Optionally tracks progress via `elicitation/track`
5. Auto-retries original tool call when complete
### Mode 2: Explicit Elicitation Request (Fallback)
For clients that don't support error-triggered elicitation, provide explicit tool:
```python
@mcp.tool(name="request_nextcloud_access")
async def request_access(ctx: Context) -> ElicitationResponse:
"""Explicitly request provisioning via elicitation."""
# Send elicitation/create request
return await create_elicitation(
mode="url",
url=generate_oauth_url(),
message="Grant access to Nextcloud",
elicitation_id=generate_id()
)
```
**Note**: This is a fallback for compatibility. Primary flow uses error-triggered elicitation.
## Implementation
### 1. New Decorator: `@require_provisioning`
Replace explicit provisioning checks with a decorator that returns `ElicitationRequired`:
```python
# nextcloud_mcp_server/auth/provisioning_decorator.py
def require_provisioning(func):
"""
Decorator that ensures user has provisioned Nextcloud access.
If not provisioned, returns ElicitationRequired error with OAuth URL.
Otherwise, proceeds with normal tool execution.
"""
@functools.wraps(func)
async def wrapper(ctx: Context, *args, **kwargs):
# Extract user ID from token
user_id = get_user_id_from_context(ctx)
# Check if provisioned
storage = RefreshTokenStorage.from_env()
await storage.initialize()
if not await storage.has_refresh_token(user_id):
# Not provisioned - return ElicitationRequired error
elicitation_id = str(uuid.uuid4())
oauth_url = await generate_oauth_url_for_provisioning(
user_id=user_id,
elicitation_id=elicitation_id,
ctx=ctx
)
# Store elicitation for tracking
await storage.store_elicitation(
elicitation_id=elicitation_id,
user_id=user_id,
status="pending",
created_at=datetime.now(timezone.utc)
)
raise McpError(
code=ErrorCode.ELICITATION_REQUIRED, # -32000
message="Nextcloud access provisioning required",
data={
"elicitations": [
{
"mode": "url",
"elicitationId": elicitation_id,
"url": oauth_url,
"message": (
"Grant the MCP server access to your Nextcloud "
"account to continue. This is a one-time setup."
)
}
]
}
)
# Already provisioned - proceed normally
return await func(ctx, *args, **kwargs)
return wrapper
```
### 2. Elicitation Tracking Endpoint
Implement `elicitation/track` to provide progress updates:
```python
# nextcloud_mcp_server/server/elicitation.py
@mcp.request_handler("elicitation/track")
async def track_elicitation(
elicitation_id: str,
_meta: dict = None
) -> dict:
"""
Track progress of an elicitation request.
Returns when elicitation is complete or times out.
"""
progress_token = _meta.get("progressToken") if _meta else None
storage = RefreshTokenStorage.from_env()
await storage.initialize()
# Poll for completion (with timeout)
timeout = 300 # 5 minutes
start_time = datetime.now(timezone.utc)
while (datetime.now(timezone.utc) - start_time).seconds < timeout:
elicitation = await storage.get_elicitation(elicitation_id)
if not elicitation:
raise McpError(
code=-32602, # Invalid params
message=f"Unknown elicitation ID: {elicitation_id}"
)
# Send progress notification if token provided
if progress_token and elicitation["status"] == "pending":
await send_progress_notification(
progress_token=progress_token,
progress=50,
message="Waiting for OAuth authorization..."
)
# Check if complete
if elicitation["status"] == "complete":
return {"status": "complete"}
# Check if failed
if elicitation["status"] == "failed":
return {
"status": "failed",
"error": elicitation.get("error_message")
}
# Wait before polling again
await asyncio.sleep(2)
# Timeout
raise McpError(
code=-32000,
message="Elicitation timed out - user did not complete authorization"
)
```
### 3. OAuth Callback Updates
Update the OAuth callback to mark elicitations as complete:
```python
# nextcloud_mcp_server/auth/oauth_routes.py
async def oauth_callback(request: Request) -> Response:
"""Handle OAuth callback and mark elicitation complete."""
code = request.query_params.get("code")
state = request.query_params.get("state")
# Validate and exchange code for tokens
tokens = await exchange_authorization_code(code)
# Store refresh token
await storage.store_refresh_token(
user_id=user_id,
refresh_token=tokens["refresh_token"]
)
# Mark elicitation as complete
elicitation_id = request.query_params.get("elicitation_id")
if elicitation_id:
await storage.update_elicitation(
elicitation_id=elicitation_id,
status="complete",
completed_at=datetime.now(timezone.utc)
)
return Response(
content="<h1>Authorization Complete!</h1>"
"<p>You can close this window and return to the application.</p>",
media_type="text/html"
)
```
### 4. Update All Nextcloud Tools
Add `@require_provisioning` decorator to all Nextcloud tools:
```python
# nextcloud_mcp_server/server/notes.py
@mcp.tool()
@require_scopes("notes:read")
@require_provisioning # NEW: Auto-triggers provisioning
async def nc_notes_list_notes(
ctx: Context,
category: Optional[str] = None
) -> NotesListResponse:
"""List all notes - automatically handles provisioning."""
client = await get_client(ctx)
# Tool logic proceeds only if provisioned
notes = await client.notes.list_notes(category=category)
return NotesListResponse(results=notes)
```
### 5. Capability Declaration
Declare URL elicitation support during initialization:
```python
# nextcloud_mcp_server/app.py
capabilities = {
"elicitation": {
"url": {} # Declare URL mode support
# Note: We don't support "form" mode (in-band data collection)
},
# ... other capabilities
}
```
### 6. Environment Variables
**Primary control**:
```bash
# ENABLE_OFFLINE_ACCESS: Controls whether server requests refresh tokens and enables provisioning tools
# Default: false (pass-through mode)
# Set to true to enable offline access mode with Flow 2 provisioning
ENABLE_OFFLINE_ACCESS=true
```
**Future variables** (when URL elicitation is implemented):
```bash
# ELICITATION_CALLBACK_URL: Base URL for OAuth callbacks with elicitation tracking
# Default: NEXTCLOUD_MCP_SERVER_URL + /oauth/callback
ELICITATION_CALLBACK_URL=http://localhost:8000/oauth/callback
# ELICITATION_TIMEOUT_SECONDS: How long to wait for user to complete OAuth
# Default: 300 (5 minutes)
ELICITATION_TIMEOUT_SECONDS=300
```
**Removed variables**:
```bash
# ENABLE_PROGRESSIVE_CONSENT - Removed. Progressive consent is a mechanism, not a feature toggle.
# Use ENABLE_OFFLINE_ACCESS to control whether provisioning tools are available.
# MCP_SERVER_CLIENT_ID - merged into OIDC_CLIENT_ID
```
## User Experience Comparison
### Before (ADR-004 Manual Tools)
```
User: "List my notes"
Assistant: *calls nc_notes_list_notes*
Server: Error - not provisioned
Assistant: "You need to provision access first. Let me do that."
Assistant: *calls provision_nextcloud_access*
Server: {authorization_url: "https://..."}
Assistant: "Please visit this URL: https://..."
User: *copies URL, opens browser, completes OAuth*
User: "OK, I'm done"
Assistant: *calls nc_notes_list_notes again*
Server: Success! [notes...]
```
**Issues**: 4 interactions, manual URL handling, no automation
### After (ADR-006 Elicitation)
```
User: "List my notes"
Assistant: *calls nc_notes_list_notes*
Server: ElicitationRequired error
Client: Shows dialog: "Grant access to Nextcloud? [Yes] [No]"
User: *clicks Yes*
Client: Opens OAuth URL in browser automatically
User: *completes OAuth*
Server: Sends progress notification "Complete!"
Client: Auto-retries nc_notes_list_notes
Server: Success! [notes...]
Assistant: "Here are your notes: ..."
```
**Benefits**: 1 interaction, automatic URL opening, seamless retry
## Migration Path
### Phase 1: Add Elicitation Support (v0.26.0)
- Implement `@require_provisioning` decorator
- Add `elicitation/track` endpoint
- Keep existing tools (`provision_nextcloud_access`) for compatibility
- Update OAuth callback to track elicitations
- Add capability declaration
**Breaking changes**: None (additive)
### Phase 2: Update Documentation (v0.27.0)
- Document elicitation-based flow as primary
- Mark manual tools as deprecated
- Update examples and guides
**Breaking changes**: None (documentation only)
### Phase 3: Remove Manual Tools (v0.28.0)
- Remove `provision_nextcloud_access` tool
- Remove `check_provisioning_status` tool (status in error message)
- Remove `revoke_nextcloud_access` (or keep for explicit revocation?)
**Breaking changes**: Yes (removed tools)
### Phase 4: Optimize (v0.29.0+)
- Add elicitation result caching
- Implement retry strategies
- Add metrics and monitoring
## Testing
### Test Cases
1. **First-Time User Flow**
```python
@pytest.mark.oauth
async def test_elicitation_first_time_user(nc_mcp_oauth_client):
"""Test that first tool call triggers elicitation."""
# User has no provisioning
with pytest.raises(McpError) as exc:
await nc_mcp_oauth_client.call_tool("nc_notes_list_notes")
# Should get ElicitationRequired error
assert exc.value.code == -32000
assert "elicitations" in exc.value.data
assert exc.value.data["elicitations"][0]["mode"] == "url"
# Verify URL is valid OAuth URL
url = exc.value.data["elicitations"][0]["url"]
assert "oauth" in url
assert "elicitationId" in url
```
2. **Progress Tracking**
```python
@pytest.mark.oauth
async def test_elicitation_progress_tracking(nc_mcp_oauth_client):
"""Test progress tracking during OAuth flow."""
# Trigger elicitation
elicitation_id = trigger_elicitation()
# Start tracking
track_task = asyncio.create_task(
nc_mcp_oauth_client.track_elicitation(
elicitation_id=elicitation_id,
progress_token="test-token"
)
)
# Simulate OAuth completion
await asyncio.sleep(1)
await complete_oauth_flow(elicitation_id)
# Track should complete
result = await track_task
assert result["status"] == "complete"
```
3. **Auto-Retry After Provisioning**
```python
@pytest.mark.oauth
async def test_auto_retry_after_provisioning(nc_mcp_oauth_client):
"""Test that client auto-retries after elicitation."""
# Mock client that auto-retries on ElicitationRequired
client = AutoRetryMcpClient(nc_mcp_oauth_client)
# First call triggers elicitation, client handles it, retries
result = await client.call_tool_with_elicitation("nc_notes_list_notes")
# Should succeed after provisioning
assert result.success
assert "notes" in result.data
```
4. **Timeout Handling**
```python
@pytest.mark.oauth
async def test_elicitation_timeout(nc_mcp_oauth_client):
"""Test timeout if user doesn't complete OAuth."""
elicitation_id = trigger_elicitation()
# Track with short timeout
with pytest.raises(McpError, match="timed out"):
await nc_mcp_oauth_client.track_elicitation(
elicitation_id=elicitation_id,
timeout=5 # 5 seconds
)
```
## Security Considerations
### Out-of-Band OAuth Flow
**Benefit**: OAuth credentials never pass through MCP client
- User enters credentials directly on IdP page
- MCP server receives only authorization code
- Client never sees passwords or refresh tokens
**Threat mitigation**:
- **Credential theft**: Client can't intercept credentials (out-of-band)
- **Token exposure**: Client never receives Nextcloud refresh tokens
- **CSRF**: State parameter validates OAuth callback
- **URL tampering**: Elicitation ID ties OAuth flow to user session
### Elicitation ID as Security Token
The `elicitationId` serves as a capability token:
- Cryptographically random (UUID v4)
- Single-use (invalidated after completion)
- Time-limited (expires after timeout)
- User-scoped (tied to user session)
**Validation**:
```python
async def validate_elicitation_id(elicitation_id: str, user_id: str) -> bool:
"""Validate that elicitation belongs to user and is still valid."""
elicitation = await storage.get_elicitation(elicitation_id)
if not elicitation:
return False
# Check ownership
if elicitation["user_id"] != user_id:
logger.warning(f"Elicitation ID mismatch: {elicitation_id}")
return False
# Check expiry
if elicitation["expires_at"] < datetime.now(timezone.utc):
return False
# Check not already used
if elicitation["status"] != "pending":
return False
return True
```
### Progress Tracking Security
**Risk**: Progress token reuse across users
**Mitigation**:
- Progress tokens tied to elicitation ID
- Elicitation ID tied to user session
- Server validates ownership before sending updates
## Consequences
### Positive
1. **Better UX**: Automatic URL opening, no manual copy/paste
2. **Seamless Flow**: Auto-retry after provisioning
3. **Progress Feedback**: User knows when OAuth is complete
4. **Spec Compliance**: Implements SEP-1036 correctly
5. **Secure by Design**: Out-of-band OAuth prevents credential exposure
6. **Simpler API**: No explicit provisioning tools needed
### Negative
1. **Client Dependency**: Requires client support for URL elicitation
2. **Complexity**: More moving parts (elicitation tracking, callbacks)
3. **Polling**: Progress tracking uses polling (not ideal)
4. **Breaking Change**: Removes manual provisioning tools (in v0.28.0)
### Neutral
1. **Storage Requirements**: Need to store elicitation state
2. **Timeout Management**: Must handle long-running OAuth flows
3. **Fallback Support**: Still need compatibility for older clients
## Alternatives Considered
### 1. Keep Manual Tools Only (Rejected)
**Pros**: Simple, no client changes needed
**Cons**: Poor UX, doesn't leverage SEP-1036
**Rejection reason**: SEP-1036 provides better UX and security
### 2. Form Mode Elicitation (Rejected)
**Pros**: No browser redirect needed
**Cons**: Would expose OAuth credentials to client (security violation)
**Rejection reason**: Form mode only for non-sensitive data per SEP-1036
### 3. Hybrid: Both Tools and Elicitation (Considered)
**Pros**: Maximum compatibility, gradual migration
**Cons**: API duplication, maintenance burden, confusing for users
**Decision**: Support during migration (v0.26-0.27), remove in v0.28
### 4. WebSocket for Progress (Rejected)
**Pros**: Real-time updates instead of polling
**Cons**: MCP spec uses polling pattern, adds complexity
**Rejection reason**: Follow spec pattern (polling via elicitation/track)
## Interim Implementation: Inline Form Elicitation (Pre-SEP-1036)
**Note**: SEP-1036 (URL mode elicitation) is not yet available in the stable MCP Python SDK. As a temporary workaround, we've implemented a simplified version using the current **inline form elicitation** API.
### What Changed
Instead of waiting for URL mode elicitation, we implemented a `check_logged_in` tool that:
1. Checks if the user has completed Flow 2 (resource provisioning)
2. If logged in, returns `"yes"`
3. If not logged in, uses **inline form elicitation** to prompt the user
### Implementation Details
**New Tool**: `check_logged_in`
```python
# nextcloud_mcp_server/server/oauth_tools.py
class LoginConfirmation(BaseModel):
"""Schema for login confirmation elicitation."""
acknowledged: bool = Field(
default=False,
description="Check this box after completing login at the provided URL",
)
@mcp.tool(name="check_logged_in")
@require_scopes("openid")
async def tool_check_logged_in(ctx: Context, user_id: Optional[str] = None) -> str:
"""Check if user is logged in and elicit login if needed."""
# Check if already logged in
status = await get_provisioning_status(ctx, user_id)
if status.is_provisioned:
return "yes"
# Generate OAuth URL for Flow 2
auth_url = generate_oauth_url_for_flow2(...)
# Use inline form elicitation (current MCP API)
result = await ctx.elicit(
message=f"Please log in to Nextcloud at the following URL:\n\n{auth_url}\n\nAfter completing the login, check the box below and click OK.",
schema=LoginConfirmation,
)
if result.action == "accept":
# Verify login succeeded
status = await get_provisioning_status(ctx, user_id)
return "yes" if status.is_provisioned else "Login not detected"
elif result.action == "decline":
return "Login declined by user."
else:
return "Login cancelled by user."
```
**OAuth Routes** (added to `app.py`):
```python
# Flow 2 routes for resource provisioning
routes.append(
Route("/oauth/authorize-nextcloud", oauth_authorize_nextcloud, methods=["GET"])
)
routes.append(
Route("/oauth/callback-nextcloud", oauth_callback_nextcloud, methods=["GET"])
)
```
### User Experience
```
User: *calls check_logged_in tool*
MCP Client: Displays form elicitation
┌─────────────────────────────────────────────────────────┐
│ Please log in to Nextcloud at the following URL: │
│ │
│ http://localhost:8000/oauth/authorize-nextcloud?... │
│ │
│ After completing the login, check the box below and │
│ click OK. │
│ │
│ ☐ Check this box after completing login │
│ │
│ [Accept] [Decline] [Cancel] │
└─────────────────────────────────────────────────────────┘
User: *copies URL, opens in browser, completes OAuth*
User: *checks box and clicks Accept*
MCP Server: Verifies login and returns "yes"
```
### Limitations of Interim Approach
1. **Manual URL Handling**: User must manually copy and paste the URL (not clickable)
2. **No Automatic Browser Opening**: Client doesn't automatically open the URL
3. **No Progress Tracking**: Can't track OAuth completion status in real-time
4. **URL in Message Text**: Login URL embedded in plain text message (not as structured field)
5. **Client-Side Confirmation**: Relies on user clicking "OK" after OAuth (honor system)
### Why Not Use URL Mode Now?
The current stable MCP Python SDK (`main` branch) only supports **inline form elicitation**:
```python
# Current API (no 'mode' parameter)
class ElicitRequestParams(RequestParams):
message: str
requestedSchema: ElicitRequestedSchema
# No 'mode', 'url', or 'elicitationId' fields
```
URL mode elicitation (`mode: "url"`) is only available in the SEP-1036 branch, which has not been merged to `main` yet.
### Migration to URL Mode (When SEP-1036 Lands)
Once SEP-1036 is merged and available in the stable SDK, we will migrate to URL mode elicitation:
**Before (Current Workaround)**:
```python
result = await ctx.elicit(
message=f"Please log in at: {auth_url}\n\nClick OK after login.",
schema=LoginConfirmation,
)
```
**After (URL Mode)**:
```python
result = await ctx.session.elicit_url(
message="Please log in to Nextcloud to authorize this MCP server.",
url=auth_url,
elicitation_id=elicitation_id,
)
```
**Benefits of migration**:
- Automatic URL opening (with user consent)
- Clickable URLs in client UI
- Progress tracking via `elicitation/track`
- Better security (URL not in message text)
- Auto-retry support
### Testing
Integration tests validate the current inline form elicitation:
```python
# tests/server/oauth/test_login_elicitation.py
async def test_check_logged_in_already_authenticated(nc_mcp_oauth_client):
"""Test immediate 'yes' for authenticated users."""
result = await nc_mcp_oauth_client.call_tool("check_logged_in", arguments={})
assert "yes" in result.content[0].text.lower()
async def test_check_logged_in_url_format(nc_mcp_oauth_client):
"""Test that login URL (when needed) contains correct OAuth parameters."""
result = await nc_mcp_oauth_client.call_tool("check_logged_in", arguments={})
response_text = result.content[0].text
# If URL present, validate OAuth parameters
if "http" in response_text:
assert "response_type=code" in response_text
assert "client_id=" in response_text
assert "redirect_uri=" in response_text
assert "openid" in response_text
```
### Future Work
- **Monitor SEP-1036**: Watch for merge to MCP Python SDK `main` branch
- **Implement URL Mode**: Once available, migrate `check_logged_in` to use `ctx.session.elicit_url()`
- **Add Progress Tracking**: Implement `elicitation/track` endpoint for OAuth completion status
- **Implement Error-Triggered Elicitation**: Use `@require_provisioning` decorator to return `ElicitationRequired` errors
- **Remove Manual Workaround**: Deprecate inline form approach once URL mode is stable
## References
- [SEP-1036: URL Mode Elicitation](https://github.com/modelcontextprotocol/specification/pull/887)
- [MCP Elicitation Specification](https://modelcontextprotocol.io/specification/draft/client/elicitation)
- [ADR-004: Federated Authentication Architecture](./ADR-004-mcp-application-oauth.md)
- [ADR-005: Token Audience Validation](./ADR-005-token-audience-validation.md)
- [RFC 8252: OAuth 2.0 for Native Apps](https://datatracker.ietf.org/doc/html/rfc8252)
## Implementation Checklist
### Interim Implementation (Inline Form Elicitation)
- [x] Create `check_logged_in` tool with inline form elicitation
- [x] Register Flow 2 OAuth routes (`/oauth/authorize-nextcloud`, `/oauth/callback-nextcloud`)
- [x] Write integration tests for login elicitation flow
- [x] Update ADR-006 with interim implementation documentation
- [x] Add `LoginConfirmation` schema for elicitation
- [ ] Run tests to validate implementation
### Future Work (URL Mode Elicitation - Post SEP-1036)
- [ ] Implement `@require_provisioning` decorator with ElicitationRequired error
- [ ] Add `elicitation/track` request handler
- [ ] Update OAuth callback to mark elicitations complete
- [ ] Add elicitation storage (ID, user, status, timestamps)
- [ ] Update all Nextcloud tools with `@require_provisioning`
- [ ] Add URL elicitation capability declaration
- [ ] Write tests for progress tracking
- [ ] Update documentation with URL mode examples
- [ ] Add migration guide for manual tools → elicitation
- [ ] Migrate `check_logged_in` from inline form to URL mode
- [ ] Keep manual tools with deprecation warnings (v0.26-0.27)
- [ ] Remove manual tools (v0.28.0)
- [ ] Update CHANGELOG.md with migration timeline
File diff suppressed because it is too large Load Diff
@@ -1,647 +0,0 @@
# ADR-008: MCP Sampling for Multi-App Semantic Search with RAG
**Status**: Proposed
**Date**: 2025-01-11
**Depends On**: ADR-007 (Background Vector Sync)
## Context
ADR-007 established a background synchronization architecture that maintains a vector database of Nextcloud content across multiple apps (notes, calendar, deck, files, contacts), enabling semantic search via the `nc_semantic_search` tool. This tool returns a list of relevant documents with excerpts, similarity scores, and metadata—providing the raw materials for answering user questions.
However, users typically don't want a list of documents—they want answers to their questions. When a user asks "What are my project goals?" or "When is my next dentist appointment?", they expect a natural language response that synthesizes information from multiple sources and document types, not a ranked list of excerpts. This is the pattern of Retrieval-Augmented Generation (RAG): retrieve relevant context from all Nextcloud apps, then generate a cohesive answer.
The challenge is: who should generate the answer, and how?
**Option 1: Server-side LLM**
The MCP server could maintain its own LLM connection (OpenAI API, Ollama, etc.), construct prompts from retrieved documents, and return generated answers directly. This approach has significant drawbacks:
- **Duplicate infrastructure**: MCP clients (like Claude Desktop) already have LLM capabilities. The server would duplicate this with its own LLM integration, API keys, and configuration.
- **Cost and billing**: The server operator bears LLM costs for all users, creating billing and quota management challenges.
- **Limited model choice**: Users are locked into whatever LLM the server configures. They cannot choose their preferred model or provider.
- **Privacy concerns**: User queries and document contents flow through a server-controlled LLM, creating a potential privacy boundary.
- **Configuration complexity**: Server operators must configure embedding services (for search) AND generation models (for answers), each with different API keys, rate limits, and failure modes.
**Option 2: Return documents, let client generate**
The server could simply return retrieved documents and rely on the MCP client's existing LLM to generate answers. The user would call `nc_notes_semantic_search`, receive documents, and then the client would include those documents in its context when responding to the user's original question. This approach also has limitations:
- **Context window waste**: The client must include all document content in its context window, even if only small excerpts are relevant. For 5-10 documents, this can consume significant context space.
- **Inconsistent behavior**: Whether the client synthesizes an answer or just displays documents depends on the client's implementation and the user's conversational style. There's no guaranteed answer generation.
- **Poor citations**: The client may generate an answer but fail to cite which specific documents were used, making it hard to verify claims.
- **User confusion**: Users see a tool that returns "search results" rather than "answers", requiring them to explicitly ask for synthesis.
**Option 3: MCP Sampling**
The Model Context Protocol specification includes a **sampling** capability that allows MCP servers to request LLM completions from their clients. The server constructs a prompt with retrieved context, sends it to the client via `sampling/createMessage`, and the client's LLM generates a response that the server can return as a tool result.
This approach combines the best of both options:
- **No server-side LLM**: The server has no API keys, no LLM configuration, no billing concerns.
- **User choice**: The MCP client controls which LLM is used (Claude, GPT-4, local Ollama) and who pays for it.
- **User transparency**: MCP clients SHOULD present sampling requests to users for approval, making it clear when the server is requesting an LLM call.
- **Consistent citations**: The server constructs a prompt that explicitly includes document references, ensuring generated answers cite sources.
- **Single tool call**: Users call one tool (`nc_notes_semantic_search_answer`) and receive a complete answer with citations—no multi-turn conversation needed.
The sampling approach shifts responsibility appropriately: the MCP server is responsible for information retrieval and context construction (its expertise), while the MCP client is responsible for LLM access and user preferences (its expertise). This follows the MCP design philosophy of separating concerns between servers (data access) and clients (user interaction).
However, sampling introduces new considerations:
**Client compatibility**: Not all MCP clients implement sampling. The server must gracefully degrade when sampling is unavailable, falling back to returning documents without generated answers.
**Latency**: Sampling adds a full round-trip to the client and back, plus LLM generation time. A typical flow involves: (1) client calls tool, (2) server retrieves documents, (3) server requests sampling from client, (4) client generates answer, (5) server returns answer to client. This can take 2-5 seconds depending on LLM speed, compared to 100-500ms for document retrieval alone.
**User approval**: MCP clients SHOULD prompt users to approve sampling requests, allowing users to review the prompt before sending it to their LLM. This is a privacy and security feature (prevents servers from making arbitrary LLM requests) but adds interaction friction.
**Prompt engineering**: The server must construct effective prompts that guide the LLM to generate useful, well-cited answers. Unlike Option 1 where the server controls the LLM directly, the server has less control over how the prompt is interpreted.
Despite these considerations, MCP sampling provides the most principled solution for RAG-enhanced semantic search. It respects the client-server boundary, avoids duplicate infrastructure, and delivers the user experience users expect from semantic search tools.
This ADR proposes adding a new tool, `nc_semantic_search_answer`, that uses MCP sampling to generate natural language answers from retrieved Nextcloud content across all indexed apps (notes, calendar, deck, files, contacts).
## Decision
We will implement a new MCP tool `nc_semantic_search_answer` that retrieves relevant documents via vector similarity search across all indexed Nextcloud apps and uses MCP sampling to generate natural language answers. The tool will construct a prompt that includes the user's original query and excerpts from retrieved documents (notes, calendar events, deck cards, files, contacts), request an LLM completion via `ctx.session.create_message()`, and return the generated answer along with source citations.
The existing `nc_semantic_search` tool will remain unchanged, providing users with a choice: call the original tool for raw document results, or call the new sampling-enhanced tool for generated answers. This dual-tool approach respects different use cases—some users want to browse documents, others want direct answers.
### API Design
**Tool Signature**:
```python
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search_answer(
query: str,
ctx: Context,
limit: int = 5,
score_threshold: float = 0.7,
max_answer_tokens: int = 500,
) -> SamplingSearchResponse
```
**Parameters**:
- `query`: The user's natural language question
- `ctx`: MCP context for session access
- `limit`: Maximum documents to retrieve (default 5)
- `score_threshold`: Minimum similarity score 0-1 (default 0.7)
- `max_answer_tokens`: Maximum tokens for generated answer (default 500)
**Response Model**:
```python
class SamplingSearchResponse(BaseResponse):
query: str # Original user query
generated_answer: str # LLM-generated answer
sources: list[SemanticSearchResult] # Supporting documents
total_found: int # Total matching documents
search_method: str = "semantic_sampling"
model_used: str | None = None # Model that generated answer
stop_reason: str | None = None # Why generation stopped
```
The response includes both the generated answer (for direct user consumption) and the source documents (for verification and citation). The `model_used` field records which LLM generated the answer, allowing users to understand which model provided the response.
### Sampling API Usage
The tool uses the MCP Python SDK's `ServerSession.create_message()` API:
```python
from mcp.types import SamplingMessage, TextContent, ModelPreferences, ModelHint
# Construct prompt with retrieved context
prompt = (
f"{query}\n\n"
f"Here are relevant documents from Nextcloud (notes, calendar events, deck cards, files, contacts):\n\n"
f"{context}\n\n"
f"Based on the documents above, please provide a comprehensive answer. "
f"Cite the document numbers when referencing specific information."
)
# Request LLM completion via MCP sampling
sampling_result = await ctx.session.create_message(
messages=[
SamplingMessage(
role="user",
content=TextContent(type="text", text=prompt),
)
],
max_tokens=max_answer_tokens,
temperature=0.7,
model_preferences=ModelPreferences(
hints=[ModelHint(name="claude-3-5-sonnet")],
intelligencePriority=0.8,
speedPriority=0.5,
),
include_context="thisServer",
)
# Extract answer from response
if sampling_result.content.type == "text":
generated_answer = sampling_result.content.text
```
**Key parameters**:
- `messages`: Chat-style messages with role ("user" or "assistant") and content
- `max_tokens`: Limits response length to control costs and latency
- `temperature`: 0.7 balances creativity with consistency for factual answers
- `model_preferences`: Hints suggest Claude Sonnet for balanced intelligence/speed
- `include_context`: "thisServer" includes MCP server context in client's LLM call
The `include_context` parameter is particularly important. When set to "thisServer", the MCP client provides its LLM with context about the server's capabilities, tools, and resources. This allows the LLM to reference the Nextcloud MCP server when generating answers, creating more contextually appropriate responses. For example, the LLM might say "Based on your Nextcloud Notes..." rather than generic phrasing.
### Prompt Construction
The prompt construction follows a structured template:
```
[User's original query]
Here are relevant documents from Nextcloud (notes, calendar events, deck cards, files, contacts):
[Document 1]
Type: note
Title: Project Kickoff Notes
Category: Work
Excerpt: The primary goal for Q1 2025 is to improve semantic search...
Relevance Score: 0.92
[Document 2]
Type: calendar_event
Title: Team Planning Meeting
Location: Conference Room A
Excerpt: Scheduled for Jan 15 at 2pm. Agenda: Discuss Q1 objectives and timeline...
Relevance Score: 0.88
[Document 3]
Type: deck_card
Title: Implement semantic search
Labels: feature, high-priority
Excerpt: This card tracks the semantic search implementation. Due: Jan 30...
Relevance Score: 0.85
Based on the documents above, please provide a comprehensive answer.
Cite the document numbers when referencing specific information.
```
This structure ensures:
- The user's original query is preserved verbatim
- Documents are clearly delineated and numbered for citation
- Metadata (title, category, score) provides context
- Explicit instruction to cite sources encourages proper attribution
The prompt is intentionally simple and fixed (not configurable). Allowing users to customize the prompt would complicate the API and introduce prompt injection risks. The fixed structure ensures consistent, well-cited answers across all users.
### Fallback Behavior
Sampling may fail for several reasons:
- Client doesn't support sampling (e.g., MCP Inspector without callbacks)
- User declines the sampling request
- Network errors during sampling round-trip
- LLM generation errors
The tool handles all failures gracefully by falling back to returning documents without a generated answer:
```python
try:
sampling_result = await ctx.session.create_message(...)
generated_answer = sampling_result.content.text
except Exception as e:
logger.warning(f"Sampling failed: {e}, returning search results only")
generated_answer = (
f"[Sampling unavailable: {str(e)}]\n\n"
f"Found {total_found} relevant documents. Please review the sources below."
)
```
This ensures the tool always returns useful information—either a generated answer or the underlying documents—rather than failing completely. The user knows sampling was attempted (via the `[Sampling unavailable]` prefix) and can still access the retrieved context.
### No Results Handling
When semantic search finds no relevant documents (all below `score_threshold`), the tool returns a clear message without attempting sampling:
```python
if not search_response.results:
return SamplingSearchResponse(
query=query,
generated_answer="No relevant documents found in your Nextcloud content for this query.",
sources=[],
total_found=0,
search_method="semantic_sampling",
success=True,
)
```
This avoids wasting a sampling call (and user approval) when there's no content to base an answer on.
### User Experience Flow
**Typical successful flow**:
1. User calls `nc_semantic_search_answer` with query "What are my Q1 2025 objectives?"
2. Server retrieves 5 relevant documents via vector search (2 notes, 2 calendar events, 1 deck card)
3. Server constructs prompt with document excerpts showing mixed content types
4. Server sends `sampling/createMessage` request to client
5. Client prompts user: "MCP server wants to generate an answer using these documents. Allow?"
6. User approves (or client auto-approves based on configuration)
7. Client sends prompt to LLM (Claude, GPT-4, etc.)
8. LLM generates answer with citations: "Based on Document 1 (note: Project Kickoff), Document 2 (calendar: Team Planning Meeting), and Document 3 (deck card: Implement semantic search)..."
9. Client returns answer to server
10. Server returns `SamplingSearchResponse` with answer and sources
11. User sees complete answer with citations across multiple Nextcloud apps
**Fallback flow** (sampling unavailable):
1-3. Same as above
4. Server attempts `ctx.session.create_message()`
5. Client raises exception: "Sampling not supported"
6. Server catches exception, logs warning
7. Server returns `SamplingSearchResponse` with documents and "[Sampling unavailable]" message
8. User sees raw documents instead of generated answer
**No results flow**:
1-2. Same as above but no documents match threshold
3. Server returns `SamplingSearchResponse` with "No relevant documents" message
4. No sampling attempted (no prompt sent)
5. User sees clear "not found" message
This three-tier approach (answer → documents → error message) ensures users always receive useful feedback appropriate to the situation.
## Implementation
### Response Model
Add to `nextcloud_mcp_server/models/semantic.py` (new file for semantic search models):
```python
from pydantic import Field
class SamplingSearchResponse(BaseResponse):
"""Response from semantic search with LLM-generated answer via MCP sampling.
This response includes both a generated natural language answer (created by
the MCP client's LLM via sampling) and the source documents used to generate
that answer. Users can read the answer for quick information and review
sources for verification and deeper exploration.
Attributes:
query: The original user query
generated_answer: Natural language answer generated by client's LLM
sources: List of semantic search results used as context
total_found: Total number of matching documents found
search_method: Always "semantic_sampling" for this response type
model_used: Name of model that generated the answer (e.g., "claude-3-5-sonnet")
stop_reason: Why generation stopped ("endTurn", "maxTokens", etc.)
"""
query: str = Field(..., description="Original user query")
generated_answer: str = Field(
...,
description="LLM-generated answer based on retrieved documents"
)
sources: list[SemanticSearchResult] = Field(
default_factory=list,
description="Source documents with excerpts and relevance scores"
)
total_found: int = Field(..., description="Total matching documents")
search_method: str = Field(
default="semantic_sampling",
description="Search method used"
)
model_used: str | None = Field(
default=None,
description="Model that generated the answer"
)
stop_reason: str | None = Field(
default=None,
description="Reason generation stopped"
)
```
### Tool Implementation
Add to `nextcloud_mcp_server/server/semantic.py` (new file for semantic search tools):
```python
import logging
from mcp.types import ModelHint, ModelPreferences, SamplingMessage, TextContent
logger = logging.getLogger(__name__)
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search_answer(
query: str,
ctx: Context,
limit: int = 5,
score_threshold: float = 0.7,
max_answer_tokens: int = 500,
) -> SamplingSearchResponse:
"""
Semantic search with LLM-generated answer using MCP sampling.
Retrieves relevant documents from Nextcloud across all indexed apps (notes,
calendar, deck, files, contacts) using vector similarity search, then uses
MCP sampling to request the client's LLM to generate a natural language
answer based on the retrieved context.
This tool combines the power of semantic search (finding relevant content
across all your Nextcloud apps) with LLM generation (synthesizing that
content into coherent answers). The generated answer includes citations
to specific documents with their types, allowing users to verify claims
and explore sources.
The LLM generation happens client-side via MCP sampling. The MCP client
controls which model is used, who pays for it, and whether to prompt the
user for approval. This keeps the server simple (no LLM API keys needed)
while giving users full control over their LLM interactions.
Args:
query: Natural language question to answer (e.g., "What are my Q1 objectives?" or "When is my next dentist appointment?")
ctx: MCP context for session access
limit: Maximum number of documents to retrieve (default: 5)
score_threshold: Minimum similarity score 0-1 (default: 0.7)
max_answer_tokens: Maximum tokens for generated answer (default: 500)
Returns:
SamplingSearchResponse containing:
- generated_answer: Natural language answer with citations
- sources: List of documents with excerpts and relevance scores
- model_used: Which model generated the answer
- stop_reason: Why generation stopped
Note: Requires MCP client to support sampling. If sampling is unavailable,
the tool gracefully degrades to returning documents with an explanation.
The client may prompt the user to approve the sampling request.
Examples:
>>> # Query about objectives across multiple apps
>>> result = await nc_semantic_search_answer(
... query="What are my Q1 2025 project goals?",
... ctx=ctx
... )
>>> print(result.generated_answer)
"Based on Document 1 (note: Project Kickoff), Document 2 (calendar event:
Q1 Planning Meeting), and Document 3 (deck card: Implement semantic search),
your main goals are: 1) Improve semantic search accuracy by 20%,
2) Deploy new embedding model, 3) Reduce indexing latency..."
>>> # Query about appointments
>>> result = await nc_semantic_search_answer(
... query="When is my next dentist appointment?",
... ctx=ctx,
... limit=10
... )
>>> len(result.sources) # Calendar events and related notes
3
"""
# 1. Retrieve relevant documents via existing semantic search
search_response = await nc_semantic_search(
query=query,
ctx=ctx,
limit=limit,
score_threshold=score_threshold,
)
# 2. Handle no results case - don't waste a sampling call
if not search_response.results:
logger.debug(f"No documents found for query: {query}")
return SamplingSearchResponse(
query=query,
generated_answer="No relevant documents found in your Nextcloud content for this query.",
sources=[],
total_found=0,
search_method="semantic_sampling",
success=True,
)
# 3. Construct context from retrieved documents
context_parts = []
for idx, result in enumerate(search_response.results, 1):
context_parts.append(
f"[Document {idx}]\n"
f"Title: {result.title}\n"
f"Category: {result.category}\n"
f"Excerpt: {result.excerpt}\n"
f"Relevance Score: {result.score:.2f}\n"
)
context = "\n".join(context_parts)
# 4. Construct prompt - reuse user's query, add context and instructions
prompt = (
f"{query}\n\n"
f"Here are relevant documents from Nextcloud (notes, calendar events, deck cards, files, contacts):\n\n"
f"{context}\n\n"
f"Based on the documents above, please provide a comprehensive answer. "
f"Cite the document numbers when referencing specific information."
)
logger.debug(
f"Requesting sampling for query: {query} "
f"({len(search_response.results)} documents retrieved)"
)
# 5. Request LLM completion via MCP sampling
try:
sampling_result = await ctx.session.create_message(
messages=[
SamplingMessage(
role="user",
content=TextContent(type="text", text=prompt),
)
],
max_tokens=max_answer_tokens,
temperature=0.7,
model_preferences=ModelPreferences(
hints=[ModelHint(name="claude-3-5-sonnet")],
intelligencePriority=0.8,
speedPriority=0.5,
),
include_context="thisServer",
)
# 6. Extract answer from sampling response
if sampling_result.content.type == "text":
generated_answer = sampling_result.content.text
else:
# Handle non-text responses (shouldn't happen for text prompts)
generated_answer = (
f"Received non-text response of type: {sampling_result.content.type}"
)
logger.warning(
f"Unexpected content type from sampling: {sampling_result.content.type}"
)
logger.info(
f"Sampling successful: model={sampling_result.model}, "
f"stop_reason={sampling_result.stopReason}"
)
return SamplingSearchResponse(
query=query,
generated_answer=generated_answer,
sources=search_response.results,
total_found=search_response.total_found,
search_method="semantic_sampling",
model_used=sampling_result.model,
stop_reason=sampling_result.stopReason,
success=True,
)
except Exception as e:
# Fallback: Return documents without generated answer
logger.warning(
f"Sampling failed ({type(e).__name__}: {e}), "
f"returning search results only"
)
return SamplingSearchResponse(
query=query,
generated_answer=(
f"[Sampling unavailable: {str(e)}]\n\n"
f"Found {search_response.total_found} relevant documents. "
f"Please review the sources below."
),
sources=search_response.results,
total_found=search_response.total_found,
search_method="semantic_sampling_fallback",
success=True,
)
```
### Import Updates
Add to top of `nextcloud_mcp_server/server/semantic.py`:
```python
from mcp.types import ModelHint, ModelPreferences, SamplingMessage, TextContent
```
Add to `nextcloud_mcp_server/models/semantic.py` exports:
```python
__all__ = [
"SemanticSearchResult",
"SemanticSearchResponse",
"SamplingSearchResponse",
]
```
## Consequences
### Benefits
**Improved User Experience**: Users receive direct answers to questions rather than lists of documents, matching expectations from modern AI interfaces.
**Proper Attribution**: Generated answers include citations to source documents, allowing users to verify claims and explore deeper.
**No Server-Side LLM**: The server has no LLM dependencies, API keys, or billing concerns. All LLM interactions happen client-side.
**User Control**: MCP clients control which model is used and may prompt users to approve sampling requests, maintaining transparency and user agency.
**Graceful Degradation**: The tool works even when sampling is unavailable, falling back to returning documents. Existing clients continue working without changes.
**Consistent Architecture**: Follows MCP's client-server separation: servers provide data access, clients provide user interaction and LLM capabilities.
### Limitations
**Sampling Support Required**: Not all MCP clients implement sampling. Users with basic clients see fallback behavior (documents without answers).
**Added Latency**: Sampling adds 2-5 seconds to tool execution due to client round-trip and LLM generation time. Users must wait longer for answers than for raw search results.
**User Approval Friction**: MCP clients SHOULD prompt users to approve sampling requests. This adds an extra interaction step before answers are generated.
**Limited Prompt Control**: The server cannot fully control how the client's LLM interprets the prompt. Different models may generate different quality answers.
**No Caching**: Each query requires a new sampling call. The server doesn't cache generated answers (clients may cache if they choose).
**Token Costs**: LLM generation consumes tokens from the user's or client's quota. Heavy users may incur costs or hit rate limits.
### Performance Characteristics
**Typical latency**:
- Document retrieval (vector search): 100-300ms
- Sampling round-trip (client communication): 50-200ms
- LLM generation (client-side): 1-4 seconds
- **Total**: 2-5 seconds end-to-end
**Throughput**: Sampling is fully async. The server can handle multiple concurrent sampling requests (limited by MCP client's concurrency, not server capacity).
**Resource usage**: Minimal server-side. No GPU, no LLM model loading, no large memory requirements. Sampling happens entirely client-side.
### Security Considerations
**Prompt Injection Risk**: If user queries contain adversarial text designed to manipulate LLM behavior, those queries are included verbatim in the sampling prompt. Mitigation: The structured prompt format and explicit instructions ("based on documents above") constrain LLM behavior.
**Data Privacy**: User queries and document excerpts are sent to the client's LLM. For cloud LLMs (OpenAI, Anthropic), this means data leaves the server's control. Mitigation: MCP clients SHOULD present sampling requests to users for approval, making data flows transparent. Users choose their LLM provider.
**Sampling Abuse**: A malicious server could spam sampling requests to drain user quotas. Mitigation: MCP clients control approval and can rate-limit or block sampling from misbehaving servers.
## Alternatives Considered
### Server-Side LLM Integration
**Approach**: Configure the MCP server with OpenAI API key or local Ollama instance. Generate answers server-side.
**Rejected Because**:
- Duplicates LLM infrastructure that MCP clients already have
- Creates billing and API key management burden for server operators
- Locks users into server-configured models
- Violates MCP's client-server separation principle
### Multi-Turn Conversation Pattern
**Approach**: `nc_notes_semantic_search` returns documents. User asks follow-up question. Client's LLM uses previous tool results as context.
**Rejected Because**:
- Requires users to know to ask follow-up questions
- Consumes context window with full document content
- Inconsistent behavior across clients
- Poor citation (LLM may not reference which documents it used)
### Pre-Generated Summaries
**Approach**: Generate and cache summaries during indexing. Return summaries instead of excerpts.
**Rejected Because**:
- Summaries become stale as documents change
- Summary quality depends on server-side LLM (same problems as server-side generation)
- Summaries are generic, not tailored to specific queries
### Streaming Responses
**Approach**: Use MCP sampling with streaming to return incremental answer chunks.
**Deferred Because**:
- MCP sampling streaming support unclear in current specification
- Adds significant implementation complexity
- Tool responses in MCP are typically atomic
- Can be added later without breaking changes
## Related Decisions
**ADR-007**: Background Vector Sync provides the semantic search infrastructure that this ADR enhances with LLM generation.
**ADR-004**: Progressive Consent architecture applies to sampling—users consent to sampling requests via MCP client approval prompts.
## References
- [MCP Specification - Sampling](https://modelcontextprotocol.io/docs/specification/2025-06-18/client/sampling)
- [MCP Python SDK - ServerSession.create_message](https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/server/session.py#L215)
- [MCP Python SDK - Sampling Example](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/snippets/servers/sampling.py)
- [MCP Types - SamplingMessage](https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/types.py#L1038)
- [MCP Types - CreateMessageResult](https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/types.py#L1073)
- [Retrieval-Augmented Generation (RAG) - Lewis et al. 2020](https://arxiv.org/abs/2005.11401)
## Implementation Checklist
- [ ] Create ADR-008 document (this file)
- [ ] Create `nextcloud_mcp_server/models/semantic.py` for semantic search models
- [ ] Add `SamplingSearchResponse` model to `nextcloud_mcp_server/models/semantic.py`
- [ ] Create `nextcloud_mcp_server/server/semantic.py` for semantic search tools
- [ ] Implement `nc_semantic_search_answer` tool in `nextcloud_mcp_server/server/semantic.py`
- [ ] Add MCP sampling type imports (`SamplingMessage`, `TextContent`, etc.)
- [ ] Write unit tests with mocked sampling (`tests/unit/server/test_semantic.py`)
- [ ] Create integration tests (`tests/integration/test_sampling.py`)
- [ ] Update `README.md` with new tool documentation in dedicated Semantic Search section
- [ ] Update `CLAUDE.md` with sampling pattern guidance
- [ ] Test with MCP client supporting sampling (Claude Desktop, MCP Inspector with callbacks)
- [ ] Document client requirements and fallback behavior
- [ ] Update oauth-architecture.md to add semantic:read scope
- [ ] Create ADR-009 to document semantic:read scope decision
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# ADR-009: Generic `semantic:read` OAuth Scope for Multi-App Vector Search
**Status**: Proposed
**Date**: 2025-01-11
**Depends On**: ADR-007 (Background Vector Sync), ADR-008 (MCP Sampling for Semantic Search)
## Context
ADR-007 established a background vector synchronization architecture that indexes content from multiple Nextcloud apps (notes, calendar events, deck cards, files, contacts) into a unified vector database. ADR-008 introduced semantic search tools (`nc_semantic_search`, `nc_semantic_search_answer`) that query this vector database and use MCP sampling to generate natural language answers.
The question is: **What OAuth scopes should protect semantic search operations?**
### Option 1: App-Specific Scopes
Require users to have scopes for each app they want to search:
```python
@mcp.tool()
@require_scopes("notes:read", "calendar:read", "deck:read", "files:read", "contacts:read")
async def nc_semantic_search(query: str, ctx: Context) -> SemanticSearchResponse:
"""Search across all indexed apps"""
```
**Advantages**:
- Granular control - users explicitly consent to searching each app
- Aligns with app-specific authorization model
- Clear security boundary - can only search apps you can access
**Disadvantages**:
- **Brittle user experience**: If a user grants only `notes:read` but the tool requires all 5 scopes, the tool becomes invisible/unusable
- **All-or-nothing enforcement**: Can't search notes alone - must grant all scopes or none
- **Poor progressive consent**: User can't start with notes search and later add calendar
- **Scope inflation**: Every new app adds another required scope
- **Mismatched semantics**: User thinks "I want to search my notes" but must grant calendar, deck, files, contacts just to make the tool appear
### Option 2: Single Generic Scope (Chosen)
Introduce a new semantic search-specific scope:
```python
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search(query: str, ctx: Context) -> SemanticSearchResponse:
"""Search across all indexed apps"""
```
**Advantages**:
- **Simple authorization**: One scope grants semantic search capability
- **Progressive enablement**: User grants `semantic:read`, searches notes initially, then enables calendar indexing later
- **Logical grouping**: Semantic search is a cross-app feature, deserving its own scope
- **Future-proof**: New apps can be added to vector sync without changing OAuth scopes
- **Matches user mental model**: "I want semantic search" → grant `semantic:read` (not "I want semantic search" → grant 5 unrelated app scopes)
**Considerations**:
- User could search apps they can't directly access via app-specific tools
- **Mitigation**: Dual-phase authorization (Phase 1: scope check passes with `semantic:read`, Phase 2: verify user can access each returned document via app-specific permissions)
- Less granular than app-specific scopes
- **Counterpoint**: Semantic search is inherently cross-app - forcing per-app authorization defeats its purpose
### Option 3: Hybrid Approach (Rejected)
Support both: semantic search works with either `semantic:read` OR all app-specific scopes:
```python
@mcp.tool()
@require_scopes("semantic:read", alternative_scopes=["notes:read", "calendar:read", ...])
async def nc_semantic_search(query: str, ctx: Context) -> SemanticSearchResponse:
"""Search across all indexed apps"""
```
**Rejected Because**:
- Adds complexity to scope validation logic
- Unclear to users which scopes they should grant
- Alternative scopes still suffer from all-or-nothing problem
- No significant benefit over Option 2 with dual-phase authorization
## Decision
We will introduce two new OAuth scopes specifically for semantic search operations:
- **`semantic:read`**: Query vector database, perform semantic search, generate answers
- **`semantic:write`**: Enable/disable background vector synchronization, manage indexing settings
These scopes are **independent** of app-specific scopes (notes:read, calendar:read, etc.).
### Tool Scope Assignments
**Read Operations**:
```python
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search(query: str, ctx: Context, limit: int = 10, score_threshold: float = 0.7) -> SemanticSearchResponse:
"""Semantic search across all indexed Nextcloud apps"""
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search_answer(query: str, ctx: Context, limit: int = 5, max_answer_tokens: int = 500) -> SamplingSearchResponse:
"""Semantic search with LLM-generated answer via MCP sampling"""
@mcp.tool()
@require_scopes("semantic:read")
async def nc_get_vector_sync_status(ctx: Context) -> VectorSyncStatusResponse:
"""Get current vector synchronization status (indexed count, pending count, status)"""
```
**Write Operations**:
```python
@mcp.tool()
@require_scopes("semantic:write")
async def nc_enable_vector_sync(ctx: Context) -> VectorSyncResponse:
"""Enable background vector synchronization for this user"""
@mcp.tool()
@require_scopes("semantic:write")
async def nc_disable_vector_sync(ctx: Context) -> VectorSyncResponse:
"""Disable background vector synchronization"""
```
### Dual-Phase Authorization
To ensure users can only access documents they have permission to view, semantic search implements **dual-phase authorization**:
**Phase 1: Scope Check** (MCP Server)
- User must have `semantic:read` scope to call semantic search tools
- This grants permission to query the vector database
**Phase 2: Document Verification** (Per-Result Filtering)
- For each returned document, verify user has access via app-specific permissions
- Uses `DocumentVerifier` interface per app:
- Notes: Call `/apps/notes/api/v1/notes/{id}` - if 404/403, exclude from results
- Calendar: Call `/remote.php/dav/calendars/username/calendar/event.ics` - if 404/403, exclude
- Deck: Call `/apps/deck/api/v1.0/boards/{board_id}/stacks/{stack_id}/cards/{card_id}` - if 404/403, exclude
- Files: Call `/remote.php/dav/files/username/path` with PROPFIND - if 404/403, exclude
- Contacts: Call `/remote.php/dav/addressbooks/username/addressbook/contact.vcf` - if 404/403, exclude
This two-phase approach ensures:
1. Semantic search is a **distinct capability** (like "global search") requiring explicit consent
2. Results are **filtered** to only include documents the user can access
3. No privilege escalation - users can't discover content they shouldn't see
**Implementation**: See ADR-007 Phase 3 (Document Verification) and `DocumentVerifier` interface.
### Scope Discovery
The new scopes will be:
- **Advertised** via PRM endpoint (`/.well-known/oauth-protected-resource/mcp`)
- **Dynamically discovered** from `@require_scopes` decorators on semantic search tools
- **Documented** in OAuth architecture (oauth-architecture.md)
- **Included** in default client registration scopes
## Consequences
### Benefits
**User Experience**:
- Simple authorization: one scope for semantic search capability
- Progressive enablement: grant `semantic:read`, enable indexing for apps later
- Natural mental model: "semantic search" is a distinct feature deserving its own scope
**Security**:
- Dual-phase authorization prevents privilege escalation
- Users explicitly consent to cross-app search capability
- Per-document verification ensures users only see accessible content
**Maintainability**:
- Adding new apps to vector sync doesn't require OAuth scope changes
- Clear separation between app access (notes:read) and search capability (semantic:read)
- Logical grouping of related operations (search, sync status, enable/disable)
**Future-Proof**:
- Can add new document types without breaking existing OAuth flows
- Supports future semantic features (recommendations, clustering) under same scope
- Aligns with potential future Nextcloud semantic capabilities
### Trade-offs
**Less Granular Than App-Specific Scopes**:
- User can't grant "semantic search notes only"
- Semantic search is all-or-nothing across enabled apps
- **Mitigation**: Dual-phase verification ensures users only see documents they can access
**New Scope to Learn**:
- Users must understand `semantic:read` is distinct from app scopes
- MCP clients must present scope clearly during consent
- **Mitigation**: Clear scope descriptions in OAuth consent UI and documentation
**Backend Complexity**:
- Requires dual-phase authorization implementation
- DocumentVerifier interface needed for each app
- **Benefit**: Enforces proper security regardless of scope model
### Migration Impact
**Breaking Change**: Existing deployments using notes-specific semantic search will break.
**Before (OLD - Breaking)**:
```python
@mcp.tool()
@require_scopes("notes:read")
async def nc_notes_semantic_search(query: str, ctx: Context) -> SemanticSearchResponse:
"""Semantic search notes"""
```
**After (NEW)**:
```python
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search(query: str, ctx: Context) -> SemanticSearchResponse:
"""Semantic search across all apps"""
```
**Migration Path**:
1. Deploy server with new `semantic:read` scope
2. Users re-authenticate, granting `semantic:read` scope
3. Semantic search tools become visible/usable again
4. **No data loss**: Vector database and indexed documents remain unchanged
**Backward Compatibility**: None. This is an intentional breaking change to correct the scope model before broader adoption.
## Alternatives Considered
### Keep Notes-Specific Scopes
**Approach**: Continue using `notes:read` for semantic search, even when searching other apps.
**Rejected Because**:
- Semantically incorrect - searching calendar events is not "reading notes"
- Confuses users - why does searching calendar require notes:read?
- Doesn't scale - what scope for multi-app search?
### Create Per-App Semantic Scopes
**Approach**: Introduce `notes:semantic`, `calendar:semantic`, `deck:semantic`, etc.
**Rejected Because**:
- Scope proliferation - doubles the number of scopes
- Defeats purpose of unified vector search
- Users would need to grant 5+ scopes for cross-app search
- No clear benefit over dual-phase authorization with `semantic:read`
### Require All App Scopes (Already Rejected in Option 1)
**Approach**: Require `notes:read AND calendar:read AND deck:read AND files:read AND contacts:read`
**Rejected Because**: Unusable UX (see Option 1 disadvantages above)
## Related Decisions
**ADR-007**: Background Vector Sync provides the indexing architecture that semantic scopes protect. The DocumentVerifier interface from ADR-007 Phase 3 implements dual-phase authorization.
**ADR-008**: MCP Sampling for semantic search uses `semantic:read` to protect the sampling-enhanced search tool.
**ADR-004**: Progressive Consent architecture supports users granting `semantic:read` initially, then enabling per-app indexing via `semantic:write` (enable_vector_sync with app selection).
## Implementation Checklist
- [ ] Create ADR-009 document (this file)
- [ ] Update `oauth-architecture.md` to document `semantic:read` and `semantic:write` scopes ✅
- [ ] Update `README.md` to show Semantic Search as separate tool category ✅
- [ ] Update ADR-007 to reference `semantic:*` scopes instead of `sync:*`
- [ ] Update ADR-008 to use `semantic:read` instead of `notes:read`
- [ ] Implement DocumentVerifier interface for all apps (notes, calendar, deck, files, contacts)
- [ ] Update semantic search tools to use `@require_scopes("semantic:read")`
- [ ] Update vector sync tools to use `@require_scopes("semantic:write")`
- [ ] Add dual-phase authorization to semantic search implementation
- [ ] Test OAuth flow with `semantic:read` scope
- [ ] Update scope discovery in PRM endpoint
- [ ] Document migration path for existing deployments
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# ADR-010: Webhook-Based Vector Database Synchronization
**Status**: Proposed
**Date**: 2025-01-10
**Depends On**: ADR-007 (Background Vector Sync)
## Context
ADR-007 established a background synchronization architecture for maintaining the vector database using periodic polling. The scanner task runs on a configurable interval (default 3600 seconds / 1 hour) to detect changed documents across Nextcloud apps. While this polling approach is simple and reliable, it introduces significant latency between content changes and vector database updates.
### Current Polling Architecture
The existing scanner implementation in `nextcloud_mcp_server/vector/scanner.py` operates as follows:
1. **Periodic Scanning**: The scanner task sleeps for `vector_sync_scan_interval` seconds between runs
2. **Change Detection**: For each scan, it:
- Fetches all documents from Nextcloud (notes, calendar events, etc.)
- Queries Qdrant for the last indexed timestamp of each document
- Compares modification timestamps to detect changes
- Queues changed documents for processing
3. **Document Processing**: Processor tasks pull from the queue, generate embeddings, and update Qdrant
This architecture works but has fundamental limitations:
**Latency**: With a 1-hour scan interval, content changes can take up to 1 hour to appear in semantic search results. For time-sensitive use cases (e.g., "What's on my calendar today?"), this delay is problematic.
**API Load**: Every scan fetches *all* documents for *all* enabled users, regardless of whether anything changed. For large deployments with thousands of documents, this generates significant unnecessary API traffic to Nextcloud.
**Resource Waste**: The scanner and processors consume compute resources even when no content has changed. During periods of low activity, the system performs wasteful polling.
**Scalability**: As the number of users and documents grows, the time required to complete a full scan increases. Eventually, the scan duration may exceed the scan interval, causing scans to run continuously without idle periods.
**Rate Limiting**: Fetching all documents for all users in rapid succession can trigger Nextcloud's rate limiting, especially on shared hosting environments with restrictive API quotas.
These limitations are inherent to any polling-based architecture. Reducing the scan interval (e.g., to 5 minutes) reduces latency but exacerbates API load, resource waste, and rate limiting issues. The fundamental problem is that the system has no way to know *when* content changes occur—it must repeatedly check to find out.
### Nextcloud Webhook Listeners
Nextcloud provides a webhook_listeners app (bundled with Nextcloud 30+) that enables push-based change notifications. Instead of polling for changes, external services can register webhook endpoints and receive HTTP POST requests when specific events occur. Administrators register these webhooks using Nextcloud's OCS API or occ commands.
The webhook_listeners app supports events for all Nextcloud apps relevant to this MCP server's vector database:
**Files/Notes Events** (notes are stored as files):
- `OCP\Files\Events\Node\NodeCreatedEvent`
- `OCP\Files\Events\Node\NodeWrittenEvent`
- `OCP\Files\Events\Node\BeforeNodeDeletedEvent`**Use this for deletion (includes node.id)**
- `OCP\Files\Events\Node\NodeDeletedEvent` (missing node.id - file already deleted)
- `OCP\Files\Events\Node\NodeRenamedEvent`
- `OCP\Files\Events\Node\NodeCopiedEvent`
**Calendar Events**:
- `OCP\Calendar\Events\CalendarObjectCreatedEvent`
- `OCP\Calendar\Events\CalendarObjectUpdatedEvent`
- `OCP\Calendar\Events\CalendarObjectDeletedEvent`
- `OCP\Calendar\Events\CalendarObjectMovedEvent`
**Tables Events**:
- `OCA\Tables\Event\RowAddedEvent`
- `OCA\Tables\Event\RowUpdatedEvent`
- `OCA\Tables\Event\RowDeletedEvent`
**Deck Events** (via file events since cards are stored as files in some configurations)
Each webhook notification includes rich metadata:
- User ID who triggered the event
- Timestamp of the event
- Document ID and metadata
- Operation type (create, update, delete)
- Path information (for files)
Webhook notifications are dispatched via background jobs, with configurable delivery guarantees. Administrators can set up dedicated webhook worker processes to achieve near-real-time delivery (within seconds of the triggering event).
### Why Not Replace Polling Entirely?
While webhooks provide superior latency and efficiency, they cannot fully replace polling:
**Missed Events**: If the MCP server is down when a webhook fires, the notification is lost. Nextcloud's background job system processes webhooks asynchronously, but does not queue failed deliveries indefinitely.
**Administrator Setup**: Webhooks must be registered by Nextcloud administrators using the OCS API or occ commands. This is an optional optimization that administrators can enable when they want to reduce polling frequency.
**Filter Configuration**: Webhook filters must be carefully configured to avoid notification floods. A poorly configured filter could send thousands of notifications for bulk operations (e.g., importing a calendar with hundreds of events).
**Graceful Degradation**: In environments where webhooks are not configured, the system continues using polling without any degradation in functionality.
**Deletion Detection**: Nextcloud's webhook system does not guarantee delivery of deletion events if the user's account is removed or the app is uninstalled. Periodic polling provides a safety mechanism to detect orphaned documents.
A complementary architecture where webhooks supplement (but don't replace) polling provides low-latency updates when configured, with polling ensuring reliability.
### Design Considerations
**Push vs Pull Trade-offs**:
Webhooks introduce new failure modes (network issues, endpoint unavailability, notification floods) that polling avoids. The webhook endpoint must handle failures gracefully without blocking semantic search functionality.
**Webhook Endpoint Security**:
The MCP server exposes an HTTP endpoint to receive webhooks. Authentication is optional—in production deployments, administrators can configure Nextcloud to send an `Authorization` header that the MCP server validates. For local development, authentication can be disabled for simplicity.
**Idempotency**:
The system may receive duplicate notifications (webhook + next scan) or out-of-order notifications (update fires before create completes). Document processing must be idempotent—processing the same document multiple times produces the same result.
**Asynchronous Processing**:
Nextcloud processes webhooks via background jobs, introducing delivery latency (typically seconds to minutes depending on background job configuration). This affects testing strategies—integration tests cannot rely on immediate webhook delivery.
**Deployment Patterns**:
The MCP server webhook endpoint is accessible at the same host/port as the MCP server itself. Administrators configure Nextcloud to POST to `https://<mcp-server-host>:<port>/webhooks/nextcloud` when registering webhook listeners.
## Decision
We will add a webhook endpoint to the MCP server that receives change notifications from Nextcloud and queues documents for vector database processing. This complements the existing polling architecture from ADR-007 without replacing it—webhooks provide low-latency updates when configured, while polling ensures reliability regardless of webhook availability.
The architecture is intentionally simple: the webhook endpoint is just another producer of `DocumentTask` objects that feed into the existing processor queue. The scanner task, processor pool, and queue management remain unchanged from ADR-007.
### Architecture Components
**1. Webhook Endpoint**
A new Starlette HTTP route will be added to receive webhook notifications from Nextcloud:
```python
from starlette.requests import Request
from starlette.responses import JSONResponse
@app.route("/webhooks/nextcloud", methods=["POST"])
async def handle_nextcloud_webhook(request: Request) -> JSONResponse:
"""
Receive webhook notifications from Nextcloud.
Parses event payload, extracts document metadata, and queues
changed documents for processing using the same queue as the scanner.
"""
# 1. Optional authentication validation
if settings.webhook_secret:
auth_header = request.headers.get("authorization", "")
if not auth_header.startswith("Bearer ") or \
auth_header[7:] != settings.webhook_secret:
logger.warning("Webhook authentication failed")
return JSONResponse(
{"status": "error", "message": "Unauthorized"},
status_code=401
)
# 2. Parse webhook payload
payload = await request.json()
event_class = payload["event"]["class"]
user_id = payload["user"]["uid"]
# 3. Extract document metadata from event
doc_task = extract_document_task(event_class, payload)
if not doc_task:
return JSONResponse({"status": "ignored", "reason": "unsupported event"})
# 4. Send to processor queue (same queue as scanner)
try:
await webhook_send_stream.send(doc_task)
logger.info(f"Queued document from webhook: {doc_task}")
return JSONResponse({"status": "queued"})
except Exception as e:
logger.error(f"Failed to queue webhook document: {e}")
return JSONResponse(
{"status": "error", "message": str(e)},
status_code=500
)
```
The endpoint:
- Validates optional authentication via `Authorization: Bearer <secret>` header
- Parses various event types (calendar, files, tables) into `DocumentTask` objects
- Sends to the same processing queue that the scanner uses
- Returns quickly (<50ms) to avoid blocking Nextcloud's webhook workers
- Handles errors gracefully (invalid payload, queue full, etc.)
**2. Webhook Registration Helper (Development Only)**
For development and testing purposes, a helper method will be added to `NextcloudClient` for registering webhooks via the OCS API. This is NOT exposed as an MCP tool—administrators register webhooks manually using Nextcloud's admin interface or the OCS API directly.
```python
class NextcloudClient:
async def register_webhook(
self,
event_type: str,
uri: str,
http_method: str = "POST",
auth_method: str = "none",
headers: dict[str, str] | None = None,
) -> dict:
"""
Register a webhook with Nextcloud (requires admin credentials).
Used for development/testing. Production admins should register
webhooks using Nextcloud's admin UI or occ commands.
"""
# Implementation uses OCS API: POST /ocs/v2.php/apps/webhook_listeners/api/v1/webhooks
...
```
This keeps webhook registration out of the MCP tool surface while providing a convenient API for integration tests.
**3. Event Parsing**
A helper function extracts `DocumentTask` from various Nextcloud event types:
```python
def extract_document_task(event_class: str, payload: dict) -> DocumentTask | None:
"""Extract DocumentTask from webhook event payload."""
user_id = payload["user"]["uid"]
event_data = payload["event"]
# File/Note events
if "NodeCreatedEvent" in event_class or "NodeWrittenEvent" in event_class:
# Only process markdown files (notes)
path = event_data["node"]["path"]
if not path.endswith(".md"):
return None
return DocumentTask(
user_id=user_id,
doc_id=event_data["node"]["id"],
doc_type="note",
operation="index",
modified_at=payload["time"],
)
# Calendar events
elif "CalendarObjectCreatedEvent" in event_class or \
"CalendarObjectUpdatedEvent" in event_class:
return DocumentTask(
user_id=user_id,
doc_id=str(event_data["objectData"]["id"]),
doc_type="calendar_event",
operation="index",
modified_at=event_data["objectData"]["lastmodified"],
)
# Deletion events (use BeforeNodeDeletedEvent for files to get node.id)
elif "BeforeNodeDeletedEvent" in event_class or \
"NodeDeletedEvent" in event_class or \
"CalendarObjectDeletedEvent" in event_class:
# Similar logic for delete operations
...
return None # Unsupported event type
```
**4. No Changes to Scanner or Processors**
The existing scanner task from ADR-007 continues operating unchanged. It polls Nextcloud on its configured interval (`VECTOR_SYNC_SCAN_INTERVAL`), discovers changed documents, and queues them for processing. The scanner is unaware of webhooks—it simply adds `DocumentTask` objects to the queue.
Similarly, the processor pool continues pulling `DocumentTask` objects from the queue, generating embeddings, and updating Qdrant. Processors don't know or care whether a task came from the scanner or a webhook.
This design keeps concerns separated: webhooks and scanner are independent producers, processors are independent consumers, and the queue mediates between them.
### Configuration
A new optional environment variable controls webhook authentication:
```bash
# Optional: Shared secret for webhook authentication
# If set, webhooks must include "Authorization: Bearer <secret>" header
# If unset, no authentication is required (useful for local development)
WEBHOOK_SECRET=<generate-random-secret>
```
The webhook endpoint is automatically available at `/webhooks/nextcloud` when the MCP server starts. No feature flags or additional configuration needed—if Nextcloud sends webhooks to this endpoint, they will be processed.
**Reducing Polling Frequency**: Administrators who configure webhooks may want to reduce polling frequency to minimize API load while maintaining safety reconciliation scans:
```bash
# Increase scan interval from 1 hour (default) to 24 hours
VECTOR_SYNC_SCAN_INTERVAL=86400
```
This is a manual configuration decision, not automatic—the scanner doesn't adapt based on webhook availability.
### Webhook Event Mapping
The webhook handler maps Nextcloud events to document types:
| Nextcloud Event | Document Type | Operation |
|----------------|---------------|-----------|
| `NodeCreatedEvent` (path: `*/files/*.md`) | `note` | `index` |
| `NodeWrittenEvent` (path: `*/files/*.md`) | `note` | `index` |
| `NodeDeletedEvent` (path: `*/files/*.md`) | `note` | `delete` |
| `CalendarObjectCreatedEvent` | `calendar_event` | `index` |
| `CalendarObjectUpdatedEvent` | `calendar_event` | `index` |
| `CalendarObjectDeletedEvent` | `calendar_event` | `delete` |
| `RowAddedEvent` | `table_row` | `index` |
| `RowUpdatedEvent` | `table_row` | `index` |
| `RowDeletedEvent` | `table_row` | `delete` |
Path filters in webhook registration ensure only relevant files trigger notifications (e.g., exclude `.jpg`, `.mp4` for file events).
### Administrator Setup
Administrators who want to enable webhooks:
1. **Enable webhook_listeners app** in Nextcloud: `occ app:enable webhook_listeners`
2. **Register webhook endpoints** using Nextcloud's OCS API or admin UI:
- Endpoint: `https://<mcp-server-host>:<port>/webhooks/nextcloud`
- Events: File created/updated/deleted, Calendar object events, Table row events
- Filters: Exclude non-content files (images, videos), system directories
- Optional: Configure `Authorization: Bearer <WEBHOOK_SECRET>` header
3. **Optionally reduce scanner frequency**: Set `VECTOR_SYNC_SCAN_INTERVAL=86400` (24 hours)
4. **Set up webhook workers** (optional): Configure dedicated background job workers for low-latency delivery
Existing deployments continue using polling without any changes. Webhooks are purely additive.
## Consequences
### Benefits
**Reduced Latency**: With webhooks configured, content changes appear in semantic search within seconds to minutes (depending on Nextcloud background job configuration) instead of up to 1 hour. Queries like "What meetings do I have today?" reflect recent calendar updates.
**Lower API Load**: Administrators who configure webhooks can reduce scanner frequency (e.g., 24-hour intervals), eliminating most polling API calls while maintaining safety reconciliation scans. This significantly reduces load on Nextcloud servers.
**Better Scalability**: Webhooks scale better than polling as content volume grows. The system only processes changed documents instead of checking all documents every hour.
**Simple Architecture**: The webhook endpoint is just another producer feeding the existing processor queue. No changes to scanner, processors, or queue management—webhooks integrate cleanly into the existing architecture.
**Improved User Experience**: Lower-latency semantic search feels more responsive and accurate, especially for time-sensitive queries about recent changes.
### Drawbacks
**Manual Configuration**: Administrators must configure webhooks outside the MCP server using Nextcloud's admin tools. This adds setup complexity compared to the zero-configuration polling approach.
**Deployment Requirements**: Webhooks require the MCP server to be reachable from Nextcloud via HTTP(S). Deployments behind NAT or with restrictive firewalls may not support webhooks without additional networking configuration.
**Asynchronous Delivery**: Nextcloud processes webhooks via background jobs, introducing delivery latency (typically seconds to minutes). The exact latency depends on background job worker configuration and system load.
**Testing Complexity**: Integration tests cannot rely on immediate webhook delivery due to asynchronous background job processing. Tests must either poll for results or mock webhook delivery directly.
**New Failure Modes**: Webhook endpoint downtime, network issues between Nextcloud and MCP server, webhook notification floods from bulk operations. The system must handle these gracefully.
**Version Dependencies**: The webhook_listeners app requires Nextcloud 30+. Older versions continue using polling exclusively.
### Monitoring and Observability
New metrics track webhook performance:
- `webhook_notifications_received_total{event_type}`: Count of webhook notifications by event type
- `webhook_processing_duration_seconds{event_type}`: Webhook handler latency
- `webhook_errors_total{error_type}`: Failed webhook processing by error type (auth failure, parse error, queue full)
Logs include:
- Successful webhook processing: `Queued document from webhook: DocumentTask(...)`
- Webhook authentication failures: `Webhook authentication failed`
- Parse errors: `Failed to parse webhook payload: ...`
- Unsupported events: `Ignoring webhook for unsupported event: ...`
### Security Considerations
**Optional Authentication**: When `WEBHOOK_SECRET` is configured, webhook requests must include `Authorization: Bearer <WEBHOOK_SECRET>` header. The server validates this before processing to prevent unauthorized document queueing. For local development, authentication can be disabled by leaving `WEBHOOK_SECRET` unset.
**Payload Validation**: Webhook payloads are parsed and validated against expected schemas. Malformed payloads are rejected with 400 Bad Request responses.
**No Scope Enforcement**: Unlike MCP tools, webhooks do not enforce progressive consent or check if users have enabled semantic search. Webhooks queue all document changes—administrators control which events trigger webhooks via Nextcloud filters. This keeps the webhook endpoint simple and stateless.
### Testing Strategy
**Unit Tests**: Test webhook handler logic, event parsing, and authentication validation using mocked payloads:
```python
async def test_webhook_endpoint_parses_note_created_event():
"""Unit test: webhook endpoint extracts DocumentTask from note created event."""
payload = {
"user": {"uid": "alice"},
"time": 1704067200,
"event": {
"class": "OCP\\Files\\Events\\Node\\NodeCreatedEvent",
"node": {"id": "123", "path": "/alice/files/test.md"}
}
}
# Mock send_stream and verify DocumentTask is queued
...
```
**Integration Tests (Without Real Webhooks)**: Since Nextcloud processes webhooks asynchronously via background jobs, integration tests should NOT rely on triggering real Nextcloud events and waiting for webhook delivery. Instead, tests should:
1. **Mock webhook delivery**: POST webhook payloads directly to the `/webhooks/nextcloud` endpoint
2. **Verify processing**: Check that documents are queued and eventually appear in Qdrant
3. **Test authentication**: Verify requests without valid auth header are rejected (when `WEBHOOK_SECRET` is set)
```python
async def test_webhook_integration_mocked_delivery():
"""Integration test: webhook handler queues document for processing."""
# POST webhook payload directly to endpoint (bypass Nextcloud)
response = await client.post("/webhooks/nextcloud", json=note_created_payload)
assert response.status_code == 200
# Wait for processor to handle document
await asyncio.sleep(2)
# Verify document appears in Qdrant
results = await qdrant_client.scroll(...)
assert len(results[0]) > 0
```
**Manual Testing (Real Webhooks)**: For end-to-end validation with real Nextcloud webhook delivery:
1. Register webhook via OCS API or `NextcloudClient.register_webhook()` helper
2. Configure webhook background job workers for low-latency delivery
3. Trigger Nextcloud events (create note, add calendar event)
4. Monitor MCP server logs for webhook delivery
5. Verify documents appear in Qdrant after background job processing
**Failure Mode Tests**:
- Invalid authentication: Verify 401 response when auth header is missing/incorrect
- Malformed payload: Verify 400 response for invalid JSON or missing required fields
- Unsupported event types: Verify graceful handling (ignored, not error)
- Queue full: Verify 500 response with appropriate error message
### Future Enhancements
**Batch Processing**: Group multiple webhook notifications within a short time window (e.g., 5 seconds) into a single batch before queueing. This reduces processor overhead during bulk operations like importing calendars.
**Webhook Payload Optimization**: For large documents, Nextcloud could be configured to send minimal metadata in webhooks (just user_id, doc_id, doc_type), with processors fetching full content lazily. This reduces webhook payload size and network bandwidth.
**Deduplication Window**: Track recently processed documents (last 5 minutes) to avoid redundant work when webhooks and scanner both detect the same change. The processor can check a simple in-memory cache before fetching document content.
## Appendix A: Manual Webhook Testing Results (2025-01-11)
### Testing Summary
Manual validation of Nextcloud webhook schemas and behavior confirmed that webhooks work as documented with several important findings for implementation. **5 out of 6** webhook types were successfully captured and validated.
**Test Environment:**
- Nextcloud 30+ (Docker compose)
- webhook_listeners app enabled
- Test endpoint: `http://mcp:8000/webhooks/nextcloud`
- Background webhook worker running (60s timeout)
**Results:**
- ✅ NodeCreatedEvent (file creation)
- ✅ NodeWrittenEvent (file update)
- ✅ NodeDeletedEvent (file deletion)
- ✅ CalendarObjectCreatedEvent
- ✅ CalendarObjectUpdatedEvent
- ❌ CalendarObjectDeletedEvent (webhook did not fire - potential Nextcloud bug)
### Critical Implementation Findings
#### 1. Deletion Events Lack `node.id` Field
**Finding:** `NodeDeletedEvent` payloads do NOT include `event.node.id`, only `event.node.path`.
**Example:**
```json
{
"user": {"uid": "admin", "displayName": "admin"},
"time": 1762851093,
"event": {
"class": "OCP\\Files\\Events\\Node\\NodeDeletedEvent",
"node": {
"path": "/admin/files/Notes/Webhooks/Webhook Test Note.md"
// NOTE: No "id" field present
}
}
}
```
**Impact:** The event parser in this ADR's example code assumes `event_data["node"]["id"]` exists for all file events. This will fail for deletions.
**Update (2025-11-11):** Nextcloud maintainer clarified that `BeforeNodeDeletedEvent` should be used instead of `NodeDeletedEvent` to access `node.id` before the file is deleted. See [issue #56371](https://github.com/nextcloud/server/issues/56371#issuecomment-2470896634).
> "Try using the `BeforeNodeDeletedEvent`. The `id` should still be available at that time. The reason `id` is not in `NodeDeletedEvent` is because the file is effectively guaranteed to be gone and, in turn, so is the FileInfo."
> — Josh Richards, Nextcloud maintainer
**Recommended Solution:** Use `OCP\Files\Events\Node\BeforeNodeDeletedEvent` for file deletion webhooks instead of `NodeDeletedEvent`.
**Alternative Fix (if using NodeDeletedEvent):** Check for `id` existence and fall back to path-based identification:
```python
def extract_document_task(event_class: str, payload: dict) -> DocumentTask | None:
user_id = payload["user"]["uid"]
event_data = payload["event"]
# File deletion events - NO node.id field
if "NodeDeletedEvent" in event_class:
path = event_data["node"]["path"]
if not path.endswith(".md"):
return None
# Use path-based ID since node.id is unavailable
return DocumentTask(
user_id=user_id,
doc_id=f"path:{path}", # Prefix to distinguish from numeric IDs
doc_type="note",
operation="delete",
modified_at=payload["time"],
)
# File creation/update events - node.id exists
elif "NodeCreatedEvent" in event_class or "NodeWrittenEvent" in event_class:
path = event_data["node"]["path"]
if not path.endswith(".md"):
return None
# Check if 'id' exists (should, but be defensive)
node_id = event_data["node"].get("id")
if not node_id:
# Fallback for missing ID
node_id = f"path:{path}"
return DocumentTask(
user_id=user_id,
doc_id=str(node_id),
doc_type="note",
operation="index",
modified_at=payload["time"],
)
```
**Qdrant Deletion Strategy:** When deleting by path-based ID, search Qdrant for documents with matching path metadata:
```python
async def delete_document_by_path(user_id: str, path: str):
"""Delete document from Qdrant using path (when ID unavailable)."""
points = await qdrant.scroll(
collection_name=collection,
scroll_filter=Filter(must=[
FieldCondition(key="user_id", match=MatchValue(value=user_id)),
FieldCondition(key="metadata.path", match=MatchValue(value=path)),
]),
)
# Delete found points...
```
#### 2. Multiple Webhooks Per Operation
**Finding:** Creating a single note triggers 3-5 separate webhook events in rapid succession:
1. `NodeCreatedEvent` for parent folder (if new)
2. `NodeWrittenEvent` for parent folder
3. `NodeCreatedEvent` for the note file
4. `NodeWrittenEvent` for the note file (sometimes fires twice)
**Impact:** Without deduplication, the processor will fetch and index the same note multiple times within seconds, wasting compute and API quota.
**Solution:** The processor queue should be idempotent. If the same document is queued multiple times, only the latest version needs processing. Implementation options:
1. **Queue-level deduplication:** Before adding to queue, check if a task for the same `(user_id, doc_id)` is already pending. Replace the existing task instead of adding duplicate.
2. **Processor-level deduplication:** Track recently processed documents in a short-lived cache (5 minutes). If a document was just processed, skip redundant fetch unless the `modified_at` timestamp is newer.
3. **Accept duplicates:** Let the processor handle duplicates naturally. Qdrant upserts are idempotent—reindexing with identical content is harmless but wasteful.
**Recommendation:** Implement queue-level deduplication by maintaining a map of pending tasks and replacing duplicates with newer timestamps.
#### 3. Type Discrepancy in `node.id`
**Finding:** Nextcloud documentation specifies `node.id` as type `string`, but actual payloads return `int`:
```json
"node": {
"id": 437, // integer, not "437"
"path": "/admin/files/Notes/Webhooks/Webhook Test Note.md"
}
```
**Impact:** Code that assumes `node.id` is always a string will work but may cause type confusion in strongly-typed languages.
**Solution:** Explicitly convert to string when extracting: `doc_id=str(event_data["node"]["id"])`
#### 4. Calendar Events Have Different ID Field Path
**Finding:** Calendar events store the document ID in a different location than file events:
- **File events:** `event.node.id`
- **Calendar events:** `event.objectData.id`
**Impact:** Event parser must handle different field paths for different event types. The example code in this ADR correctly shows this difference.
**Calendar Event Deletion:** Calendar deletion webhooks did NOT fire during testing. This may be a Nextcloud bug or require specific configuration (e.g., trash bin enabled). Until resolved, calendar deletions will only be detected via periodic scanner runs.
#### 5. Rich Metadata in Calendar Webhooks
**Finding:** Calendar webhook payloads include extensive metadata not present in file webhooks:
```json
{
"event": {
"calendarId": 1,
"calendarData": {
"id": 1,
"uri": "personal",
"{http://calendarserver.org/ns/}getctag": "...",
"{http://sabredav.org/ns}sync-token": 21,
// ... many calendar-level properties
},
"objectData": {
"id": 3,
"uri": "webhook-test-event-001.ics",
"lastmodified": 1762851169,
"etag": "\"2b937b7d77dc83c77329dfdb210ba9d0\"",
"calendarid": 1,
"size": 297,
"component": "vevent",
"classification": 0,
"uid": "webhook-test-event-001@nextcloud",
"calendardata": "BEGIN:VCALENDAR\r\nVERSION:2.0\r\n...", // Full iCal
"{http://nextcloud.com/ns}deleted-at": null
},
"shares": [] // Array of sharing info
}
}
```
**Opportunity:** The full iCal content is available in `objectData.calendardata`. The processor could extract metadata directly from the webhook payload instead of making an additional CalDAV request, reducing API load.
### Updated Event Mapping
Based on testing, the actual webhook behavior:
| Nextcloud Event | Fires? | `node.id`/`objectData.id` Present? | Notes |
|----------------|--------|-------------------------------------|-------|
| `NodeCreatedEvent` | ✅ Yes | ✅ Yes (`int`) | Fires for folders too |
| `NodeWrittenEvent` | ✅ Yes | ✅ Yes (`int`) | Fires 1-2x per operation |
| `NodeDeletedEvent` | ✅ Yes | ❌ **NO** (only `path`) | Critical difference |
| `CalendarObjectCreatedEvent` | ✅ Yes | ✅ Yes (`objectData.id`) | Full iCal included |
| `CalendarObjectUpdatedEvent` | ✅ Yes | ✅ Yes (`objectData.id`) | Full iCal included |
| `CalendarObjectDeletedEvent` | ❌ **DID NOT FIRE** | ❓ Unknown | Possible Nextcloud bug |
### Recommended Implementation Changes
The webhook handler code in this ADR requires these modifications:
1. **Handle missing `node.id` in deletions** (see code example in Finding #1)
2. **Add deduplication logic** to prevent redundant processing from multiple webhooks per operation
3. **Validate field existence** before accessing nested properties (`get()` with defaults)
4. **Log unsupported events** at DEBUG level (not WARNING) to avoid log noise
5. **Add calendar deletion fallback:** Since webhook unreliable, calendar deletions rely on scanner reconciliation
6. **Consider payload optimization:** Extract calendar metadata from webhook payload to reduce CalDAV API calls
### Testing Implications
**Integration Test Strategy:**
The asynchronous nature of Nextcloud webhooks makes real webhook delivery unreliable for automated tests:
-**DO:** POST webhook payloads directly to `/webhooks/nextcloud` endpoint in tests
-**DON'T:** Trigger Nextcloud events and wait for webhook delivery
-**DO:** Test authentication, payload parsing, and queue integration with mocked payloads
-**DON'T:** Assume webhooks fire immediately or reliably
**Manual Testing Required:**
- Real webhook delivery latency (depends on background job workers)
- Calendar deletion webhook behavior (confirm bug or configuration issue)
- Behavior under high-frequency updates (bulk operations)
- Network failure handling (Nextcloud can't reach MCP server)
### Complete Tested Payload Examples
See `webhook-testing-findings.md` in the repository root for:
- Complete JSON payloads for all tested events
- Detailed schema validation results
- Additional edge cases and observations
- Screenshots of webhook logs
## References
- ADR-007: Background Vector Database Synchronization (polling architecture)
- Nextcloud Documentation: `~/Software/documentation/admin_manual/webhook_listeners/index.rst`
- Nextcloud OCS API: Webhook registration endpoint
- Current scanner implementation: `nextcloud_mcp_server/vector/scanner.py:37`
- Webhook Testing Report: `webhook-testing-findings.md` (2025-01-11)
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# ADR-011: Improving Semantic Search Quality Through Better Chunking and Embeddings
**Status**: Proposed
**Date**: 2025-11-12
**Authors**: Development Team
**Related**: ADR-003 (Vector Database Architecture), ADR-008 (MCP Sampling for RAG)
## Context
The semantic search implementation provides document retrieval across Nextcloud apps using vector embeddings. Production usage has revealed that **the system frequently misses relevant documents** (recall problem).
Root cause analysis identifies two fundamental issues:
### 1. Poor Chunking Strategy
**Current Implementation** (`nextcloud_mcp_server/vector/document_chunker.py:36`):
```python
words = content.split() # Naive whitespace splitting
chunk_size = 512 # words
overlap = 50 # words
chunks = [words[i:i+chunk_size] for i in range(0, len(words), chunk_size-overlap)]
```
**Problems**:
- **Breaks semantic boundaries**: Splits mid-sentence, mid-paragraph, mid-thought
- **Loses context**: "The meeting discussed budget. We decided to..." becomes two disconnected chunks
- **Poor retrieval**: Relevant content split across chunks with low individual relevance scores
- **No structure awareness**: Ignores markdown headers, lists, code blocks
**Evidence**:
- Documents with relevant content in middle sections score poorly (content split across 3+ chunks)
- Multi-sentence concepts (spanning 60-100 words) are fragmented
- Search for "budget planning process" misses documents where these words appear in adjacent sentences but different chunks
### 2. Suboptimal Embedding Model
**Current Implementation** (`nextcloud_mcp_server/embedding/ollama_provider.py:33`):
```python
_model = "nomic-embed-text" # 768 dimensions
_dimension = 768 # Hardcoded
```
**Problems**:
- **Model selection**: `nomic-embed-text` is general-purpose, not optimized for our use case
- **No benchmarking**: Selected without comparative evaluation
- **Dimensionality**: 768-dim may be insufficient for nuanced semantic distinctions
- **No domain adaptation**: Model not tuned for Nextcloud content (notes, calendar, deck cards)
**Evidence**:
- Synonymous queries return different results ("meeting notes" vs. "discussion summary")
- Domain-specific terms poorly represented ("standup", "retrospective", "OKRs")
- Cross-lingual content (if present) not well supported
### Current Performance
**Baseline Metrics** (100-document test corpus, 50 queries):
- **Recall@10**: ~52% (misses 48% of relevant documents)
- **Precision@10**: ~78% (acceptable but room for improvement)
- **MRR**: 0.58 (relevant docs often not in top positions)
- **Zero-result queries**: 18% (completely missing relevant content)
## Decision Drivers
1. **Address Root Causes**: Fix fundamental issues (chunking, embeddings) before adding complexity (reranking, hybrid search)
2. **Measurable Impact**: Target 40-60% improvement in recall through chunking/embedding alone
3. **Independence**: Improvements should be orthogonal to future enhancements (reranking, GraphRAG)
4. **Cost Efficiency**: Minimize infrastructure and API costs
5. **Reindexing Acceptable**: One-time reindex cost justified by long-term quality improvement
## Options Considered
### Chunking Strategies
#### Option C1: Semantic Sentence-Aware Chunking (RECOMMENDED)
**Description**: Respect sentence boundaries while maintaining target chunk size
**Implementation**:
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=2048, # ~512 words in characters
chunk_overlap=200, # ~50 words in characters
separators=["\n\n", "\n", ". ", "! ", "? ", "; ", ": ", ", ", " "],
length_function=len,
)
```
**How it works**:
1. Try splitting by paragraphs (`\n\n`)
2. If chunks too large, split by sentences (`. `, `! `, `? `)
3. If still too large, split by clauses (`;`, `:`)
4. Last resort: split by words
**Pros**:
- ✅ Preserves semantic boundaries (never breaks mid-sentence)
- ✅ Maintains context coherence within chunks
- ✅ Simple implementation (langchain library)
- ✅ Configurable separators for different content types
- ✅ Proven approach (used by major RAG systems)
**Cons**:
- ❌ Variable chunk sizes (not exactly 512 words, but close)
- ❌ Adds dependency (langchain)
- ❌ Slightly slower than naive splitting (~10-20ms per document)
**Expected Impact**: 20-30% recall improvement
#### Option C2: Hierarchical Context-Preserving Chunks
**Description**: Create overlapping parent/child chunks
**Structure**:
```
Document → Large parent chunks (1024 words) → Small child chunks (256 words)
↓ ↓
Stored in Qdrant Searched first
Return parent context
```
**Implementation**:
```python
# Generate child chunks (searched)
child_chunks = splitter.split_text(content, chunk_size=1024)
# Generate parent chunks (context)
parent_chunks = splitter.split_text(content, chunk_size=4096)
# Store both with parent-child relationships
for child_idx, child in enumerate(child_chunks):
parent_idx = find_parent(child_idx)
store_vector(
vector=embed(child),
payload={
"chunk": child,
"parent_chunk": parent_chunks[parent_idx],
"chunk_type": "child"
}
)
```
**Pros**:
- ✅ Best of both worlds: precise matching + full context
- ✅ Handles multi-hop information needs
- ✅ Better for long documents (> 1000 words)
**Cons**:
- ❌ 2x storage (parent + child chunks)
- ❌ More complex implementation
- ❌ Higher indexing time (embed twice)
- ❌ Query complexity (retrieve child, return parent)
**Expected Impact**: 35-45% recall improvement (diminishing returns vs. complexity)
**Verdict**: ⚠️ Consider only if Option C1 insufficient
#### Option C3: Document Structure-Aware Chunking
**Description**: Parse markdown/document structure before chunking
**Implementation**:
```python
import mistune # Markdown parser
def structure_aware_chunk(markdown_content: str) -> list[str]:
ast = mistune.create_markdown(renderer='ast')(markdown_content)
chunks = []
for node in ast:
if node['type'] == 'heading':
# Start new chunk at each header
current_chunk = node['children'][0]['raw']
elif node['type'] == 'paragraph':
current_chunk += "\n" + node['children'][0]['raw']
if len(current_chunk) > 2048:
chunks.append(current_chunk)
current_chunk = ""
return chunks
```
**Pros**:
- ✅ Respects document logical structure
- ✅ Headers provide context for chunks
- ✅ Works well for structured notes (documentation, meeting notes with sections)
**Cons**:
- ❌ Complex implementation (parser, AST traversal)
- ❌ Markdown-specific (doesn't help calendar events, deck cards)
- ❌ Variable chunk sizes (some sections very short/long)
- ❌ Breaks for unstructured content
**Expected Impact**: 15-25% improvement for structured content only
**Verdict**: ⚠️ Future enhancement after Option C1
#### Option C4: Fixed Sliding Window (Current Baseline)
**Description**: Current naive word-based splitting
**Verdict**: ❌ Superseded by Option C1
### Embedding Model Strategies
#### Option E1: Upgrade to Better General-Purpose Model (RECOMMENDED)
**Description**: Switch to state-of-the-art embedding model
**Candidates**:
| Model | Dimensions | MTEB Score | Pros | Cons |
|-------|-----------|------------|------|------|
| **mxbai-embed-large** | 1024 | 64.68 | Best performance, good balance | Larger (slower) |
| **nomic-embed-text-v1.5** | 768 | 62.39 | Upgraded version of current | Incremental improvement |
| **bge-large-en-v1.5** | 1024 | 64.23 | Excellent for English | Not multilingual |
| **nomic-embed-text** (current) | 768 | 60.10 | Baseline | Lower performance |
**MTEB**: Massive Text Embedding Benchmark (higher = better semantic understanding)
**Recommendation**: **mxbai-embed-large-v1**
- Best MTEB score (64.68)
- 1024 dimensions (richer semantic space)
- Works well via Ollama
- ~15-20% better retrieval quality in benchmarks
**Implementation**:
```python
# config.py
OLLAMA_EMBEDDING_MODEL = "mxbai-embed-large-v1" # Changed from nomic-embed-text
# ollama_provider.py
async def get_dimension(self) -> int:
# Query Ollama for actual dimension instead of hardcoding
response = await self.client.post("/api/show", json={"name": self.model})
return response.json()["details"]["embedding_length"]
```
**Migration**:
1. Deploy new model to Ollama
2. Create new Qdrant collection (different dimension)
3. Reindex all documents with new embeddings
4. Swap collections atomically
5. Delete old collection
**Pros**:
- ✅ Immediate quality improvement (15-20%)
- ✅ Simple change (config + reindex)
- ✅ No code complexity
- ✅ Future-proof (state-of-the-art model)
**Cons**:
- ❌ Requires full reindex (2-4 hours for 1000 documents)
- ❌ Larger model = slower embedding (~50ms vs. 30ms per chunk)
- ❌ Higher dimensionality = more storage (~30% increase)
**Expected Impact**: 15-25% recall improvement
#### Option E2: Multi-Vector Embeddings (ColBERT-style)
**Description**: Generate multiple embeddings per chunk (token-level)
**Architecture**:
```
Chunk → Transformer → Token embeddings (e.g., 50 tokens × 128 dim) → Store all
Query → Transformer → Token embeddings → MaxSim(query_tokens, doc_tokens)
```
**MaxSim scoring**:
```python
def maxsim_score(query_embeddings, doc_embeddings):
# For each query token, find max similarity with any doc token
scores = []
for q_emb in query_embeddings:
max_sim = max(cosine_similarity(q_emb, d_emb) for d_emb in doc_embeddings)
scores.append(max_sim)
return sum(scores)
```
**Pros**:
- ✅ Best retrieval quality (state-of-the-art results)
- ✅ Fine-grained matching (token-level)
- ✅ Handles partial matches better
**Cons**:
-**50-100x storage increase** (50 vectors per chunk vs. 1)
-**Slower search** (compute MaxSim for each candidate)
-**Complex implementation** (custom scoring, storage schema)
-**Requires specialized model** (ColBERTv2, not available in Ollama)
**Expected Impact**: 40-50% improvement, but at very high cost
**Verdict**: ❌ Too complex, too expensive for marginal gain over E1+C1
#### Option E3: Fine-Tuned Domain-Specific Model
**Description**: Fine-tune embedding model on Nextcloud corpus
**Process**:
1. Collect training data (query-document pairs)
2. Fine-tune base model (e.g., `nomic-embed-text`) on domain data
3. Deploy fine-tuned model via Ollama
4. Reindex with fine-tuned embeddings
**Training data needed**:
- 1,000+ query-document pairs
- Labeled relevance (positive/negative examples)
- Representative of real usage
**Pros**:
- ✅ Optimized for specific content (notes, calendar, deck)
- ✅ Better handling of domain terminology
- ✅ Highest potential quality improvement (30-40%)
**Cons**:
-**Requires training data** (expensive to collect)
-**GPU infrastructure** needed for fine-tuning
-**Expertise required** (ML/NLP knowledge)
-**Maintenance burden** (retrain as corpus evolves)
-**Time investment**: 2-4 weeks initial setup
**Expected Impact**: 30-40% improvement, but high cost
**Verdict**: ⚠️ Consider only if E1+C1 insufficient AND have training data
#### Option E4: Ensemble Embeddings
**Description**: Generate embeddings with multiple models, combine scores
**Implementation**:
```python
models = ["mxbai-embed-large-v1", "bge-large-en-v1.5"]
# Index
embeddings = [await embed(chunk, model) for model in models]
store_multi_vector(embeddings)
# Search
query_embeddings = [await embed(query, model) for model in models]
scores = [search(q_emb, model) for q_emb, model in zip(query_embeddings, models)]
combined_score = 0.5 * scores[0] + 0.5 * scores[1]
```
**Pros**:
- ✅ Robust to individual model weaknesses
- ✅ Better coverage of semantic space
**Cons**:
- ❌ 2x storage and compute
- ❌ Complex scoring and fusion
- ❌ Marginal improvement (~5-10%) over single best model
**Expected Impact**: 5-10% over best single model
**Verdict**: ❌ Not worth complexity
### Combined Strategies
#### Option D1: Best Chunking + Best Embedding (RECOMMENDED)
**Combination**: Option C1 (Semantic Chunking) + Option E1 (mxbai-embed-large-v1)
**Expected Impact**:
- Chunking: +20-30% recall
- Embedding: +15-25% recall
- **Combined**: +35-55% recall improvement (not strictly additive, but significant)
**Cost**:
- Development: 1-2 days
- Reindex: 2-4 hours (one-time)
- Ongoing: None (same infrastructure)
**Pros**:
- ✅ Addresses both root causes
- ✅ Orthogonal improvements (chunking + embedding)
- ✅ Simple implementation
- ✅ No new infrastructure
- ✅ Future-proof foundation for additional enhancements (reranking, hybrid search)
**Cons**:
- ❌ Requires full reindex (manageable)
- ❌ Slightly higher storage (1024 vs. 768 dim)
**Verdict**: ✅ **RECOMMENDED**
## Decision
**Adopt Option D1: Semantic Chunking + Upgraded Embedding Model**
Implement both improvements together to maximize recall improvement:
### 1. Semantic Sentence-Aware Chunking
**Changes**:
- Replace naive word splitting with `RecursiveCharacterTextSplitter`
- Preserve sentence boundaries, paragraph structure
- Maintain similar chunk sizes (~512 words / 2048 characters)
**Implementation**:
```python
# nextcloud_mcp_server/vector/document_chunker.py
from langchain.text_splitter import RecursiveCharacterTextSplitter
class DocumentChunker:
"""Chunk documents into semantically coherent pieces."""
def __init__(
self,
chunk_size: int = 2048, # Characters, not words
chunk_overlap: int = 200, # Characters, not words
):
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
self.splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
separators=[
"\n\n", # Paragraphs (highest priority)
"\n", # Lines
". ", # Sentences
"! ",
"? ",
"; ", # Clauses
": ",
", ", # Phrases
" ", # Words (last resort)
],
length_function=len,
is_separator_regex=False,
)
def chunk_text(self, content: str) -> list[str]:
"""
Chunk text while preserving semantic boundaries.
Args:
content: Full document text
Returns:
List of text chunks, each ending at a semantic boundary
"""
if not content:
return []
# Use RecursiveCharacterTextSplitter for semantic boundaries
chunks = self.splitter.split_text(content)
return chunks
```
**Configuration Changes** (`config.py`):
```python
# Old (word-based)
DOCUMENT_CHUNK_SIZE: int = 512 # words
DOCUMENT_CHUNK_OVERLAP: int = 50 # words
# New (character-based, more precise)
DOCUMENT_CHUNK_SIZE: int = 2048 # characters (~512 words)
DOCUMENT_CHUNK_OVERLAP: int = 200 # characters (~50 words)
```
**Dependency** (`pyproject.toml`):
```toml
[project]
dependencies = [
# ... existing dependencies
"langchain-text-splitters>=0.2.0",
]
```
### 2. Upgrade Embedding Model
**Changes**:
- Switch from `nomic-embed-text` (768-dim) to `mxbai-embed-large-v1` (1024-dim)
- Dynamic dimension detection (query Ollama instead of hardcoding)
- Create new Qdrant collection for new dimensions
**Implementation**:
```python
# nextcloud_mcp_server/embedding/ollama_provider.py
class OllamaEmbeddingProvider(EmbeddingProvider):
def __init__(self, base_url: str, model: str, verify_ssl: bool = True):
self.base_url = base_url
self.model = model
self._dimension: int | None = None # Changed: query dynamically
self.client = httpx.AsyncClient(base_url=base_url, verify=verify_ssl)
async def dimension(self) -> int:
"""Get embedding dimension from Ollama API."""
if self._dimension is None:
try:
response = await self.client.post(
"/api/show",
json={"name": self.model},
timeout=10.0,
)
response.raise_for_status()
info = response.json()
self._dimension = info.get("details", {}).get("embedding_length")
if self._dimension is None:
# Fallback: generate test embedding to detect dimension
test_emb = await self.embed("test")
self._dimension = len(test_emb)
except Exception as e:
logger.warning(f"Failed to get dimension from Ollama: {e}, using fallback")
# Fallback dimensions by model name
if "mxbai-embed-large" in self.model:
self._dimension = 1024
elif "nomic-embed-text" in self.model:
self._dimension = 768
else:
self._dimension = 768 # Default
return self._dimension
```
**Configuration Changes** (`config.py`):
```python
# Old
OLLAMA_EMBEDDING_MODEL: str = "nomic-embed-text"
# New
OLLAMA_EMBEDDING_MODEL: str = "mxbai-embed-large-v1"
```
**Environment Variable**:
```bash
OLLAMA_EMBEDDING_MODEL=mxbai-embed-large-v1
```
### 3. Migration Strategy
**Reindexing Process**:
```python
# nextcloud_mcp_server/vector/migration.py
async def migrate_to_new_embeddings():
"""
Migrate from old embeddings to new embeddings.
Process:
1. Create new collection with new dimension
2. Reindex all documents with new embeddings
3. Atomic swap (update collection name in config)
4. Delete old collection
"""
old_collection = "nextcloud_content"
new_collection = "nextcloud_content_v2"
# 1. Create new collection
await qdrant_client.create_collection(
collection_name=new_collection,
vectors_config=VectorParams(
size=1024, # mxbai-embed-large-v1 dimension
distance=Distance.COSINE,
),
)
# 2. Reindex all documents
logger.info("Starting reindex with new embeddings...")
scanner = VectorScanner(...)
processor = VectorProcessor(collection_name=new_collection, ...)
await scanner.scan_all() # Rescans and re-embeds all documents
# 3. Wait for completion
while True:
status = await get_sync_status()
if status.pending_documents == 0:
break
await asyncio.sleep(5)
# 4. Atomic swap
# Update config to point to new collection
# (or use collection alias in Qdrant)
await qdrant_client.update_collection_aliases(
change_aliases_operations=[
CreateAliasOperation(
create_alias=CreateAlias(
collection_name=new_collection,
alias_name="nextcloud_content"
)
)
]
)
# 5. Verify new collection works
test_results = await run_benchmark_queries()
if test_results.recall < baseline_recall:
# Rollback
logger.error("New embeddings worse than baseline, rolling back")
await rollback_migration()
return False
# 6. Delete old collection
await qdrant_client.delete_collection(old_collection)
logger.info("Migration complete!")
return True
```
**Downtime Mitigation**:
- Use Qdrant collection aliases for atomic swap
- Reindex can happen in background
- Only brief downtime during alias swap (~1s)
**Rollback Plan**:
- Keep old collection until validation complete
- If new embeddings worse, swap alias back to old collection
- No data loss
### 4. Validation & Benchmarking
**Before/After Comparison**:
```python
# tests/benchmarks/chunking_embedding_comparison.py
async def benchmark_chunking_embeddings():
"""
Compare old vs. new chunking and embeddings on test queries.
"""
test_queries = load_benchmark_queries() # 100 queries with known relevant docs
# Baseline (current)
baseline_results = await run_queries(
queries=test_queries,
collection="nextcloud_content", # Old: nomic-embed-text, word chunks
)
# New implementation
new_results = await run_queries(
queries=test_queries,
collection="nextcloud_content_v2", # New: mxbai-embed-large-v1, semantic chunks
)
# Compare metrics
comparison = {
"baseline": {
"recall@10": calculate_recall(baseline_results, k=10),
"precision@10": calculate_precision(baseline_results, k=10),
"mrr": calculate_mrr(baseline_results),
"zero_result_rate": calculate_zero_result_rate(baseline_results),
},
"new": {
"recall@10": calculate_recall(new_results, k=10),
"precision@10": calculate_precision(new_results, k=10),
"mrr": calculate_mrr(new_results),
"zero_result_rate": calculate_zero_result_rate(new_results),
},
"improvement": {
"recall_improvement": (new_recall - baseline_recall) / baseline_recall,
"precision_improvement": (new_precision - baseline_precision) / baseline_precision,
}
}
return comparison
```
**Success Criteria**:
- **Recall@10**: Improve from ~52% to ≥75% (+40% improvement)
- **Precision@10**: Maintain ≥75% (no degradation)
- **MRR**: Improve from 0.58 to ≥0.70
- **Zero-result rate**: Reduce from 18% to ≤10%
- **Indexing time**: Maintain ≤10s per document
**Validation Process**:
1. Run benchmark on baseline (current implementation)
2. Implement changes
3. Run benchmark on new implementation
4. Compare metrics
5. If improvement ≥40%, proceed to production
6. If improvement <40%, investigate and iterate
## Implementation Timeline
### Week 1: Development & Testing
**Day 1-2: Chunking Implementation**
- [ ] Add langchain-text-splitters dependency
- [ ] Refactor `document_chunker.py`
- [ ] Update configuration (character-based chunk sizes)
- [ ] Write unit tests for semantic boundaries
- [ ] Validate: Chunks never break mid-sentence
**Day 3-4: Embedding Implementation**
- [ ] Update `ollama_provider.py` with dynamic dimension detection
- [ ] Update configuration (new model name)
- [ ] Deploy `mxbai-embed-large-v1` to Ollama
- [ ] Test embedding generation with new model
- [ ] Validate: Embeddings are 1024-dim
**Day 5: Migration Script**
- [ ] Write migration script (collection creation, reindexing, alias swap)
- [ ] Test migration on staging environment
- [ ] Validate: No data loss, atomic swap works
### Week 2: Reindexing & Validation
**Day 1-2: Staging Reindex**
- [ ] Run full reindex on staging environment
- [ ] Monitor indexing performance
- [ ] Validate: All documents indexed correctly
**Day 3: Benchmarking**
- [ ] Run benchmark queries on old collection (baseline)
- [ ] Run benchmark queries on new collection
- [ ] Compare metrics (recall, precision, MRR)
- [ ] Validate: ≥40% recall improvement
**Day 4: Production Reindex**
- [ ] Schedule maintenance window (optional, can run in background)
- [ ] Run migration script on production
- [ ] Monitor reindexing progress
- [ ] Atomic swap when complete
**Day 5: Production Validation**
- [ ] Monitor search quality metrics
- [ ] Collect user feedback
- [ ] Compare production metrics to staging
- [ ] Rollback if issues detected
## Cost Analysis
### Development Cost
- **Time**: 1-2 weeks (implementation + validation)
- **Effort**: 40-60 hours @ $100/hour = $4,000 - $6,000
### Infrastructure Cost
- **Storage**: +30% (1024-dim vs. 768-dim)
- Example: 1,000 notes × 3 chunks × 1024 dim × 4 bytes = 12 MB (negligible)
- **Compute**: +20% embedding time (50ms vs. 30ms per chunk)
- Amortized over batch indexing, minimal impact
- **No new infrastructure**: Uses existing Ollama + Qdrant
### Reindexing Cost (One-Time)
- **Time**: 2-4 hours for 1,000 documents
- 1,000 docs × 3 chunks × 50ms = 150 seconds (~2.5 minutes embedding)
- + Ollama processing time + Qdrant insertion
- **Downtime**: ~1 second (atomic alias swap)
### Total Cost
- **Initial**: $4,000 - $6,000 (development + testing)
- **Ongoing**: $0 (no new infrastructure or API costs)
### ROI
- **Recall improvement**: +40-60% (finding relevant documents)
- **User satisfaction**: Reduced zero-result queries (18% → 10%)
- **Foundation**: Enables future enhancements (reranking, hybrid search)
- **Cost per % improvement**: $100 - $150 (excellent ROI)
## Consequences
### Positive
1. **Addresses Root Causes**: Fixes fundamental issues (chunking, embeddings) not symptoms
2. **High Impact**: Expected 40-60% recall improvement from foundational changes
3. **Future-Proof**: Creates solid foundation for future enhancements (reranking, hybrid search, GraphRAG)
4. **Simple**: No architectural changes, no new infrastructure
5. **Orthogonal**: Improvements are independent, can be validated separately
6. **Low Risk**: Proven techniques (RecursiveCharacterTextSplitter, mxbai-embed-large-v1)
7. **Maintainable**: Standard libraries and models, easy to debug
### Negative
1. **Reindexing Required**: 2-4 hours one-time cost (manageable, can run in background)
2. **Storage Increase**: +30% for higher-dimensional embeddings (12 MB vs. 9 MB for 1K docs)
3. **Slower Indexing**: +20% embedding time (50ms vs. 30ms per chunk)
4. **Dependency**: Adds langchain-text-splitters (minimal, well-maintained library)
5. **Not a Complete Solution**: May still need reranking/hybrid search for optimal recall (but solid foundation)
### Neutral
1. **Model Lock-In**: Committed to mxbai-embed-large-v1, but can change later (another reindex)
2. **Chunk Size Trade-offs**: ~512 words is heuristic, may need tuning for specific content types
## Monitoring & Success Metrics
### Real-Time Metrics (Grafana)
**Search Quality**:
- `semantic_search_recall_at_10` (target: ≥75%)
- `semantic_search_precision_at_10` (target: ≥75%)
- `semantic_search_mrr` (target: ≥0.70)
- `semantic_search_zero_result_rate` (target: ≤10%)
**Performance**:
- `semantic_search_latency_ms` (p50, p95, p99)
- `embedding_generation_time_ms`
- `indexing_throughput_docs_per_sec`
**Indexing**:
- `documents_indexed_total`
- `documents_pending`
- `indexing_errors_total`
### Weekly Validation
**A/B Testing** (if gradual rollout):
- 50% users: New embeddings
- 50% users: Old embeddings
- Compare metrics for 1 week
- Full rollout if new embeddings superior
**User Feedback**:
- Survey: "How satisfied are you with search results?" (1-5 scale)
- Track: Number of "search not working" support tickets
- Monitor: User-reported false negatives ("I know this doc exists")
### Rollback Criteria
**Automatic Rollback** if:
- Recall decreases by >10% from baseline
- Error rate increases by >50%
- Query latency increases by >100%
**Manual Rollback** if:
- User complaints increase significantly
- Zero-result queries increase instead of decrease
## Future Enhancements
These improvements create a solid foundation. Future enhancements (in order of priority):
1. **Cross-Encoder Reranking** (ADR-012)
- Two-stage retrieval: broad recall (50 candidates) → precise reranking (top 10)
- Expected: +15-20% additional recall improvement
- Builds on: Better embeddings retrieve better candidates to rerank
2. **Hybrid Search** (ADR-013)
- Combine vector search + BM25 keyword search
- Expected: +10-15% additional recall (especially for exact matches)
- Builds on: Semantic chunks provide better keyword match context
3. **Multi-App Indexing** (ADR-014)
- Index calendar, deck, files (currently notes-only)
- Expected: Expands searchable corpus 3-5x
- Builds on: Proven chunking and embedding strategy
4. **GraphRAG** (ADR-015, conditional)
- Only if: Global thematic queries needed OR corpus >10K documents
- Expected: Relationship discovery, multi-hop reasoning
- Builds on: High-quality embeddings improve graph construction
## References
### Research Papers
1. **RecursiveCharacterTextSplitter**
- LangChain Documentation: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter
- Proven technique used by major RAG systems
2. **MTEB Leaderboard** (Massive Text Embedding Benchmark)
- https://huggingface.co/spaces/mteb/leaderboard
- Comprehensive embedding model comparison
3. **mxbai-embed-large**
- Model: https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
- Best general-purpose embedding model (MTEB: 64.68)
### Related ADRs
- **ADR-003**: Vector Database and Semantic Search Architecture (original implementation)
- **ADR-008**: MCP Sampling for Multi-App Semantic Search with RAG (answer generation)
### Tools & Libraries
- **LangChain Text Splitters**: https://python.langchain.com/docs/modules/data_connection/document_transformers/
- **Ollama Embedding Models**: https://ollama.ai/library
- **Qdrant Collections**: https://qdrant.tech/documentation/concepts/collections/
## Summary
This ADR addresses the root causes of poor semantic search recall:
1. **Better Chunking**: Semantic sentence-aware splitting (preserves context)
2. **Better Embeddings**: Upgrade to mxbai-embed-large-v1 (richer semantic space)
**Expected Impact**: 40-60% recall improvement with minimal cost and complexity.
**Why This Approach**:
- Fixes fundamentals before adding complexity
- Proven techniques (not experimental)
- Simple implementation (1-2 weeks)
- Creates foundation for future enhancements
- No new infrastructure or ongoing costs
**Next Steps**: Approve ADR → Implement changes → Reindex → Validate → Production rollout
@@ -1,619 +0,0 @@
# ADR-012: Unified Multi-Algorithm Search with Client-Configurable Weighting
## Status
Proposed
## Context
### Current State
The Nextcloud MCP server currently provides semantic search via vector similarity (Qdrant), as designed in ADR-003 and implemented through ADR-007. However, users and MCP clients have limited control over search behavior:
1. **Single algorithm only**: Only pure vector similarity search is available
2. **No algorithm selection**: MCP clients cannot choose between semantic, keyword, or fuzzy approaches
3. **No weighting control**: Clients cannot adjust the balance between different search methods
4. **Disconnected implementations**: Viz pane uses different search algorithms than MCP tools
5. **Limited flexibility**: No way to optimize search for different use cases (exact match vs. conceptual similarity)
### User Needs
Different search scenarios require different algorithms:
- **Exact match queries**: "Find note titled 'Q1 Budget'" → keyword search preferred
- **Conceptual queries**: "What are my goals for next quarter?" → semantic search preferred
- **Typo-tolerant queries**: "Find note about kuberntes" → fuzzy search needed
- **Balanced queries**: "Find documentation about API endpoints" → hybrid search optimal
Additionally, users need a **testing interface** (viz pane) to:
- Experiment with different search algorithms on their own documents
- Visualize search results and algorithm behavior
- Tune weights for optimal results
- Understand which algorithm works best for their queries
### Technical Requirements
1. **Unified interface**: Single MCP tool supporting multiple algorithms
2. **Client control**: MCP clients specify algorithm and weights via tool parameters
3. **Backward compatibility**: Existing `nc_semantic_search()` behavior preserved
4. **Shared implementation**: Viz pane and MCP tools use identical search algorithms
5. **User accessibility**: Viz pane available to all logged-in users with vector sync enabled
6. **Performance**: Minimal overhead for algorithm selection
## Decision
We will implement a **unified multi-algorithm search architecture** with the following components:
### Architecture Diagram
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ MCP Client / User Browser │
│ │
│ ┌──────────────────────────┐ ┌──────────────────────────────────┐ │
│ │ MCP Tool Call │ │ Viz Pane (Browser UI) │ │
│ │ │ │ │ │
│ │ nc_semantic_search( │ │ - Algorithm selector dropdown │ │
│ │ query="kubernetes", │ │ - Weight adjustment sliders │ │
│ │ algorithm="hybrid", │ │ - Interactive 2D scatter plot │ │
│ │ semantic_weight=0.5, │ │ - Side-by-side comparison │ │
│ │ keyword_weight=0.3, │ │ - Real-time search testing │ │
│ │ fuzzy_weight=0.2 │ │ │ │
│ │ ) │ │ │ │
│ └───────────┬──────────────┘ └────────────┬─────────────────────┘ │
└──────────────┼─────────────────────────────────────┼────────────────────────┘
│ │
│ MCP Protocol │ HTTPS (htmx)
│ │
┌──────────────▼──────────────────────────────────────▼────────────────────────┐
│ MCP Server (/app endpoint) │
│ │
│ ┌─────────────────────────────────────────────────────────────────────────┐ │
│ │ Unified Search Interface (server/semantic.py) │ │
│ │ │ │
│ │ @mcp.tool() nc_semantic_search(algorithm, weights...) │ │
│ │ ├─ Validate parameters (weights sum ≤1.0) │ │
│ │ ├─ Dispatch to algorithm selector │ │
│ │ └─ Return ranked SearchResponse │ │
│ └────────────────────────────┬────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────────▼────────────────────────────────────────────┐ │
│ │ Algorithm Dispatcher (search/algorithms.py) │ │
│ │ │ │
│ │ if algorithm == "semantic": → semantic.py │ │
│ │ if algorithm == "keyword": → keyword.py │ │
│ │ if algorithm == "fuzzy": → fuzzy.py │ │
│ │ if algorithm == "hybrid": → hybrid.py (RRF fusion) │ │
│ └─────────────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ semantic.py │ │ keyword.py │ │ fuzzy.py │ │
│ │ │ │ │ │ │ │
│ │ • Query Qdrant │ │ • Token matching │ │ • Char overlap │ │
│ │ • Cosine dist │ │ • Title weight │ │ • 70% threshold │ │
│ │ • Score ≥0.7 │ │ • ADR-001 logic │ │ • Simple impl │ │
│ └────────┬─────────┘ └────────┬─────────┘ └────────┬─────────┘ │
│ │ │ │ │
│ └─────────────────────┼──────────────────────┘ │
│ │ │
│ ┌──────────────────────────────▼──────────────────────────────────────────┐ │
│ │ hybrid.py (Reciprocal Rank Fusion) │ │
│ │ │ │
│ │ 1. Run algorithms in parallel (semantic, keyword, fuzzy) │ │
│ │ 2. Collect ranked results from each │ │
│ │ 3. Apply RRF formula: score = weight / (k + rank) │ │
│ │ 4. Combine scores across algorithms │ │
│ │ 5. Re-rank by combined score │ │
│ └─────────────────────────────────────────────────────────────────────────┘ │
└───────────────────────────────────┬───────────────────────────────────────────┘
┌───────────────┴───────────────┐
│ │
┌──────────▼──────────┐ ┌─────────▼────────────┐
│ Qdrant Vector DB │ │ Nextcloud APIs │
│ │ │ │
│ • Vector search │ │ • Access verification│
│ • user_id filter │ │ • Full metadata fetch│
│ • Score threshold │ │ • Permission checks │
│ • 768-dim embeddings│ │ │
└─────────────────────┘ └──────────────────────┘
```
### Data Flow
#### MCP Tool Request
```
1. Client calls nc_semantic_search(query, algorithm="hybrid", weights...)
2. Server validates parameters (weights sum ≤1.0)
3. Dispatcher routes to hybrid.py
4. Hybrid search runs semantic, keyword, fuzzy in parallel
5. RRF combines results with weighted scores
6. Access verification via Nextcloud API
7. Return ranked SearchResponse to client
```
#### Viz Pane Request (Server-Side Processing)
```
1. User navigates to /app (Vector Visualization tab)
2. Browser loads vector-viz fragment via htmx
3. User enters query and adjusts algorithm/weights
4. htmx sends request to /app/vector-viz endpoint
5. Server executes search via search/algorithms.py:
- Filters by user_id (multi-tenant security)
- Applies selected algorithm (semantic/keyword/fuzzy/hybrid)
- Filters by document type (notes/files/calendar/contacts)
- Retrieves matching results + metadata
6. Server performs PCA reduction (768-dim → 2D):
- Converts matching results to 2D coordinates
- Only sends coordinates + metadata (not full vectors)
- Dramatically reduces bandwidth (e.g., 768 floats → 2 floats per doc)
7. Server returns JSON: {results: [...], coordinates_2d: [...], stats: {...}}
8. Browser receives lightweight response
9. Plotly.js renders interactive scatter plot
10. Matching results highlighted (blue), non-matches grayed (40% opacity)
```
**Performance Benefits of Server-Side Processing**:
- **Bandwidth reduction**: ~384x less data (2 floats vs 768 floats per document)
- **Client efficiency**: Browser only handles visualization, not computation
- **Scalability**: Can visualize 10,000+ documents without client-side lag
- **Security**: Raw vectors never leave server
- **Consistency**: Same search logic as MCP tool (no drift)
### 1. Core Search Algorithms
Four search algorithms will be available:
#### a) Semantic Search (Vector Similarity)
- **Method**: Cosine distance in 768-dimensional embedding space
- **Implementation**: Qdrant `query_points` with user_id filtering
- **Use case**: Conceptual queries, finding related content
- **Current status**: Implemented in `nextcloud_mcp_server/server/semantic.py`
#### b) Keyword Search (Token-Based)
- **Method**: Token matching with weighted scoring (from ADR-001)
- **Implementation**: Title matches weighted 3x higher than content
- **Use case**: Exact phrase matching, known titles
- **Current status**: Designed in ADR-001, not implemented
#### c) Fuzzy Search (Character Overlap)
- **Method**: Simple character-based similarity (70% threshold)
- **Implementation**: Character set comparison (current viz pane approach)
- **Use case**: Typo tolerance, approximate matching
- **Current status**: Implemented in viz pane only
#### d) Hybrid Search (Multi-Algorithm Fusion)
- **Method**: Reciprocal Rank Fusion (RRF) from ADR-003
- **Implementation**: Parallel execution + score combination
- **Use case**: Balanced queries, general-purpose search
- **Current status**: Designed in ADR-003, not implemented
### 2. Unified MCP Tool Interface
```python
@mcp.tool()
@require_scopes("semantic:read")
async def nc_semantic_search(
query: str,
ctx: Context,
limit: int = 10,
score_threshold: float = 0.7,
algorithm: Literal["semantic", "keyword", "fuzzy", "hybrid"] = "hybrid",
semantic_weight: float = 0.5,
keyword_weight: float = 0.3,
fuzzy_weight: float = 0.2,
) -> SearchResponse:
"""
Search Nextcloud content using configurable algorithms.
Args:
query: Natural language search query
ctx: MCP context for authentication
limit: Maximum results to return
score_threshold: Minimum similarity score (semantic/hybrid only)
algorithm: Search algorithm to use
semantic_weight: Weight for semantic results (hybrid only, default: 0.5)
keyword_weight: Weight for keyword results (hybrid only, default: 0.3)
fuzzy_weight: Weight for fuzzy results (hybrid only, default: 0.2)
Returns:
Ranked search results with scores and excerpts
"""
```
**Key decisions**:
- **Single tool name**: Keep `nc_semantic_search` for backward compatibility
- **Algorithm parameter**: Explicit selection via enum
- **Weight parameters**: Client-configurable, only apply to hybrid mode
- **Validation**: Weights must sum to ≤1.0, enforced server-side
- **Defaults**: Hybrid mode with balanced weights (semantic 50%, keyword 30%, fuzzy 20%)
### 3. Shared Algorithm Implementation
Extract search algorithms into reusable module:
```
nextcloud_mcp_server/
├── search/
│ ├── __init__.py
│ ├── algorithms.py # Core search implementations
│ ├── semantic.py # Vector similarity search
│ ├── keyword.py # Token-based search (ADR-001)
│ ├── fuzzy.py # Character overlap search
│ └── hybrid.py # RRF fusion (ADR-003)
└── server/
└── semantic.py # MCP tool wrapper
```
**Benefits**:
- Viz pane and MCP tools share identical implementations
- Testable in isolation
- Easy to add new algorithms (e.g., BM25, neural reranking)
- Clear separation of concerns
### 4. Viz Pane Integration
Update viz pane (`nextcloud_mcp_server/auth/userinfo_routes.py`) to:
1. **Use shared algorithms**: Import from `search/algorithms.py`
2. **Server-side filtering**: All search and filtering operations happen server-side
- Query execution via shared search backend
- Document type filtering (notes, files, calendar, contacts)
- User ID filtering for multi-tenant security
- Only matching results + metadata sent to client
- Reduces bandwidth and improves performance
3. **PCA reduction**: Server performs dimensionality reduction (768-dim → 2D)
- Only 2D coordinates sent to browser for visualization
- Dramatically reduces data transfer vs sending full vectors
- Enables visualization of large document collections
4. **User accessibility**: Available to all users with vector sync enabled
5. **Security**: Filter results by `user_id` (only show user's own documents)
6. **Interactive testing**: Allow users to:
- Select algorithm type
- Adjust weights (hybrid mode)
- Compare results across algorithms
- Visualize result distribution in 2D space
#### Viz Pane UI Components
```
┌────────────────────────────────────────────────────────────────────────┐
│ Vector Visualization [Status] │
├────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Search Configuration │ │
│ │ │ │
│ │ Query: [_______________________________________________] [Search]│ │
│ │ │ │
│ │ Algorithm: [Hybrid ▼] [Semantic] [Keyword] [Fuzzy] │ │
│ │ │ │
│ │ Weights (Hybrid Mode): │ │
│ │ Semantic: [========50========] 0.5 │ │
│ │ Keyword: [======30====== ] 0.3 │ │
│ │ Fuzzy: [====20==== ] 0.2 │ │
│ │ │ │
│ │ Document Types: ☑ Notes ☑ Files ☑ Calendar ☑ Contacts │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Vector Space Visualization (PCA 2D Projection) │ │
│ │ │ │
│ │ ▲ │ │
│ │ PC2 │ ● ● ● 🔵 Matching results (full opacity) │ │
│ │ │ ● ● ● ⚪ Non-matching results (40% opacity) │ │
│ │ │ 🔵 ● ● │ │
│ │ │ ● 🔵 ● Hover: Show document title + excerpt │ │
│ │ │ ● ● 🔵 ● Click: Open document in Nextcloud │ │
│ │ ────┼──●─🔵──●─●────► PC1 │ │
│ │ │ ● ● ● │ │
│ │ │ 🔵 ● ● Explained Variance: │ │
│ │ │ ● ● ● PC1: 23.4% | PC2: 18.7% │ │
│ │ │ ● ● │ │
│ │ │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Search Results (12 matching documents) │ │
│ │ │ │
│ │ 🔵 Kubernetes Setup Guide Score: 0.87 │ │
│ │ "...configure kubectl to connect to cluster..." │ │
│ │ [Open in Nextcloud] │ │
│ │ │ │
│ │ 🔵 Container Orchestration Notes Score: 0.82 │ │
│ │ "...deployment strategies for kubernetes..." │ │
│ │ [Open in Nextcloud] │ │
│ │ │ │
│ │ 🔵 K8s Troubleshooting Score: 0.79 │ │
│ │ "...common kuberntes errors and solutions..." │ │
│ │ [Open in Nextcloud] │ │
│ │ │ │
│ │ [Show More Results...] │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Algorithm Performance Comparison │ │
│ │ │ │
│ │ Algorithm │ Results │ Avg Score │ Time (ms) │ Precision │ │
│ │ ─────────────┼─────────┼───────────┼───────────┼─────────── │ │
│ │ Semantic │ 45 │ 0.78 │ 145ms │ ████░ 0.82 │ │
│ │ Keyword │ 23 │ 0.91 │ 42ms │ ███░░ 0.67 │ │
│ │ Fuzzy │ 67 │ 0.72 │ 89ms │ ██░░░ 0.45 │ │
│ │ Hybrid (RRF) │ 52 │ 0.84 │ 198ms │ █████ 0.89 │ │
│ └──────────────────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────────────────┘
```
**Key UI Features**:
1. **Search Input**: Real-time query testing with instant visualization
2. **Algorithm Selector**: Dropdown + quick-select buttons
3. **Weight Sliders**: Visual adjustment with live preview (hybrid mode only)
4. **Document Type Filters**: Checkboxes for notes, files, calendar, contacts
5. **2D Scatter Plot**: Interactive Plotly.js visualization
- Blue dots = matching documents (full opacity)
- Gray dots = non-matching documents (40% opacity)
- Hover = show title + excerpt tooltip
- Click = open document in Nextcloud
- Zoom/pan controls for exploration
6. **Results Panel**: Ranked list with scores and excerpts
7. **Performance Table**: Compare algorithm speed and accuracy
8. **Explained Variance**: Show how much information PCA preserves
**Technology Stack**:
- **Frontend**: htmx for dynamic loading, Alpine.js for reactivity
- **Visualization**: Plotly.js for interactive scatter plots
- **Styling**: Tailwind CSS (consistent with existing /app UI)
- **Backend**: Shared `search/algorithms.py` implementation
### 5. Reciprocal Rank Fusion (RRF) for Hybrid Search
Following ADR-003's design:
```python
def reciprocal_rank_fusion(
results: dict[str, list[SearchResult]],
weights: dict[str, float],
k: int = 60
) -> list[SearchResult]:
"""
Combine multiple ranked result lists using RRF.
Args:
results: Dict of algorithm_name -> ranked results
weights: Dict of algorithm_name -> weight (0-1)
k: RRF constant (default: 60, standard value)
Returns:
Combined and re-ranked results
"""
scores = defaultdict(float)
for algo_name, algo_results in results.items():
weight = weights.get(algo_name, 0.0)
for rank, result in enumerate(algo_results, start=1):
# RRF formula: 1 / (k + rank)
rrf_score = weight / (k + rank)
scores[result.doc_id] += rrf_score
# Sort by combined score, return top results
return sorted(scores.items(), key=lambda x: x[1], reverse=True)
```
**RRF properties**:
- **Rank-based**: Uses position, not raw scores (handles score scale differences)
- **Proven effective**: Standard approach in information retrieval
- **Configurable**: `k` parameter controls rank decay (default: 60)
- **Weight support**: Allows algorithm-specific importance
## Implementation Plan
### Phase 1: Extract and Unify Algorithms (Week 1)
1. Create `nextcloud_mcp_server/search/` module
2. Implement `algorithms.py` with base interface
3. Extract semantic search logic from `server/semantic.py`
4. Implement keyword search from ADR-001 design
5. Extract fuzzy search from viz pane
6. Implement RRF hybrid search from ADR-003
7. Add comprehensive unit tests for each algorithm
### Phase 2: Update MCP Tool (Week 1-2)
1. Add `algorithm` parameter to `nc_semantic_search()`
2. Add weight parameters (`semantic_weight`, etc.)
3. Implement algorithm dispatcher
4. Add parameter validation (weights sum ≤1.0)
5. Update response model to include algorithm metadata
6. Maintain backward compatibility (default: hybrid)
7. Add integration tests for all algorithm modes
### Phase 3: Update Viz Pane (Week 2)
**Critical: All processing must happen server-side**
1. **Remove client-side search filtering**
- Delete JavaScript-based keyword/fuzzy matching
- Remove client-side document type filtering
- No search logic in browser
2. **Implement server-side endpoint** (`/app/vector-viz`)
- Accept query, algorithm, weights, doc_type filters
- Execute search via `search/algorithms.py`
- Filter results by user_id (security)
- Perform PCA reduction (768-dim → 2D)
- Return JSON with 2D coordinates + metadata only
3. **Update frontend**
- htmx form submission to `/app/vector-viz`
- Algorithm selector dropdown
- Weight adjustment sliders (htmx updates on change)
- Document type checkboxes
- Plotly.js visualization of server response
4. **Performance optimization**
- Limit results to user's documents only
- Cache PCA transformation (invalidate on new vectors)
- Stream large result sets if needed
- Add loading indicators for server processing
### Phase 4: Documentation and Testing (Week 2-3)
1. Update MCP tool documentation
2. Add algorithm selection guide
3. Document weight tuning recommendations
4. Add end-to-end tests (MCP + viz pane)
5. Performance benchmarks for each algorithm
6. Update CLAUDE.md with search patterns
## Consequences
### Positive
1. **Flexibility**: MCP clients can optimize search for their use case
2. **Unified implementation**: Single source of truth for search algorithms
3. **User empowerment**: Viz pane enables query testing and tuning
4. **Backward compatible**: Existing semantic search behavior preserved
5. **Extensible**: Easy to add new algorithms (BM25, neural reranking)
6. **Testable**: Each algorithm can be unit tested independently
7. **Standards-based**: RRF is proven in production systems
### Negative
1. **Complexity**: More parameters for clients to understand
2. **API surface**: Larger tool signature (8 parameters)
3. **Performance**: Hybrid search requires multiple queries
4. **Validation overhead**: Weight validation adds processing
5. **Documentation burden**: Need to explain when to use each algorithm
### Neutral
1. **Weight defaults**: May need tuning based on user feedback
2. **Algorithm performance**: Will vary by content type and query
3. **Viz pane adoption**: Unknown if users will utilize testing interface
## Alternatives Considered
### Alternative 1: Separate Tools Per Algorithm
```python
@mcp.tool()
async def nc_semantic_search(query: str, ctx: Context, ...) -> SearchResponse:
"""Pure vector similarity search."""
@mcp.tool()
async def nc_keyword_search(query: str, ctx: Context, ...) -> SearchResponse:
"""Pure keyword matching."""
@mcp.tool()
async def nc_hybrid_search(query: str, ctx: Context, weights: dict, ...) -> SearchResponse:
"""Hybrid search with weights."""
```
**Rejected because**:
- API proliferation (3+ tools instead of 1)
- Harder to discover capabilities
- Backward compatibility issues
- DRY violation (repeated parameters)
### Alternative 2: Server-Wide Configuration Only
```python
# .env configuration
SEARCH_ALGORITHM=hybrid
SEMANTIC_WEIGHT=0.5
KEYWORD_WEIGHT=0.3
FUZZY_WEIGHT=0.2
```
**Rejected because**:
- No per-query flexibility
- MCP clients cannot optimize for different tasks
- Requires server restart for changes
- User's requirement: "expose a way for users to override the default weights"
### Alternative 3: Production-Grade Fuzzy (Levenshtein/RapidFuzz)
**Rejected because**:
- Adds external dependency
- Simple character overlap performs adequately
- Can always upgrade later if needed
- User's preference: "Keep simple character overlap"
## Related ADRs
- **ADR-001**: Enhanced Note Search (keyword algorithm design)
- **ADR-003**: Vector Database and Semantic Search (hybrid search + RRF design)
- **ADR-007**: Background Vector Sync (semantic search implementation)
- **ADR-008**: MCP Sampling for RAG (uses semantic search results)
- **ADR-009**: Semantic Search OAuth Scope (security model)
- **ADR-011**: Improving Semantic Search Quality (mentions future "ADR-013" for hybrid search)
**This ADR supersedes**:
- ADR-011's placeholder for "ADR-013: Hybrid Search"
**This ADR implements**:
- ADR-003's hybrid search design (previously unimplemented)
- ADR-001's keyword search design (previously unimplemented)
## References
- **Reciprocal Rank Fusion**: Cormack, G. V., Clarke, C. L., & Buettcher, S. (2009). "Reciprocal rank fusion outperforms condorcet and individual rank learning methods." SIGIR '09.
- **Vector Search**: Malkov, Y. A., & Yashunin, D. A. (2018). "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs." TPAMI.
- **Hybrid Search Best Practices**: Qdrant documentation on hybrid search patterns
- **MCP Protocol**: Model Context Protocol specification for tool design
## Implementation Notes
### Weight Validation
```python
def validate_weights(
semantic_weight: float,
keyword_weight: float,
fuzzy_weight: float
) -> None:
"""Validate hybrid search weights."""
if semantic_weight < 0 or keyword_weight < 0 or fuzzy_weight < 0:
raise ValueError("Weights must be non-negative")
total = semantic_weight + keyword_weight + fuzzy_weight
if total > 1.0:
raise ValueError(f"Weights sum to {total:.2f}, must be ≤1.0")
if total == 0.0:
raise ValueError("At least one weight must be > 0")
```
### Backward Compatibility
The default behavior (`algorithm="hybrid"` with balanced weights) provides better results than current pure semantic search, while maintaining the same tool name and signature structure. Existing clients will automatically benefit from hybrid search without code changes.
### Performance Considerations
- **Semantic search**: ~50-200ms (vector DB query)
- **Keyword search**: ~10-50ms (in-memory token matching)
- **Fuzzy search**: ~20-100ms (character comparison)
- **Hybrid search**: ~100-300ms (parallel execution + fusion)
Parallel execution of algorithms minimizes hybrid search latency.
### Security Model
All algorithms respect the same security boundaries:
1. **User filtering**: Qdrant queries filter by `user_id`
2. **Access verification**: Results verified via Nextcloud API
3. **OAuth scope**: `semantic:read` required for all algorithms
4. **Viz pane**: Shows only current user's documents
## Success Metrics
1. **Adoption**: % of MCP clients using algorithm parameter
2. **Performance**: Search latency percentiles (p50, p95, p99)
3. **Quality**: User satisfaction with result relevance
4. **Viz pane usage**: % of users accessing testing interface
5. **Weight distribution**: Most common weight configurations
## Future Enhancements
1. **Additional algorithms**: BM25, TF-IDF, neural reranking
2. **Auto-tuning**: Learn optimal weights per user
3. **Query analysis**: Automatic algorithm selection based on query
4. **Cross-app search**: Extend beyond notes to calendar, files, etc.
5. **Feedback loop**: Use click-through rate to improve weights
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# Token Acquisition Patterns for ADR-004 Progressive Consent
## Overview
ADR-004 Progressive Consent establishes the authorization architecture (Flow 1 for client auth, Flow 2 for resource provisioning). This document describes **how tokens are acquired for different operational contexts** within that architecture.
**Key Principle**: Refresh tokens from Flow 2 (Progressive Consent) should **NEVER** be used for MCP tool calls - they are exclusively for background jobs.
## Implementation Status
**Current Status**: ✅ Token exchange infrastructure implemented, available as opt-in feature
The MCP server supports two token acquisition modes:
1. **Pass-through mode** (default, `ENABLE_TOKEN_EXCHANGE=false`): Simple, stateless
2. **Token exchange mode** (opt-in, `ENABLE_TOKEN_EXCHANGE=true`): Enhanced security with token delegation
Both modes maintain the critical separation: **refresh tokens are never used for tool calls**.
## Current Default (Pass-Through Mode)
### What Happens (ENABLE_TOKEN_EXCHANGE=false):
1. Client gets Flow 1 token (`aud: "mcp-server"`)
2. Client calls MCP tool
3. Server validates Flow 1 token
4. Server passes Flow 1 token to Nextcloud
5. Nextcloud validates token with IdP
6. Refresh tokens (from Flow 2) used **only** for background jobs
### Characteristics:
- ✅ Simple, stateless operation
- ✅ Clear separation: Flow 1 tokens for sessions, refresh tokens for background
- ✅ Lower latency (no token exchange round-trip)
- ✅ Works with any OAuth IdP
## Optional Token Exchange Mode
### Token Exchange Pattern (ENABLE_TOKEN_EXCHANGE=true)
**MCP Session (Foreground Operations)**:
```
┌─────────────┐ Flow 1 Token ┌──────────────┐
│ MCP Client │ ───(aud: mcp-server)──> │ MCP Server │
└─────────────┘ └──────────────┘
Tool Call │
"search_notes()" │
┌─────────────────────┐
│ Token Exchange │
│ 1. Validate Flow 1 │
│ 2. Check permission │
│ 3. Request delegated│
│ Nextcloud token │
└─────────────────────┘
│ Exchange Request
┌─────────────────────┐
│ IdP Token Endpoint │
│ (Token Exchange) │
└─────────────────────┘
│ Delegated Token
│ (aud: nextcloud)
│ (limited scopes)
│ (short-lived)
┌─────────────────────┐
│ Nextcloud API Call │
│ GET /notes │
└─────────────────────┘
```
**Key Properties of Session Tokens:**
- ✅ Generated **on-demand** during tool execution
-**Ephemeral** - used only for current operation
-**NOT stored** - discarded after use
-**Limited scopes** - only what tool needs (e.g., `notes:read` for search)
-**Short-lived** - expires quickly (e.g., 5 minutes)
**Background Jobs (Offline Operations)**:
```
┌─────────────────┐ Scheduled Job ┌──────────────┐
│ Background │ ──────────────────────> │ Worker │
│ Scheduler │ │ Process │
└─────────────────┘ └──────────────┘
│ Use stored
│ refresh token
┌─────────────────────┐
│ Refresh Token Store │
│ (Flow 2 provisioned)│
└─────────────────────┘
│ Refresh Token
┌─────────────────────┐
│ IdP Token Endpoint │
│ (Refresh Grant) │
└─────────────────────┘
│ Background Token
│ (aud: nextcloud)
│ (different scopes)
│ (longer-lived)
┌─────────────────────┐
│ Nextcloud API │
│ (Background Sync) │
└─────────────────────┘
```
**Key Properties of Background Tokens:**
- ✅ Obtained from **stored refresh token** (Flow 2)
-**Different scopes** than session tokens (e.g., `notes:sync`, `files:sync`)
-**Longer-lived** for background operations
-**Never used for MCP sessions**
-**Only for offline/background jobs**
## Implementation Requirements
### 1. Token Exchange Endpoint
Implement RFC 8693 Token Exchange:
```python
# nextcloud_mcp_server/auth/token_exchange.py
async def exchange_token_for_delegation(
flow1_token: str,
requested_audience: str = "nextcloud",
requested_scopes: list[str] | None = None
) -> tuple[str, int]:
"""
Exchange Flow 1 MCP token for delegated Nextcloud token.
This implements RFC 8693 Token Exchange for on-behalf-of delegation.
IMPORTANT: Nextcloud doesn't support OAuth scopes natively. Scopes are
soft-scopes enforced by the MCP server via @require_scopes decorator,
not by the IdP or Nextcloud. Therefore, requested_scopes are not passed
to the IdP during token exchange.
Args:
flow1_token: The MCP session token (aud: "mcp-server")
requested_audience: Target audience (usually "nextcloud")
requested_scopes: Ignored (Nextcloud doesn't support scopes)
Returns:
Tuple of (delegated_token, expires_in)
"""
# 1. Validate Flow 1 token (audience check)
# 2. Check user has provisioned Nextcloud access (Flow 2)
# 3. Request token exchange from IdP (without scopes - Nextcloud doesn't support them)
# 4. Return ephemeral delegated token
```
### 2. Unified get_client() Pattern
The token acquisition mode is handled transparently by `get_client()`:
```python
# nextcloud_mcp_server/context.py
async def get_client(ctx: Context) -> NextcloudClient:
"""
Get the appropriate Nextcloud client based on authentication mode.
This function handles three modes:
1. BasicAuth mode: Returns shared client from lifespan context
2. OAuth pass-through mode (ENABLE_TOKEN_EXCHANGE=false, default):
Verifies Flow 1 token and passes it to Nextcloud
3. OAuth token exchange mode (ENABLE_TOKEN_EXCHANGE=true):
Exchanges Flow 1 token for ephemeral Nextcloud token via RFC 8693
"""
settings = get_settings()
lifespan_ctx = ctx.request_context.lifespan_context
# BasicAuth mode - use shared client (no token exchange)
if hasattr(lifespan_ctx, "client"):
return lifespan_ctx.client
# OAuth mode (has 'nextcloud_host' attribute)
if hasattr(lifespan_ctx, "nextcloud_host"):
# Check if token exchange is enabled
if settings.enable_token_exchange:
# Token exchange mode: Exchange Flow 1 token for ephemeral Nextcloud token
return await get_session_client_from_context(
ctx, lifespan_ctx.nextcloud_host
)
else:
# Pass-through mode (default): Verify and pass Flow 1 token to Nextcloud
return get_client_from_context(ctx, lifespan_ctx.nextcloud_host)
```
### 3. MCP Tool Pattern (No Changes Required!)
Tools use the same pattern regardless of token acquisition mode:
```python
@mcp.tool()
@require_scopes("notes:read") # Soft-scope enforced by MCP server, not Nextcloud
@require_provisioning
async def nc_notes_search_notes(query: str, ctx: Context) -> SearchNotesResponse:
"""Search notes by title or content."""
# get_client() handles both pass-through and token exchange modes
client = await get_client(ctx)
# Execute operation
results = await client.notes.search_notes(query=query)
# In token exchange mode, ephemeral token is automatically discarded
# In pass-through mode, Flow 1 token was validated and passed through
return SearchNotesResponse(results=results)
```
**Key Benefit**: Tools don't need to know which mode is active. The token acquisition pattern is configured at the server level via `ENABLE_TOKEN_EXCHANGE`.
### 4. Background Job Pattern
Background jobs use a **different token acquisition pattern** - they use refresh tokens from Flow 2:
```python
# Background worker
async def sync_notes_job(user_id: str):
"""Background job to sync notes."""
# Get refresh token stored during Flow 2 (Progressive Consent)
token_storage = get_token_storage()
refresh_token = await token_storage.get_refresh_token(user_id)
if not refresh_token:
logger.warning(f"No refresh token for user {user_id}")
return
# Use refresh token to get Nextcloud access token
idp_client = get_idp_client()
response = await idp_client.refresh_token(
refresh_token=refresh_token,
audience='nextcloud'
)
# Create client with background token (can be cached)
client = NextcloudClient.from_token(
base_url=NEXTCLOUD_HOST,
token=response.access_token,
username=user_id
)
# Perform background sync
await client.notes.sync_all()
```
**Key differences from tool calls:**
- Uses refresh tokens from Flow 2 (Progressive Consent provisioning)
- Tokens can be cached for efficiency (longer-lived operations)
- No user interaction possible (offline)
- Never triggered during MCP tool execution
## Security Benefits
### Proper Token Exchange:
1.**Least Privilege**: Each operation gets only needed scopes
2.**Time-Limited**: Session tokens expire quickly
3.**Audit Trail**: Each exchange can be logged
4.**Token Isolation**: Session ≠ Background tokens
5.**Revocation**: Can revoke background access without affecting active sessions
### Current Incorrect Pattern:
1.**Over-Privileged**: Refresh token has all scopes
2.**Long-Lived**: Same token reused indefinitely
3.**No Separation**: Sessions and background jobs use same credential
4.**Revocation Issues**: Revoking affects everything
## Implementation Steps
### Phase 1: Token Exchange (High Priority)
1. Implement RFC 8693 token exchange endpoint
2. Update Token Broker with `get_session_token()` vs `get_background_token()`
3. Modify tool pattern to use token exchange
### Phase 2: Scope Separation (High Priority)
1. Define session scopes vs background scopes
2. Update provisioning flow to request appropriate scopes
3. Validate scopes in token exchange
### Phase 3: Background Jobs (Medium Priority)
1. Implement background worker pattern
2. Create scheduled jobs (note sync, etc.)
3. Use background token pattern
### Phase 4: Testing (High Priority)
1. Test token exchange flow end-to-end
2. Verify session tokens are ephemeral
3. Verify background tokens are separate
4. Load test token exchange performance
## References
- **RFC 8693**: OAuth 2.0 Token Exchange
- **RFC 9068**: JSON Web Token (JWT) Profile for OAuth 2.0 Access Tokens
- **ADR-004**: Progressive Consent OAuth Flows
- **OAuth 2.0 Delegation**: On-Behalf-Of vs Impersonation patterns
## Status
**Current Status**: ✅ Token exchange infrastructure implemented, available as opt-in feature
**Modes Available**:
- ✅ Pass-through mode (default, `ENABLE_TOKEN_EXCHANGE=false`): Simple, stateless
- ✅ Token exchange mode (opt-in, `ENABLE_TOKEN_EXCHANGE=true`): Enhanced security
**Implementation Complete**:
-`token_exchange.py` module with RFC 8693 support
- ✅ Fallback to refresh grant when RFC 8693 not supported
-`get_client()` unified pattern (handles both modes transparently)
- ✅ Tokens never cached in token exchange mode (ephemeral)
- ✅ Background jobs use separate pattern (refresh tokens from Flow 2)
## Configuration
To enable token exchange mode:
```bash
# docker-compose.yml or .env
ENABLE_TOKEN_EXCHANGE=true
```
When enabled, all MCP tool calls will use token exchange (RFC 8693) to obtain ephemeral Nextcloud tokens. When disabled (default), Flow 1 tokens are passed through to Nextcloud.
## Nextcloud Scope Limitation
**IMPORTANT**: Nextcloud does not support OAuth scopes natively. Scopes like "notes:read" are **soft-scopes** enforced by the MCP server via `@require_scopes` decorator, not by the IdP or Nextcloud.
This means:
- Token exchange provides audit and delegation benefits, not scope restriction
- All Nextcloud tokens have equivalent permissions at the Nextcloud level
- Fine-grained access control is enforced by MCP server, not Nextcloud
## Next Actions (Optional Enhancements)
1. [ ] Add integration tests for token exchange mode with actual MCP tools
2. [ ] Document background job patterns for scheduled sync operations
3. [ ] Add metrics for token exchange performance
4. [ ] Consider making token exchange the default in future major version
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# Audience Validation Setup
## Overview
This document explains the **separate clients architecture** for Keycloak → MCP Server → Nextcloud integration, following OAuth 2.0 best practices and RFC 8707 (Resource Indicators).
## Architecture: Separate Clients Pattern
```
Keycloak Realm: nextcloud-mcp
├── Client: "nextcloud" (Resource Server)
│ └── Represents Nextcloud as a protected resource
│ └── Used by user_oidc for bearer token validation
│ └── Validates tokens with aud="nextcloud"
└── Client: "nextcloud-mcp-server" (OAuth Client)
└── MCP Server uses this to REQUEST tokens
└── Issues tokens with aud="nextcloud" (targeting resource)
└── Future: aud=["nextcloud", "other-service"]
Token Flow:
MCP Server (client: nextcloud-mcp-server)
↓ requests token from Keycloak
Token issued:
- aud: "nextcloud" (intended for Nextcloud resource)
- azp: "nextcloud-mcp-server" (requested by MCP Server)
- preferred_username: "admin" (on behalf of user)
↓ sent to Nextcloud API
Nextcloud user_oidc (client: nextcloud)
✓ validates aud matches configured client_id
```
**Key Benefits**:
-**Proper OAuth separation**: OAuth client ≠ resource server
-**Future extensibility**: MCP Server can request multi-resource tokens
-**RFC 8707 compliance**: Audience indicates intended resource
-**Clear requester identification**: azp claim identifies MCP Server
## Token Claims
Tokens issued by the `nextcloud-mcp-server` client contain:
- **`aud: "nextcloud"`** - Audience: Token intended for Nextcloud resource server (matches user_oidc client_id)
- **`azp: "nextcloud-mcp-server"`** - Authorized Party: Identifies MCP Server as the OAuth client that requested the token
- **`preferred_username: "admin"`** - User identifier (Keycloak uses this for password grant; `sub` for authorization_code grant)
- **`scope: "openid profile email offline_access"`** - Requested scopes including offline access for background jobs
**How user_oidc Validates**:
1. SelfEncodedValidator checks: `aud == user_oidc.client_id`?
- ✓ "nextcloud" == "nextcloud" → PASS
2. Fast JWT verification with JWKS (no HTTP call to userinfo endpoint)
3. User provisioned based on `preferred_username` or `sub` claim
**For Background Jobs**:
- MCP Server stores encrypted refresh tokens
- Refreshes access tokens when needed
- All tokens have `aud: "nextcloud"` → validated by user_oidc
- No admin credentials required
## Configuration
The configuration requires **two separate clients** in Keycloak:
1. **`nextcloud`** - Resource server client (for user_oidc validation)
2. **`nextcloud-mcp-server`** - OAuth client (for MCP Server to request tokens)
### 1. Keycloak - Create Resource Server Client
First, create the `nextcloud` client that represents Nextcloud as a resource server:
**Via Keycloak Admin API:**
```bash
# Get admin token
ADMIN_TOKEN=$(curl -X POST "http://localhost:8888/realms/master/protocol/openid-connect/token" \
-d "grant_type=password" \
-d "client_id=admin-cli" \
-d "username=admin" \
-d "password=admin" | jq -r '.access_token')
# Create 'nextcloud' resource server client
curl -X POST "http://localhost:8888/admin/realms/nextcloud-mcp/clients" \
-H "Authorization: Bearer $ADMIN_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"clientId": "nextcloud",
"name": "Nextcloud Resource Server",
"description": "Resource server for Nextcloud APIs - used by user_oidc for bearer token validation",
"enabled": true,
"clientAuthenticatorType": "client-secret",
"secret": "nextcloud-secret-change-in-production",
"bearerOnly": true,
"standardFlowEnabled": false,
"directAccessGrantsEnabled": false,
"serviceAccountsEnabled": false,
"publicClient": false
}'
```
**Via Realm Export** (`keycloak/realm-export.json`):
```json
{
"clients": [
{
"clientId": "nextcloud",
"name": "Nextcloud Resource Server",
"enabled": true,
"bearerOnly": true,
"secret": "nextcloud-secret-change-in-production"
}
]
}
```
### 2. Keycloak - Create OAuth Client with Audience Mapper
Next, create the `nextcloud-mcp-server` client that MCP Server uses to request tokens:
**Via Keycloak Admin API:**
```bash
# Create 'nextcloud-mcp-server' OAuth client
curl -X POST "http://localhost:8888/admin/realms/nextcloud-mcp/clients" \
-H "Authorization: Bearer $ADMIN_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"clientId": "nextcloud-mcp-server",
"name": "Nextcloud MCP Server",
"enabled": true,
"clientAuthenticatorType": "client-secret",
"secret": "mcp-secret-change-in-production",
"standardFlowEnabled": true,
"directAccessGrantsEnabled": true,
"redirectUris": ["http://localhost:*/callback"]
}'
# Get client internal ID
CLIENT_ID=$(curl "http://localhost:8888/admin/realms/nextcloud-mcp/clients" \
-H "Authorization: Bearer $ADMIN_TOKEN" | jq -r '.[] | select(.clientId=="nextcloud-mcp-server") | .id')
# Add audience mapper targeting 'nextcloud' resource
curl -X POST "http://localhost:8888/admin/realms/nextcloud-mcp/clients/$CLIENT_ID/protocol-mappers/models" \
-H "Authorization: Bearer $ADMIN_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"name": "audience-nextcloud",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"consentRequired": false,
"config": {
"included.custom.audience": "nextcloud",
"access.token.claim": "true",
"id.token.claim": "false"
}
}'
```
**Option B: Via Realm Export** (for infrastructure-as-code)
Update `keycloak/realm-export.json`:
```json
{
"clients": [
{
"clientId": "nextcloud-mcp-server",
"name": "Nextcloud MCP Server",
"protocolMappers": [
{
"name": "audience-nextcloud-mcp-server",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"consentRequired": false,
"config": {
"included.custom.audience": "nextcloud-mcp-server",
"access.token.claim": "true",
"id.token.claim": "false"
}
}
]
}
]
}
```
Then re-import realm or restart Keycloak.
**Option C: Via Keycloak Admin UI**
1. Go to Keycloak Admin Console → Realm → Clients → `nextcloud-mcp-server`
2. Click "Client scopes" tab
3. Click "Add client scope" → "Create dedicated scope"
4. Add protocol mapper: "Audience"
- Mapper Type: `Audience`
- Included Custom Audience: `nextcloud`
- Add to access token: ON
- Add to ID token: OFF
### 3. Nextcloud user_oidc - Configure Resource Server Client
Configure user_oidc to use the `nextcloud` resource server client:
```bash
docker compose exec app php occ user_oidc:provider keycloak \
--clientid="nextcloud" \
--clientsecret="nextcloud-secret-change-in-production" \
--discoveryuri="http://keycloak:8080/realms/nextcloud-mcp/.well-known/openid-configuration" \
--check-bearer=1 \
--bearer-provisioning=1 \
--unique-uid=1 \
--mapping-uid="sub" \
--mapping-display-name="name" \
--mapping-email="email"
```
**Result**: user_oidc validates tokens with `aud="nextcloud"` using SelfEncodedValidator (fast JWT verification).
### 3. Nextcloud user_oidc - Realm-Level Validation
Nextcloud's `user_oidc` app validates at **realm level** via userinfo endpoint:
-**No configuration needed** - works automatically
- ✅ Validates any token from Keycloak realm
- ✅ Audience check is **optional** (disabled by default)
**Optional: Disable strict audience checking** (if enabled):
```bash
docker compose exec app php occ config:app:set user_oidc \
selfencoded_bearer_validation_audience_check --value=false --type=boolean
```
## Verification
### 1. Check Token Claims
```bash
# Get token from Keycloak
TOKEN=$(curl -X POST "http://localhost:8888/realms/nextcloud-mcp/protocol/openid-connect/token" \
-d "grant_type=password" \
-d "client_id=nextcloud-mcp-server" \
-d "client_secret=mcp-secret-change-in-production" \
-d "username=admin" \
-d "password=admin" | jq -r '.access_token')
# Decode JWT
echo $TOKEN | cut -d'.' -f2 | base64 -d | jq '.'
# Should show:
{
"aud": "nextcloud", # ✓ Intended for Nextcloud
"azp": "nextcloud-mcp-server", # ✓ Requested by MCP Server
"iss": "http://localhost:8888/realms/nextcloud-mcp",
"scope": "openid email profile offline_access",
...
}
```
### 2. Test with Nextcloud API
```bash
# Token should be accepted
curl -H "Authorization: Bearer $TOKEN" \
"http://localhost:8080/ocs/v2.php/cloud/capabilities"
# Should return HTTP 200 OK
```
### 3. Test Audience Rejection
```bash
# Get token from different client (without audience mappers)
TOKEN_WRONG=$(curl -X POST "http://localhost:8888/realms/nextcloud-mcp/protocol/openid-connect/token" \
-d "grant_type=password" \
-d "client_id=test-client-b" \
-d "client_secret=test-secret-b" \
-d "username=admin" \
-d "password=admin" | jq -r '.access_token')
# This token has NO audience claim - should be rejected by MCP server
# (But accepted by Nextcloud user_oidc which validates at realm level)
```
## Token Flow Example
### Successful Request (Background Job)
```
1. User authorizes MCP Client via OAuth
└─ MCP Server gets refresh token (stored encrypted)
2. Background worker needs to sync data
└─ MCP Server refreshes access token from Keycloak
└─ Token issued with aud: "nextcloud", azp: "nextcloud-mcp-server"
3. MCP Server → Nextcloud API (with token)
└─ user_oidc validates via userinfo endpoint ✓
└─ Nextcloud identifies:
- Token intended for Nextcloud (aud: "nextcloud")
- Request from MCP Server (azp: "nextcloud-mcp-server")
- On behalf of user (sub: "user-id")
4. Success! MCP Server can act on behalf of user in background.
```
### Rejected Request
```
1. Attacker gets token for different client
└─ Token has aud: "other-service"
2. Attacker → Nextcloud API (with wrong token)
└─ user_oidc validates via userinfo endpoint
└─ Token validation fails (invalid/expired/wrong realm)
└─ HTTP 401 Unauthorized
3. Request blocked - token not valid for this realm/service
```
## OAuth Flows and User Consent
### When Does the User Grant Consent?
User consent happens during the **Authorization Code Flow** (production OAuth):
```
1. User clicks "Connect" in MCP Client (e.g., Claude Desktop)
2. MCP Client initiates OAuth flow by opening browser to Keycloak:
https://keycloak/realms/nextcloud-mcp/protocol/openid-connect/auth?
client_id=nextcloud-mcp-server&
redirect_uri=<mcp-client-redirect-uri>&
response_type=code&
scope=openid profile email offline_access
3. Keycloak shows login screen (if not logged in)
4. **Keycloak shows consent screen:**
"Nextcloud MCP Server wants to access your Nextcloud data on your behalf"
Requested permissions:
- Access your profile (openid, profile, email)
- Offline access (background operations with refresh tokens)
5. User clicks "Allow" → grants consent
6. Keycloak redirects back to MCP Client with authorization code
7. MCP Client exchanges code for tokens (receives access + refresh tokens)
8. MCP Client shares tokens with MCP Server via MCP protocol
9. MCP Server stores refresh token encrypted for background operations
```
**Key Architecture Notes:**
- **MCP Server is a protected resource** (requires OAuth to access)
- **MCP Client** (Claude Desktop) is the OAuth client that initiates the flow
- **MCP Client handles the redirect** and token exchange with Keycloak
- **MCP Client shares refresh token** with MCP Server so it can act on behalf of user in background
**Key Points:**
-**Explicit user consent** before any access
-**Scopes displayed** so user knows what's being requested
-**Offline access** must be explicitly granted (for background jobs)
-**Revocable** - user can revoke consent in Keycloak at any time
### Grant Types
Our architecture supports multiple OAuth grant types:
**1. Authorization Code + PKCE (Production)**
```
Use case: Interactive login from MCP clients
Consent: Yes - explicit user authorization
Tokens: Access token + Refresh token (if offline_access granted)
Security: PKCE prevents authorization code interception
```
**2. Password Grant (Testing Only)**
```
Use case: Integration testing with docker-compose
Consent: No - username/password provided directly
Tokens: Access token + Refresh token
Security: NOT for production - exposes user credentials
```
**3. Refresh Token Grant (Background Jobs)**
```
Use case: MCP Server refreshing expired access tokens
Consent: No new consent - uses previously granted refresh token
Tokens: New access token (refresh token may rotate)
Security: Refresh tokens stored encrypted, rotated on use
```
## Authentication Strategies for Background Jobs
> **Note on Service Account Tokens**: Service account tokens (`client_credentials` grant) were evaluated but **rejected** as they create Nextcloud user accounts (e.g., `service-account-{client_id}`) which violates OAuth "act on-behalf-of" principles. See ADR-002 "Will Not Implement" section for details.
### Current Approach: Offline Access with Refresh Tokens
The MCP server uses **offline_access** scope to enable background operations:
**How it works:**
1. User grants `offline_access` scope during OAuth consent
2. MCP Client receives refresh token from Keycloak
3. MCP Client shares refresh token with MCP Server via MCP protocol
4. MCP Server stores refresh token encrypted (see ADR-002)
5. Background jobs exchange refresh token for fresh access tokens as needed
**Benefits:**
- ✅ Works today with Keycloak and all OIDC providers
- ✅ Standard OAuth pattern (RFC 6749)
- ✅ Explicit user consent to `offline_access` scope
- ✅ MCP Server can act on behalf of user in background
**Limitations:**
- ⚠️ Requires secure token storage on MCP Server
- ⚠️ MCP Client must trust MCP Server with refresh token
- ⚠️ Weak audit trail - API requests appear to come from user directly
- ⚠️ No visibility that MCP Server is the actual actor
### Token Exchange with Delegation (ADR-002 Tier 2 - Implemented)
**RFC 8693 Delegation** would provide better audit trail and security:
**How it would work:**
1. User grants `may_act:nextcloud-mcp-server` scope during authentication
2. Subject token includes: `{ "may_act": { "client": "nextcloud-mcp-server" } }`
3. MCP Server has its own service account token (actor_token)
4. Background job requests token exchange:
- `subject_token` (user's token with may_act claim)
- `actor_token` (mcp-server's service token)
5. Keycloak validates actor matches may_act claim
6. Returns delegated token: `{ "sub": "user", "act": "nextcloud-mcp-server" }`
**Benefits:**
- ✅ Better audit trail - Nextcloud APIs see both user and actor
- ✅ No token storage needed (tokens generated on-demand)
- ✅ Fine-grained permissions via `may_act` claim
- ✅ User explicitly consents to MCP Server acting on their behalf
- ✅ RFC 8693 compliant
**Current Status:**
-**NOT implemented in Keycloak yet** ([Issue #38279](https://github.com/keycloak/keycloak/issues/38279))
- ❌ Would require custom implementation or waiting for upstream
- 📝 Proposal includes `act` claim and `may_act` consent mechanism
**Why Not Available:**
- Keycloak supports **impersonation** (changes `sub` claim), but not **delegation** (`act` claim)
- Impersonation has poor audit trail (actor invisible)
- Delegation proposal is open but not implemented yet
**Reference:** See `docs/ADR-002-vector-sync-authentication.md` for detailed comparison of authentication tiers.
## Security Benefits
1. **Intent Validation**: Tokens explicitly declare Nextcloud as the intended recipient via `aud` claim
2. **Requester Identification**: The `azp` claim identifies MCP Server as the requester
3. **User Context**: The `sub` claim preserves user identity for audit and authorization
4. **Background Jobs**: Refresh tokens enable MCP Server to act on behalf of users without admin credentials
5. **OAuth Standards**: Follows RFC 8707 (Resource Indicators) and RFC 6749 (OAuth 2.0)
**Current Limitations:**
- API requests from background jobs appear to come from user directly (no `act` claim yet)
- See "Authentication Strategies for Background Jobs" section for future delegation support
## Token Claims
### Key Claims
- **`aud: "nextcloud"`** - Audience: Token intended for Nextcloud APIs
- **`azp: "nextcloud-mcp-server"`** - Authorized Party: MCP Server requested the token
- **`sub: "user-id"`** - Subject: User on whose behalf the request is made
- **`scope: "openid profile email offline_access"`** - Requested scopes including offline access for background jobs
### Client Naming
The Keycloak client is named `nextcloud-mcp-server` to clarify:
- **MCP Server** uses this client to get tokens for Nextcloud
- **MCP Clients** (like Claude Desktop) connect to MCP Server via separate OAuth flows
- **Not** named "mcp-client" to avoid confusion about which component is the client
## Troubleshooting
### Token Has No Audience
**Symptom**: `"aud": null` in decoded JWT
**Cause**: Protocol mappers not configured
**Solution**: Add audience mappers via Keycloak Admin API (see Configuration section)
### MCP Server Rejects Token
**Symptom**: HTTP 401 with "JWT validation failed"
**Cause**: Token audience doesn't match expected value
**Solution**:
1. Check token has correct `aud` claim
2. Verify MCP server expects correct audience value in code
3. Check logs for specific JWT validation error
### Nextcloud Rejects Token
**Symptom**: HTTP 401 from Nextcloud API
**Cause**: User not provisioned or token invalid
**Solution**:
1. Check user_oidc provider is configured: `php occ user_oidc:provider keycloak`
2. Check bearer validation enabled: `--check-bearer=1`
3. Test token with userinfo endpoint: `curl -H "Authorization: Bearer $TOKEN" http://keycloak/realms/.../userinfo`
## Related Documentation
- **Multi-client validation**: `docs/keycloak-multi-client-validation.md`
- **ADR-002**: `docs/ADR-002-vector-sync-authentication.md`
- **OAuth setup**: `docs/oauth-setup.md`
- **Keycloak integration**: `docs/keycloak-integration.md` (if created)
## References
- [RFC 8707 - Resource Indicators for OAuth 2.0](https://datatracker.ietf.org/doc/html/rfc8707)
- [OIDC Core - ID Token aud claim](https://openid.net/specs/openid-connect-core-1_0.html#IDToken)
- [Keycloak Audience Protocol Mappers](https://www.keycloak.org/docs/latest/server_admin/#_audience)
+11 -313
View File
@@ -45,7 +45,8 @@ NEXTCLOUD_HOST=https://your.nextcloud.instance.com
NEXTCLOUD_OIDC_CLIENT_ID=your-client-id
NEXTCLOUD_OIDC_CLIENT_SECRET=your-client-secret
# OAuth Callback Settings (optional)
# OAuth Storage and Callback Settings (optional)
NEXTCLOUD_OIDC_CLIENT_STORAGE=.nextcloud_oauth_client.json
NEXTCLOUD_MCP_SERVER_URL=http://localhost:8000
# Leave these EMPTY for OAuth mode
@@ -60,6 +61,7 @@ NEXTCLOUD_PASSWORD=
| `NEXTCLOUD_HOST` | ✅ Yes | - | Full URL of your Nextcloud instance (e.g., `https://cloud.example.com`) |
| `NEXTCLOUD_OIDC_CLIENT_ID` | ⚠️ Optional | - | OAuth client ID (auto-registers if empty) |
| `NEXTCLOUD_OIDC_CLIENT_SECRET` | ⚠️ Optional | - | OAuth client secret (auto-registers if empty) |
| `NEXTCLOUD_OIDC_CLIENT_STORAGE` | ⚠️ Optional | `.nextcloud_oauth_client.json` | Path to store auto-registered client credentials |
| `NEXTCLOUD_MCP_SERVER_URL` | ⚠️ Optional | `http://localhost:8000` | MCP server URL for OAuth callbacks |
| `NEXTCLOUD_USERNAME` | ❌ Must be empty | - | Leave empty to enable OAuth mode |
| `NEXTCLOUD_PASSWORD` | ❌ Must be empty | - | Leave empty to enable OAuth mode |
@@ -108,317 +110,6 @@ NEXTCLOUD_PASSWORD=your_app_password_or_password
---
## Semantic Search Configuration (Optional)
The MCP server includes semantic search capabilities powered by vector embeddings. This feature requires a vector database (Qdrant) and an embedding service.
### Qdrant Vector Database Modes
The server supports three Qdrant deployment modes:
1. **In-Memory Mode** (Default) - Simplest for development and testing
2. **Persistent Local Mode** - For single-instance deployments with persistence
3. **Network Mode** - For production with dedicated Qdrant service
#### 1. In-Memory Mode (Default)
No configuration needed! If neither `QDRANT_URL` nor `QDRANT_LOCATION` is set, the server defaults to in-memory mode:
```dotenv
# No Qdrant configuration needed - defaults to :memory:
VECTOR_SYNC_ENABLED=true
```
**Pros:**
- Zero configuration
- Fast startup
- Perfect for testing
**Cons:**
- Data lost on restart
- Limited to available RAM
#### 2. Persistent Local Mode
For single-instance deployments that need persistence without a separate Qdrant service:
```dotenv
# Local persistent storage
QDRANT_LOCATION=/app/data/qdrant # Or any writable path
VECTOR_SYNC_ENABLED=true
```
**Pros:**
- Data persists across restarts
- No separate service needed
- Suitable for small/medium deployments
**Cons:**
- Limited to single instance
- Shares resources with MCP server
#### 3. Network Mode
For production deployments with a dedicated Qdrant service:
```dotenv
# Network mode configuration
QDRANT_URL=http://qdrant:6333
QDRANT_API_KEY=your-secret-api-key # Optional
QDRANT_COLLECTION=nextcloud_content # Optional
VECTOR_SYNC_ENABLED=true
```
**Pros:**
- Scalable and performant
- Can be shared across multiple MCP instances
- Supports clustering and replication
**Cons:**
- Requires separate Qdrant service
- More complex deployment
### Qdrant Collection Naming
Collection names are automatically generated to include the embedding model, ensuring safe model switching and preventing dimension mismatches.
#### Auto-Generated Naming (Default)
**Format:** `{deployment-id}-{model-name}`
**Components:**
- **Deployment ID:** `OTEL_SERVICE_NAME` (if configured) or `hostname` (fallback)
- **Model name:** `OLLAMA_EMBEDDING_MODEL`
**Examples:**
```bash
# With OTEL service name configured
OTEL_SERVICE_NAME=my-mcp-server
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "my-mcp-server-nomic-embed-text"
# Simple Docker deployment (OTEL not configured)
# hostname=mcp-container
OLLAMA_EMBEDDING_MODEL=all-minilm
# → Collection: "mcp-container-all-minilm"
```
#### Switching Embedding Models
When you change `OLLAMA_EMBEDDING_MODEL`, a new collection is automatically created:
```bash
# Initial setup
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# Collection: "my-server-nomic-embed-text" (768 dimensions)
# Change model
OLLAMA_EMBEDDING_MODEL=all-minilm
# Collection: "my-server-all-minilm" (384 dimensions)
# → New collection created, full re-embedding occurs
```
**Important:**
- **Collections are mutually exclusive** - vectors cannot be shared between different embedding models
- **Switching models requires re-embedding** all documents (may take time for large note collections)
- **Old collection remains** in Qdrant and can be deleted manually if no longer needed
#### Explicit Override
Set `QDRANT_COLLECTION` to use a specific collection name:
```bash
QDRANT_COLLECTION=my-custom-collection # Bypasses auto-generation
```
**Use cases:**
- Backward compatibility with existing deployments
- Custom naming schemes
- Sharing a collection across deployments (advanced)
#### Multi-Server Deployments
Each server should have a unique deployment ID to avoid collection collisions:
```bash
# Server 1 (Production)
OTEL_SERVICE_NAME=mcp-prod
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-prod-nomic-embed-text"
# Server 2 (Staging)
OTEL_SERVICE_NAME=mcp-staging
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-staging-nomic-embed-text"
# Server 3 (Different model)
OTEL_SERVICE_NAME=mcp-experimental
OLLAMA_EMBEDDING_MODEL=bge-large
# → Collection: "mcp-experimental-bge-large"
```
**Benefits:**
- Multiple MCP servers can share one Qdrant instance safely
- No naming collisions between deployments
- Clear collection ownership (can see which deployment and model)
#### Dimension Validation
The server validates collection dimensions on startup:
```
Dimension mismatch for collection 'my-server-nomic-embed-text':
Expected: 384 (from embedding model 'all-minilm')
Found: 768
This usually means you changed the embedding model.
Solutions:
1. Delete the old collection: Collection will be recreated with new dimensions
2. Set QDRANT_COLLECTION to use a different collection name
3. Revert OLLAMA_EMBEDDING_MODEL to the original model
```
**What this prevents:**
- Runtime errors from dimension mismatches
- Data corruption in Qdrant
- Confusing error messages during indexing
### Vector Sync Configuration
Control background indexing behavior:
```dotenv
# Vector sync settings (ADR-007)
VECTOR_SYNC_ENABLED=true # Enable background indexing
VECTOR_SYNC_SCAN_INTERVAL=300 # Scan interval in seconds (default: 5 minutes)
VECTOR_SYNC_PROCESSOR_WORKERS=3 # Concurrent indexing workers (default: 3)
VECTOR_SYNC_QUEUE_MAX_SIZE=10000 # Max queued documents (default: 10000)
# Document chunking settings (for vector embeddings)
DOCUMENT_CHUNK_SIZE=512 # Words per chunk (default: 512)
DOCUMENT_CHUNK_OVERLAP=50 # Overlapping words between chunks (default: 50)
```
### Embedding Service Configuration
The server uses an embedding service to generate vector representations. Two options are available:
#### Ollama (Recommended)
Use a local Ollama instance for embeddings:
```dotenv
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_EMBEDDING_MODEL=nomic-embed-text # Default model
OLLAMA_VERIFY_SSL=true # Verify SSL certificates
```
#### Simple Embedding Provider (Fallback)
If `OLLAMA_BASE_URL` is not set, the server uses a simple random embedding provider for testing. This is **not suitable for production** as it generates random embeddings with no semantic meaning.
### Document Chunking Configuration
The server chunks documents before embedding to handle documents larger than the embedding model's context window. Chunk size and overlap can be tuned based on your embedding model and content type.
#### Choosing Chunk Size
**Smaller chunks (256-384 words)**:
- More precise matching
- Less context per chunk
- Better for finding specific information
- Higher storage requirements (more vectors)
**Larger chunks (768-1024 words)**:
- More context per chunk
- Less precise matching
- Better for understanding broader topics
- Lower storage requirements (fewer vectors)
**Default (512 words)**:
- Balanced approach suitable for most use cases
- Works well with typical note lengths
- Good compromise between precision and context
#### Choosing Overlap
Overlap preserves context across chunk boundaries. Recommended settings:
- **10-20% of chunk size** (e.g., 50-100 words for 512-word chunks)
- **Too small** (<10%): May lose context at boundaries
- **Too large** (>20%): Redundant storage, diminishing returns
**Examples**:
```dotenv
# Precise matching for short notes
DOCUMENT_CHUNK_SIZE=256
DOCUMENT_CHUNK_OVERLAP=25
# Default balanced configuration
DOCUMENT_CHUNK_SIZE=512
DOCUMENT_CHUNK_OVERLAP=50
# More context for long documents
DOCUMENT_CHUNK_SIZE=1024
DOCUMENT_CHUNK_OVERLAP=100
```
**Important**: Changing chunk size requires re-embedding all documents. The collection naming strategy (see "Qdrant Collection Naming" above) helps manage this by creating separate collections for different configurations.
### Environment Variables Reference
| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `QDRANT_URL` | ⚠️ Optional | - | Qdrant service URL (network mode) - mutually exclusive with `QDRANT_LOCATION` |
| `QDRANT_LOCATION` | ⚠️ Optional | `:memory:` | Local Qdrant path (`:memory:` or `/path/to/data`) - mutually exclusive with `QDRANT_URL` |
| `QDRANT_API_KEY` | ⚠️ Optional | - | Qdrant API key (network mode only) |
| `QDRANT_COLLECTION` | ⚠️ Optional | `nextcloud_content` | Qdrant collection name |
| `VECTOR_SYNC_ENABLED` | ⚠️ Optional | `false` | Enable background vector indexing |
| `VECTOR_SYNC_SCAN_INTERVAL` | ⚠️ Optional | `300` | Document scan interval (seconds) |
| `VECTOR_SYNC_PROCESSOR_WORKERS` | ⚠️ Optional | `3` | Concurrent indexing workers |
| `VECTOR_SYNC_QUEUE_MAX_SIZE` | ⚠️ Optional | `10000` | Max queued documents |
| `OLLAMA_BASE_URL` | ⚠️ Optional | - | Ollama API endpoint for embeddings |
| `OLLAMA_EMBEDDING_MODEL` | ⚠️ Optional | `nomic-embed-text` | Embedding model to use |
| `OLLAMA_VERIFY_SSL` | ⚠️ Optional | `true` | Verify SSL certificates |
| `DOCUMENT_CHUNK_SIZE` | ⚠️ Optional | `512` | Words per chunk for document embedding |
| `DOCUMENT_CHUNK_OVERLAP` | ⚠️ Optional | `50` | Overlapping words between chunks (must be < chunk size) |
### Docker Compose Example
Enable network mode Qdrant with docker-compose:
```yaml
services:
mcp:
environment:
- QDRANT_URL=http://qdrant:6333
- VECTOR_SYNC_ENABLED=true
qdrant:
image: qdrant/qdrant:latest
ports:
- 127.0.0.1:6333:6333
volumes:
- qdrant-data:/qdrant/storage
profiles:
- qdrant # Optional service
volumes:
qdrant-data:
```
Start with Qdrant service:
```bash
docker-compose --profile qdrant up
```
Or use default in-memory mode (no `--profile` needed):
```bash
docker-compose up
```
---
## Loading Environment Variables
After creating your `.env` file, load the environment variables:
@@ -469,6 +160,10 @@ Options:
NEXTCLOUD_OIDC_CLIENT_ID env var)
--oauth-client-secret TEXT OAuth client secret (can also use
NEXTCLOUD_OIDC_CLIENT_SECRET env var)
--oauth-storage-path TEXT Path to store OAuth client credentials
(can also use
NEXTCLOUD_OIDC_CLIENT_STORAGE env var)
[default: .nextcloud_oauth_client.json]
--mcp-server-url TEXT MCP server URL for OAuth callbacks (can
also use NEXTCLOUD_MCP_SERVER_URL env
var) [default: http://localhost:8000]
@@ -530,7 +225,10 @@ uv run nextcloud-mcp-server --no-oauth \
- Store OAuth client credentials securely
- Use environment variables from your deployment platform (Docker secrets, Kubernetes ConfigMaps, etc.)
- Never commit credentials to version control
- SQLite database permissions are handled automatically by the server
- Set appropriate file permissions on credential storage:
```bash
chmod 600 .nextcloud_oauth_client.json
```
### For Docker
+11 -10
View File
@@ -272,7 +272,7 @@ mcp-oauth:
**Key Points:**
- **No credentials needed** - DCR automatically registers the client on first start
- **Credentials persist** - Saved to SQLite database and reused
- **Credentials persist** - Saved to `.nextcloud_oauth_client.json` and reused
- **JWT tokens** - Use `--oauth-token-type jwt` for better performance
- **Token verifier supports both** - Can handle JWT and opaque tokens
- **Pre-configured credentials** - Providing `CLIENT_ID`/`CLIENT_SECRET` skips DCR
@@ -286,6 +286,7 @@ mcp-oauth:
| `NEXTCLOUD_PUBLIC_ISSUER_URL` | Public issuer URL for JWT validation | (uses `NEXTCLOUD_HOST`) |
| `NEXTCLOUD_OIDC_CLIENT_ID` | Pre-configured OAuth client ID | (optional - uses DCR if unset) |
| `NEXTCLOUD_OIDC_CLIENT_SECRET` | Pre-configured OAuth client secret | (optional - uses DCR if unset) |
| `NEXTCLOUD_OIDC_CLIENT_STORAGE` | Path to persist DCR-registered credentials | `.nextcloud_oauth_client.json` |
| `NEXTCLOUD_OIDC_SCOPES` | Space-separated scopes to request | `"openid profile email mcp:notes:read mcp:notes:write"` |
| `NEXTCLOUD_OIDC_TOKEN_TYPE` | Token format: `"jwt"` or `"Bearer"` | `"Bearer"` |
@@ -302,8 +303,8 @@ When the MCP server starts in OAuth mode, it follows this **three-tier credentia
├─ NEXTCLOUD_OIDC_CLIENT_ID
└─ NEXTCLOUD_OIDC_CLIENT_SECRET
2. SQLite Database (Second Priority)
└─ OAuth client credentials table
2. Storage File (Second Priority)
└─ NEXTCLOUD_OIDC_CLIENT_STORAGE (.nextcloud_oauth_client.json)
3. Dynamic Client Registration (Automatic Fallback)
├─ Discovers registration endpoint from /.well-known/openid-configuration
@@ -326,10 +327,10 @@ export NEXTCLOUD_OIDC_TOKEN_TYPE=jwt # or "Bearer" for opaque tokens
**Credential Storage:**
- Registered credentials are saved to SQLite database
- Database is encrypted and protected by file system permissions
- Registered credentials are saved to `NEXTCLOUD_OIDC_CLIENT_STORAGE` (default: `.nextcloud_oauth_client.json`)
- File has restrictive permissions (0600 - owner read/write only)
- Credentials are reused on subsequent starts (no re-registration needed)
- Stored credentials are checked for expiration (auto-regenerates if expired)
- Storage file is checked for expiration (auto-regenerates if expired)
**Format:**
```json
@@ -385,9 +386,9 @@ export NEXTCLOUD_OIDC_CLIENT_ID="<client_id>"
export NEXTCLOUD_OIDC_CLIENT_SECRET="<client_secret>"
export NEXTCLOUD_OIDC_TOKEN_TYPE="jwt"
# Option 2: SQLite database (second priority)
# Credentials are automatically saved to the database after DCR
# Server will automatically load them on startup
# Option 2: Storage file (second priority)
# Save the JSON response to .nextcloud_oauth_client.json
# Server will automatically load it on startup
```
When credentials are provided via environment variables or storage file, **DCR is skipped**.
@@ -723,7 +724,7 @@ docker compose exec db mariadb -u nextcloud -ppassword nextcloud \
1. Ensure `NEXTCLOUD_OIDC_SCOPES` environment variable is set correctly
2. Check MCP server startup logs for the scopes being requested
3. Verify DCR is enabled in Nextcloud OIDC app settings
4. Clear the SQLite database OAuth client entry and restart to force re-registration
4. Delete `.nextcloud_oauth_client.json` and restart to force re-registration
### Issue: Token Type Case Sensitivity
-298
View File
@@ -1,298 +0,0 @@
# Keycloak Multi-Client Token Validation
## Executive Summary
**Question**: Can Nextcloud's `user_oidc` app (configured with client A) validate bearer tokens from client B in the same Keycloak realm?
**Answer**: ✅ **YES** - user_oidc validates tokens at the **realm level**, not per-client.
## Test Results
### Setup
- **Keycloak Realm**: `nextcloud-mcp`
- **Provider in user_oidc**: Configured with `mcp-client` credentials
- **Test**: Get token from `test-client-b`, validate via Nextcloud API
### Result
```bash
# Token from test-client-b (client B)
$ TOKEN=$(curl -X POST ".../token" -d "client_id=test-client-b" ...)
# Validated successfully by Nextcloud (configured with mcp-client = client A)
$ curl -H "Authorization: Bearer $TOKEN" "http://nextcloud/ocs/.../capabilities"
HTTP/1.1 200 OK
{"ocs":{"meta":{"status":"ok"}}}
```
**Token from client B validated successfully!**
## How It Works
### Token Structure from Keycloak
**Access Token** (password grant):
```json
{
"iss": "http://keycloak/realms/nextcloud-mcp",
"azp": "test-client-b", // Authorized party = client B
"typ": "Bearer",
"exp": 1234567890,
// NO "sub" claim
// NO "aud" claim
"scope": "openid profile email"
}
```
**ID Token** (for comparison):
```json
{
"iss": "http://keycloak/realms/nextcloud-mcp",
"aud": "test-client-b", // Audience = client B
"sub": "923da741-7ebe-4cf9-baf2-37fcf2ecc95d",
"azp": "test-client-b"
}
```
**Key Observation**: Access tokens from Keycloak's password grant **do not contain** `sub` or `aud` claims!
### Validation Flow in user_oidc
From source code analysis (`~/Software/user_oidc/lib/User/Backend.php`):
```
1. Request with Bearer token arrives
2. user_oidc loops through providers with checkBearer=true
3. Try SelfEncodedValidator (JWT/JWKS validation):
- Validates JWT signature using Keycloak's JWKS
- Tries to extract 'sub' claim → FAILS (no sub in access token)
4. Fallback to UserInfoValidator:
- Calls Keycloak userinfo endpoint with bearer token
- Keycloak validates token server-side
- Returns userinfo with 'sub' claim
→ SUCCESS!
5. User identified, request authorized
```
### Why This Works
**Realm-Level Trust**:
- Keycloak's userinfo endpoint validates ANY valid token from the realm
- It doesn't matter which client issued the token
- The token is validated by Keycloak itself (via userinfo call)
**No Audience Check**:
- Access tokens have no `aud` claim
- SelfEncodedValidator's audience check is bypassed (no audience to validate)
- UserInfoValidator doesn't check audience (delegates to Keycloak)
**Client Credentials Role**:
- The configured `client_id`/`client_secret` in user_oidc are **NOT used** for bearer token validation
- They're only used for OAuth login flows (authorization code exchange)
- Userinfo endpoint doesn't require client authentication
## Source Code Evidence
### SelfEncodedValidator - Audience Check
```php
// ~/Software/user_oidc/lib/User/Validator/SelfEncodedValidator.php:64-76
$checkAudience = !isset($oidcSystemConfig['selfencoded_bearer_validation_audience_check'])
|| !in_array($oidcSystemConfig['selfencoded_bearer_validation_audience_check'],
[false, 'false', 0, '0'], true);
if ($checkAudience) {
$tokenAudience = $payload->aud ?? null;
if ((is_string($tokenAudience) && $tokenAudience !== $providerClientId)
|| (is_array($tokenAudience) && !in_array($providerClientId, $tokenAudience))) {
$this->logger->debug('Audience does not match client ID');
return null; // REJECT
}
}
// If $tokenAudience is null (our case), both conditions are false → validation continues
```
### UserInfoValidator - No Client Auth
```php
// ~/Software/user_oidc/lib/Service/OIDCService.php:28-45
public function userinfo(Provider $provider, string $accessToken): array {
$url = $this->discoveryService->obtainDiscovery($provider)['userinfo_endpoint'];
// Bearer token passed directly - NO client credentials used
$options = ['headers' => ['Authorization' => 'Bearer ' . $accessToken]];
return json_decode($this->clientService->get($url, [], $options), true);
}
```
### Keycloak Userinfo Response
```bash
$ curl -H "Authorization: Bearer $TOKEN_FROM_CLIENT_B" \
"http://keycloak/realms/nextcloud-mcp/protocol/openid-connect/userinfo"
{
"sub": "923da741-7ebe-4cf9-baf2-37fcf2ecc95d",
"email_verified": true,
"name": "Admin User",
"email": "admin@example.com"
}
```
Keycloak validates the token **regardless of which client issued it**, as long as it's from the same realm.
## Implications for Your Architecture
### Desired Architecture
```
MCP Server (client A) ← DCR with Keycloak
MCP Clients (client B, C, D...) ← DCR with Keycloak
Nextcloud user_oidc ← configured once with any client from realm
```
### What This Means
**You can do exactly what you want!**
1. **Configure user_oidc once** with any client from the Keycloak realm (e.g., a dedicated `nextcloud-validator` client)
2. **MCP Server registers via DCR** as a unique client (e.g., `mcp-server-abc123`)
- Gets its own client credentials
- Issues tokens with `azp: "mcp-server-abc123"`
- These tokens will be validated by user_oidc!
3. **MCP Clients also use DCR** (each gets unique identity)
- Client A: `client-123`
- Client B: `client-456`
- Tokens from all clients validated by user_oidc!
4. **Tokens from ANY client** in the realm can access Nextcloud APIs
- user_oidc validates via Keycloak userinfo endpoint
- Realm-level trust (not per-client)
### Configuration
**Step 1: Configure user_oidc Provider**
```bash
php occ user_oidc:provider keycloak-realm \
--clientid="nextcloud-validator" \
--clientsecret="***" \
--discoveryuri="https://keycloak/realms/my-realm/.well-known/openid-configuration" \
--check-bearer=1 \
--bearer-provisioning=1
```
**Step 2: MCP Server Registers with Keycloak (DCR)**
```python
# MCP server startup
registration_response = await keycloak_client.register_client(
client_name="MCP Server Instance",
redirect_uris=["http://mcp-server/oauth/callback"]
)
# Store: client_id, client_secret
```
**Step 3: Issue Tokens to Users**
- Users authenticate via Keycloak
- MCP server gets tokens issued to its `client_id`
- These tokens validated by user_oidc!
**Step 4: Background Operations (ADR-002)**
- Store user refresh tokens (encrypted)
- Refresh access tokens as needed
- All tokens validated by user_oidc regardless of issuing client
## Important Notes
### Token Grant Types Matter
**Password Grant** (what we tested):
- Access tokens have NO `sub` or `aud`
- Forces validation via userinfo endpoint
- Works with any client in realm
**Authorization Code Grant** (production):
- Tokens MAY include `aud` claim
- Need to verify behavior with real OAuth flows
- May require disabling audience check
### Recommendation for Production
**Option 1: Disable Audience Check (Simplest)**
```php
// config.php
'user_oidc' => [
'selfencoded_bearer_validation_audience_check' => false,
],
```
**Option 2: Rely on UserInfo Validation**
```php
// config.php
'user_oidc' => [
'userinfo_bearer_validation' => true, // Enable userinfo validation
],
```
**Option 3: Configure Keycloak to Not Include aud in Access Tokens**
- Keep default behavior (works as tested)
- Tokens validated via userinfo endpoint
## Testing Script
```bash
#!/bin/bash
# Test multi-client validation
# Create second client in Keycloak
curl -X POST "http://keycloak/admin/realms/my-realm/clients" \
-H "Authorization: Bearer $ADMIN_TOKEN" \
-d '{
"clientId": "test-client-b",
"secret": "test-secret-b",
"standardFlowEnabled": true,
"directAccessGrantsEnabled": true
}'
# Get token from client B
TOKEN=$(curl -X POST "http://keycloak/realms/my-realm/protocol/openid-connect/token" \
-d "grant_type=password" \
-d "client_id=test-client-b" \
-d "client_secret=test-secret-b" \
-d "username=testuser" \
-d "password=password" | jq -r '.access_token')
# Test with Nextcloud (configured with client A)
curl -H "Authorization: Bearer $TOKEN" \
"http://nextcloud/ocs/v2.php/cloud/capabilities"
# Should return 200 OK!
```
## Conclusion
**Your proposed architecture is fully supported!**
- user_oidc configured once with ANY client from Keycloak realm
- MCP server registers dynamically via DCR
- MCP clients also register dynamically
- ALL tokens from realm validated successfully
- No per-client configuration needed
The key insight: **user_oidc validates tokens at the realm level** (via Keycloak's userinfo endpoint), not at the client level.
## References
- Source code: `~/Software/user_oidc/lib/User/Backend.php:260-343`
- SelfEncodedValidator: `~/Software/user_oidc/lib/User/Validator/SelfEncodedValidator.php`
- UserInfoValidator: `~/Software/user_oidc/lib/User/Validator/UserInfoValidator.php`
- Test setup: `docker-compose.yml` (mcp-keycloak service)
- Configuration: `.env.keycloak.sample`
+1 -3
View File
@@ -8,9 +8,7 @@
| `nc_notes_update_note` | Update an existing note by ID |
| `nc_notes_append_content` | Append content to an existing note with a clear separator |
| `nc_notes_delete_note` | Delete a note by ID |
| `nc_notes_search_notes` | Search notes by title or content (keyword search) |
| `nc_notes_semantic_search` | Search notes by meaning using vector embeddings (requires vector sync) |
| `nc_notes_semantic_search_answer` | Search notes semantically and generate a natural language answer via MCP sampling (requires vector sync and sampling-capable MCP client) |
| `nc_notes_search_notes` | Search notes by title or content |
### Note Attachments
-323
View File
@@ -1,323 +0,0 @@
# OAuth Architecture Comparison: MCP Server Authentication Patterns
This document compares three authentication architectures for the MCP server, explaining the evolution from pass-through authentication to true offline access capabilities.
## Pattern 1: Pass-Through Authentication (Current Implementation)
### Architecture
```
┌─────────────┐ OAuth Flow ┌─────────────┐
│ MCP Client │◄──────────────────│ OAuth │
│ (Claude) │ │ Provider │
└──────┬──────┘ └─────────────┘
│ Access Token
│ (per request)
┌─────────────┐ ┌─────────────┐
│ MCP Server │───────────────────►│ Nextcloud │
│(Pass-through) │ APIs │
└─────────────┘ └─────────────┘
```
### Characteristics
| Aspect | Description |
|--------|-------------|
| **Token Flow** | MCP Client → MCP Server → Nextcloud |
| **Token Storage** | None (tokens exist only during request) |
| **Offline Access** | ❌ Impossible |
| **Background Workers** | ❌ Not supported |
| **User Consent** | Single OAuth flow (client-managed) |
| **Complexity** | Low |
| **Security** | High (no token persistence) |
### How It Works
1. MCP Client performs OAuth with provider
2. Client includes access token in each MCP request
3. MCP Server validates token and forwards to Nextcloud
4. Token discarded after request completes
### Limitations
- No operations possible without active MCP session
- Background sync/indexing impossible
- Cannot refresh tokens independently
---
## Pattern 2: Token Exchange Delegation (ADR-002 - Flawed)
### Architecture
```
┌─────────────┐ ┌─────────────┐
│ MCP Client │────────────────────│ OAuth │
│ (Claude) │ │ Provider │
└──────┬──────┘ └──────┬──────┘
│ │
│ Access Token │ Service Account Token
▼ ▼
┌─────────────────────────────────────────────┐
│ MCP Server │
│ ┌────────────────────────────────────┐ │
│ │ Token Exchange (RFC 8693) │ │
│ │ Subject: Service Account │ │
│ │ Target: User │ │
│ └────────────────────────────────────┘ │
└───────────────┬─────────────────────────────┘
│ Exchanged Token
┌─────────────┐
│ Nextcloud │
│ APIs │
└─────────────┘
```
### Characteristics
| Aspect | Description |
|--------|-------------|
| **Token Flow** | Service Account → Exchange → User Token |
| **Token Storage** | None (MCP server still stateless) |
| **Offline Access** | ❌ Still impossible (circular dependency) |
| **Background Workers** | ❌ Requires service account (rejected) |
| **User Consent** | Implicit through service account |
| **Complexity** | High |
| **Security** | ⚠️ Service accounts violate OAuth principles |
### Why It Fails
1. **Circular Dependency**: To exchange tokens, you need a token to exchange
2. **Service Account Problem**: Creates Nextcloud user identity for service
3. **OAuth Violation**: Service acts as itself, not on behalf of users
4. **No Bootstrap**: Still can't obtain initial tokens offline
### The Fatal Flaw
```
Q: How does background worker get tokens?
A: Use token exchange with service account
Q: How does service account get authorized?
A: Client credentials grant creates user account (violates OAuth)
Q: Can we use user's refresh token?
A: MCP server never sees refresh tokens (by design)
```
---
## Pattern 3: Sign-in with Nextcloud (Previous ADR-004 Draft)
### Architecture
```
┌─────────────┐ ┌─────────────────┐ ┌────────────┐
│ MCP Client ├───────────────────> │ MCP Server ├────────────────────>│ Nextcloud │
│ (Claude) │ (MCP Protocol) │ (OAuth Client) │ (OIDC + APIs) │ (IdP) │
└─────────────┘ └─────────────────┘ └────────────┘
┌──────▼────────┐
│ Token Storage │
│ (NC Tokens) │
└───────────────┘
```
### Characteristics
| Aspect | Description |
|--------|-------------|
| **Token Flow** | MCP Server uses Nextcloud as identity provider |
| **Token Storage** | ✅ Encrypted Nextcloud refresh tokens |
| **Offline Access** | ✅ Full support |
| **Background Workers** | ✅ Use stored refresh tokens |
| **User Consent** | Single OAuth flow (Nextcloud only) |
| **Complexity** | Medium |
| **Security** | High (with token rotation) |
### How It Works
1. **Initial Setup**:
- User tries to use MCP tool
- MCP server returns auth required
- User authenticates with Nextcloud's OIDC endpoint
- Nextcloud may use user_oidc to delegate to external IdP (Keycloak, etc.)
- MCP server stores Nextcloud-issued refresh token (encrypted)
2. **Subsequent Requests**:
- MCP server uses stored Nextcloud tokens
- Refreshes automatically when expired
- No client involvement needed
3. **Background Operations**:
- Worker retrieves stored refresh token
- Refreshes with Nextcloud directly
- Performs operations independently
### Advantages
- ✅ Single sign-on with Nextcloud
- ✅ True offline access capability
- ✅ OAuth-compliant with proper consent
- ✅ Supports external IdPs via user_oidc
- ✅ Simpler integration - only one OAuth endpoint
### Trade-offs
- Authentication flows through Nextcloud
- Nextcloud manages IdP relationships (via user_oidc)
- MCP server only knows about Nextcloud, not the underlying IdP
---
## Pattern 4: Federated Authentication Architecture (ADR-004 - Solution)
### Architecture
```
┌─────────────┐ ┌─────────────────┐ ┌──────────────┐ ┌────────────┐
│ MCP Client │◄──────401──────│ MCP Server │◄────OAuth──────│ Shared IdP │──Validates──►│ Nextcloud │
│ (Claude) │ │ (OAuth Client) │ (On-Behalf) │ (Keycloak) │ Tokens │(Resource) │
└─────────────┘ └─────────────────┘ └──────────────┘ └────────────┘
┌───────▼────────┐
│ Token Storage │
│ (IdP Tokens) │
└────────────────┘
```
### Characteristics
| Aspect | Description |
|--------|-------------|
| **Token Flow** | Shared IdP issues tokens for Nextcloud access |
| **Token Storage** | ✅ Encrypted IdP refresh tokens |
| **Offline Access** | ✅ Full support |
| **Background Workers** | ✅ Use stored IdP refresh tokens |
| **User Consent** | Single OAuth flow (IdP manages consent) |
| **Complexity** | Medium-High |
| **Security** | Highest (enterprise-grade IdP) |
### How It Works
1. **Initial Setup**:
- MCP client connects, receives 401
- Browser opens MCP server OAuth URL
- MCP server redirects to shared IdP
- User authenticates once to IdP
- IdP shows consent for both identity and Nextcloud access
- MCP server stores IdP refresh token (encrypted)
- MCP server issues session token to client
2. **Subsequent Requests**:
- MCP server validates session token
- Uses stored IdP token for Nextcloud
- Refreshes with IdP when expired
- No client involvement needed
3. **Background Operations**:
- Worker retrieves stored IdP refresh token
- Gets new access token from IdP
- Uses token to access Nextcloud
- Performs operations independently
### Advantages
- ✅ True single sign-on (SSO)
- ✅ Enterprise-ready with SAML/LDAP support
- ✅ OAuth-compliant with proper delegation
- ✅ Direct IdP relationship - no intermediary
- ✅ Flexible - can swap resource servers
- ✅ Industry-standard federated pattern
### Trade-offs
- Requires shared IdP infrastructure
- More complex initial setup
- Token validation overhead
---
## Comparison Matrix
| Feature | Pass-Through | Token Exchange | Sign-in with NC | Federated Auth |
|---------|--------------|----------------|-----------------|----------------|
| **Offline Access** | ❌ No | ❌ No | ✅ Yes | ✅ Yes |
| **Background Workers** | ❌ No | ❌ No* | ✅ Yes | ✅ Yes |
| **Token Storage** | None | None | NC refresh tokens | IdP refresh tokens |
| **OAuth Compliance** | ✅ Full | ⚠️ Violates | ✅ Full | ✅ Full |
| **User Consent** | Once | Implicit | Once (NC) | Once (IdP) |
| **Implementation Complexity** | Low | High | Medium | Medium-High |
| **Security** | High | Medium | High | Highest |
| **Enterprise Ready** | ❌ No | ❌ No | ⚠️ Indirect | ✅ Yes |
| **Identity Provider** | Client-managed | N/A | Nextcloud (+user_oidc) | Shared IdP |
| **Suitable For** | Interactive only | N/A (flawed) | Small teams | Enterprise |
\* *Requires service accounts that violate OAuth principles*
---
## Evolution Summary
### Stage 1: Simple Pass-Through ✅
- **Goal**: Basic MCP functionality
- **Result**: Works well for interactive use
- **Limitation**: No offline capabilities
### Stage 2: Attempted Delegation ❌
- **Goal**: Enable offline access without changing architecture
- **Result**: Circular dependencies, OAuth violations
- **Learning**: MCP protocol constraints are fundamental
### Stage 3: Sign-in with Nextcloud ⚠️
- **Goal**: True offline access with OAuth compliance
- **Result**: MCP server uses Nextcloud as identity provider
- **Limitation**: Tight coupling to Nextcloud, no enterprise IdP
### Stage 4: Federated Pattern ✅
- **Goal**: Enterprise-ready offline access
- **Result**: Shared IdP for both MCP server and Nextcloud
- **Trade-off**: Additional infrastructure justified by enterprise needs
---
## Key Insights
1. **Pattern 3 vs Pattern 4**: Both support external IdPs, but differ in integration approach:
- Pattern 3: MCP → Nextcloud OIDC → (user_oidc) → External IdP
- Pattern 4: MCP → External IdP directly (Nextcloud also uses same IdP)
- Choose Pattern 3 for Nextcloud-centric deployments, Pattern 4 for IdP-centric enterprises
2. **The MCP Protocol Boundary**: The MCP protocol creates a fundamental boundary between client and server token management. Attempting to breach this boundary (ADR-002) leads to architectural contradictions.
3. **Service Accounts Don't Solve User Problems**: Using service accounts for user operations violates OAuth's core principle of acting on behalf of users, not as a service identity.
4. **Double OAuth is Industry Standard**: Major platforms (Zapier, IFTTT, Microsoft Power Automate) use this pattern - the integration platform is an OAuth client that maintains its own relationships with upstream services.
5. **Refresh Tokens Are The Solution**: The OAuth spec designed refresh tokens specifically for offline access. Rejecting them (as ADR-002 did) means rejecting the standard solution.
6. **Complexity is Justified**: The additional complexity of managing OAuth flows is acceptable when offline access is a requirement. The alternative is no offline access at all.
---
## Recommendations
### For Simple Deployments
Use **Pattern 1 (Pass-Through)** if:
- Offline access not needed
- Only interactive operations required
- Simplicity is priority
### For Teams Using Nextcloud
Use **Pattern 3 (Sign-in with Nextcloud)** if:
- Background sync/indexing required
- Nextcloud manages your authentication
- Can use external IdPs via user_oidc
- Prefer single integration point through Nextcloud
### For Enterprise Deployments
Use **Pattern 4 (Federated Authentication)** if:
- Enterprise IdP already exists (Keycloak, Okta, Azure AD)
- Multiple resource servers beyond Nextcloud
- Compliance requirements for centralized auth
- Building platform for multiple organizations
### Never Use Pattern 2
Token Exchange with service accounts should not be used as it:
- Doesn't enable true offline access
- Violates OAuth principles
- Adds complexity without solving the problem
---
## References
- [ADR-002: Vector Database Background Sync Authentication (Deprecated)](./ADR-002-vector-sync-authentication.md)
- [ADR-004: MCP Server as OAuth Client for Offline Access](./ADR-004-mcp-application-oauth.md)
- [RFC 6749: OAuth 2.0 Framework](https://datatracker.ietf.org/doc/html/rfc6749)
- [RFC 8693: OAuth 2.0 Token Exchange](https://datatracker.ietf.org/doc/html/rfc8693)
+5 -10
View File
@@ -39,7 +39,7 @@ Phase 0: MCP Server Startup & Client Registration (DCR - RFC 7591)
│ 0d. Client credentials │
│<────────────────────────────────────┤
│ {client_id, client_secret} │
│ → Saved to SQLite database
│ → Saved to .nextcloud_oauth_*.json
│ │
│ ✓ Server ready for connections │
@@ -211,7 +211,7 @@ Insufficient Scope Example (Step-Up Authorization)
- **PKCE Validation**: Verifies server advertises S256 code challenge method
- **Dynamic Client Registration (DCR)**: Automatically registers OAuth client via `/apps/oidc/register` (RFC 7591)
- Or loads pre-configured client credentials
- Saves credentials to SQLite database
- Saves credentials to `.nextcloud_oauth_client.json`
- **Tool Registration**: Loads all MCP tools with their `@require_scopes` decorators
#### Client Connection Phase
@@ -324,7 +324,7 @@ The OAuth flow consists of four distinct phases (see diagram above for visual re
- MCP server registers itself as OAuth client (RFC 7591)
- Provides: client name, redirect URIs, requested scopes, token type
- Receives: `client_id`, `client_secret`
- Saves credentials to SQLite database
- Saves credentials to `.nextcloud_oauth_client.json`
3. **Tool Registration**
- All MCP tools loaded with their `@require_scopes` decorators
@@ -515,7 +515,7 @@ NEXTCLOUD_HOST=https://nextcloud.example.com
**How it works**:
1. Server checks `/.well-known/openid-configuration` for `registration_endpoint`
2. Calls `/apps/oidc/register` to register a client on first startup
3. Saves credentials to SQLite database
3. Saves credentials to `.nextcloud_oauth_client.json`
4. Reuses these credentials on subsequent startups
5. Re-registers only if credentials are missing or expired
@@ -634,12 +634,6 @@ The server supports the following OAuth scopes, organized by Nextcloud app:
- `sharing:read` - List shares and read share information
- `sharing:write` - Create, update, and delete shares
#### Semantic Search (Multi-App Vector Database)
- `semantic:read` - Query vector database, perform semantic search across all indexed Nextcloud apps (notes, calendar, deck, files, contacts)
- `semantic:write` - Enable/disable background vector synchronization, manage indexing settings
> **Note**: Semantic search scopes provide access to the vector database that indexes content across **all** Nextcloud apps. Unlike app-specific scopes (e.g., `notes:read`), semantic scopes grant cross-app search capabilities powered by background vector synchronization (ADR-007).
### Scope Discovery
The MCP server provides scope discovery through two mechanisms:
@@ -724,6 +718,7 @@ See [Configuration Guide](configuration.md) for all OAuth environment variables:
| `NEXTCLOUD_OIDC_CLIENT_ID` | Pre-configured client ID (optional) |
| `NEXTCLOUD_OIDC_CLIENT_SECRET` | Pre-configured client secret (optional) |
| `NEXTCLOUD_MCP_SERVER_URL` | MCP server URL for OAuth callbacks |
| `NEXTCLOUD_OIDC_CLIENT_STORAGE` | Path for auto-registered credentials |
## Testing
-387
View File
@@ -1,387 +0,0 @@
# OAuth Impersonation Investigation Findings
**Date**: 2025-11-02
**Last Updated**: 2025-11-02 (Token Exchange Resolution)
**Status**: Implementation Complete - Token Exchange Working
**Conclusion**: Keycloak Standard Token Exchange (RFC 8693) working for internal-to-internal token exchange. User impersonation requires Legacy V1.
---
## ⚠️ IMPORTANT UPDATE (2025-11-02)
**This document contains outdated information regarding service account tokens.**
After implementation and testing, we discovered that service account tokens (`client_credentials` grant) **violate OAuth "act on-behalf-of" principles** by creating Nextcloud user accounts (e.g., `service-account-nextcloud-mcp-server`). This approach has been **REJECTED** and moved to ADR-002's "Will Not Implement" section.
**Key Changes:**
-**Service account tokens (client_credentials) are INVALID** - Creates user accounts, breaks audit trail
-**Token exchange (RFC 8693) is the correct approach** - Implemented and working (ADR-002 Tier 2)
-**Offline access with refresh tokens** - Still valid for background operations (ADR-002 primary approach)
**For current architecture, see**: `docs/ADR-002-vector-sync-authentication.md`
---
## Summary
We investigated options for implementing user impersonation to enable background operations without requiring admin credentials (ADR-002 Tier 2). Here are the findings:
## 1. Keycloak Token Exchange (RFC 8693)
### What We Implemented
- ✅ Service account token acquisition (`client_credentials` grant)
-`get_service_account_token()` method in `KeycloakOAuthClient`
-`exchange_token_for_user()` method implementing RFC 8693
- ✅ Token exchange configuration in Keycloak realm
### What Works ✅
**Keycloak Standard V2 Token Exchange (RFC 8693) is WORKING**:
- ✅ Service account token acquisition via `client_credentials` grant
- ✅ Token exchange for internal-to-internal tokens
- ✅ Audience and scope modifications
- ✅ Integration with Nextcloud APIs using exchanged tokens
**Configuration Requirements**:
To enable Standard Token Exchange in Keycloak 26.2+, add to client attributes in `realm-export.json`:
```json
"attributes": {
"token.exchange.grant.enabled": "true",
"client.token.exchange.standard.enabled": "true"
}
```
### Limitations
Keycloak Standard V2 does NOT support:
- ❌ User impersonation (`requested_subject` parameter)
- ❌ Cross-client delegation (limited to same realm)
These features require Legacy V1 with `--features=preview`
### Alternative: Keycloak Legacy V1
Keycloak Legacy Token Exchange (V1) WOULD support user impersonation, but:
- ❌ Requires `--features=preview --features=token-exchange` flag
- ❌ Not suitable for production
- ❌ Deprecated and being phased out
**Decision**: Not viable for production use.
---
## 2. Nextcloud OIDC App Token Exchange
### Discovery Endpoint Analysis
```json
{
"grant_types_supported": [
"authorization_code",
"implicit"
]
}
```
### Findings
**Nextcloud OIDC app does NOT support**:
- RFC 8693 token exchange
- `client_credentials` grant
- `refresh_token` grant (refresh tokens not issued)
- User impersonation APIs
The Nextcloud OIDC app is a basic OAuth 2.0 provider focused on:
- Authorization code flow for user login
- JWT tokens for API access
- Scope-based authorization
It is NOT designed for:
- Service accounts
- Token delegation
- Background operations
**Decision**: Not viable - missing required grant types.
---
## 3. Nextcloud Impersonate App
### What It Provides
✅ Admin users can impersonate other users via:
- UI: Settings → Users → Impersonate button
- API: `POST /apps/impersonate/user` with `userId` parameter
### How It Works
```php
// From SettingsController.php
public function impersonate(string $userId): JSONResponse {
// 1. Verify admin/delegated admin permissions
// 2. Check target user has logged in before
// 3. Set session: $this->userSession->setUser($impersonatee)
// 4. Return success
}
```
### Requirements
- ✅ Admin credentials
- ✅ Session-based authentication (cookies)
- ✅ CSRF token
- ✅ Target user must have logged in at least once
- ❌ Not compatible with encryption-enabled instances
### Limitations for Background Workers
**Session-based, not stateless**:
- Requires maintaining HTTP session/cookies
- Not suitable for distributed workers
- Can't use with bearer tokens
- Requires re-authentication periodically
**Security concerns**:
- Requires admin credentials stored on server
- All impersonated actions logged as target user
- Violates principle of least privilege
**Decision**: Not suitable for background operations - session-based architecture incompatible with stateless OAuth/bearer token model.
---
## 4. What Actually Works
### Option A: Admin Credentials (Current Implementation)
**BasicAuth mode with admin account**:
```python
client = NextcloudClient.from_env() # Uses NEXTCLOUD_USERNAME/PASSWORD
# Can access all APIs with admin permissions
```
**Pros**:
- Simple, works immediately
- Full access to all APIs
**Cons**:
- Requires admin credentials stored on server
- No per-user permission scoping
- Security risk if credentials leaked
- Violates ADR-002 goals
**Status**: Available but not recommended for production.
### Option B: Service Account with Scoped Permissions
**Create dedicated service account**:
1. Create `mcp-sync` user in Nextcloud
2. Grant specific permissions (group memberships, shares)
3. Use those credentials for background operations
**Pros**:
- Dedicated account, easier to audit
- Can limit permissions via Nextcloud groups
- Works with current BasicAuth implementation
**Cons**:
- Still requires credentials storage
- Can't truly act "as" individual users
- Limited by Nextcloud's permission model
**Status**: Best available option without OAuth delegation.
---
## 5. Recommendations
### Short Term (Immediate)
**Use Service Account Pattern**:
```python
# Background worker configuration
SYNC_ACCOUNT_USERNAME=mcp-sync
SYNC_ACCOUNT_PASSWORD=<secure-password>
# Create service account with limited permissions
docker compose exec app php occ user:add mcp-sync
docker compose exec app php occ group:adduser <appropriate-group> mcp-sync
```
**Benefits**:
- Works with existing implementation
- Better than admin credentials
- Auditable
### Medium Term (If OAuth Delegation Required)
**Wait for proper standards support**:
- Monitor Keycloak for Standard V2 improvements
- Contribute to/request Nextcloud OIDC app enhancements
- Consider alternative identity providers (e.g., Authelia, Authentik)
### Long Term (Ideal Solution)
**Implement proper OAuth delegation**:
1. Use identity provider that supports RFC 8693 properly (e.g., Auth0, Okta)
2. Or implement custom delegation endpoint in Nextcloud
3. Or propose MCP protocol extension for refresh token sharing
---
## 6. Updated ADR-002 Status
| Tier | Solution | Status | Viability |
|------|----------|--------|-----------|
| **Tier 0** | Admin BasicAuth | ✅ Implemented | ⚠️ Works but not recommended |
| **Tier 1** | Offline Access (Refresh Tokens) | ⚠️ Infrastructure ready | ❌ MCP protocol limitation |
| **Tier 2** | Token Exchange (RFC 8693) | ✅ **WORKING** | ✅ **Internal token exchange functional** |
| **Tier 3** | Service Account (NEW) | ✅ Available | ✅ **RECOMMENDED for background ops** |
---
## 7. Implementation Status
### What Was Built
1.`RefreshTokenStorage` - SQLite + encryption (ready for future use)
2.`KeycloakOAuthClient.get_service_account_token()` - Works
3.`KeycloakOAuthClient.exchange_token_for_user()` - Implemented but non-functional
4. ✅ Token exchange configuration - Keycloak realm updated
5. ✅ Test scripts - Comprehensive testing completed
### What to Use
**For Background Operations**:
```python
# Use service account with BasicAuth
from nextcloud_mcp_server.client import NextcloudClient
# In background worker
sync_client = NextcloudClient(
base_url=os.getenv("NEXTCLOUD_HOST"),
username=os.getenv("SYNC_ACCOUNT_USERNAME"),
password=os.getenv("SYNC_ACCOUNT_PASSWORD"),
)
# Perform operations
notes = await sync_client.notes.search_notes("important")
# Index to vector database, etc.
```
**For User Requests**:
```python
# Continue using OAuth bearer tokens
# Per-request client creation as currently implemented
client = get_client_from_context(ctx, nextcloud_host)
```
---
## 8. Files Modified/Created
### Implementation
- `nextcloud_mcp_server/auth/keycloak_oauth.py` - Token exchange methods
- `nextcloud_mcp_server/auth/refresh_token_storage.py` - Token storage (ready for future)
- `nextcloud_mcp_server/app.py` - OAuth configuration updates
- `keycloak/realm-export.json` - Token exchange enabled
- `pyproject.toml` - Added aiosqlite dependency
### Documentation
- `docs/oauth-impersonation-findings.md` - This document
- `docs/ADR-002-vector-sync-authentication.md` - Original architecture decision
### Tests
- `tests/manual/test_token_exchange.py` - Keycloak RFC 8693 testing
- `tests/manual/test_nextcloud_impersonate.py` - Nextcloud impersonate API testing
---
## 9. Conclusion
**Neither Keycloak nor Nextcloud currently provide viable OAuth-based user impersonation for background operations.**
The infrastructure is ready (token storage, exchange methods), but provider limitations prevent use.
**Recommended approach**: Use dedicated service account with appropriate Nextcloud permissions for background operations until proper OAuth delegation becomes available.
The implemented code remains valuable:
- Ready for future when providers add support
- Demonstrates proper OAuth patterns
- Test infrastructure for validation
---
## Appendix: Technical Details
### Keycloak Configuration Applied
```json
{
"clientId": "nextcloud-mcp-server",
"serviceAccountsEnabled": true,
"attributes": {
"token.exchange.grant.enabled": "true"
}
}
```
### Test Results - UPDATED (2025-11-02)
```
✅ Service account token acquisition: WORKS
✅ Token exchange discovery: SUPPORTED
✅ Token exchange configuration: ENABLED
✅ Actual token exchange: WORKS (after adding client.token.exchange.standard.enabled)
✅ Nextcloud API access: WORKS with exchanged tokens
```
**Resolution**: The realm-export.json was missing the `client.token.exchange.standard.enabled` attribute. After adding this attribute to keycloak/realm-export.json:128, token exchange works correctly on fresh Keycloak imports.
### Nextcloud Impersonate Results
```
✓ App installation: SUCCESS
✓ Admin can impersonate: YES (session-based)
✗ Bearer token impersonate: NO (requires session cookies)
✗ Stateless impersonate: NOT AVAILABLE
```
---
## 10. Token Exchange Resolution (2025-11-02)
### Problem
Initial token exchange implementation was failing with:
```
"Standard token exchange is not enabled for the requested client"
```
### Root Cause
The `realm-export.json` was missing a critical attribute for Keycloak 26.2+ Standard Token Exchange:
- Had: `"token.exchange.grant.enabled": "true"`
- Missing: `"client.token.exchange.standard.enabled": "true"`
### Fix Applied
Updated `keycloak/realm-export.json` at line 128 to include both attributes:
```json
"attributes": {
"pkce.code.challenge.method": "S256",
"use.refresh.tokens": "true",
"backchannel.logout.session.required": "true",
"backchannel.logout.url": "http://app:80/index.php/apps/user_oidc/backchannel-logout/keycloak",
"oauth2.device.authorization.grant.enabled": "false",
"oidc.ciba.grant.enabled": "false",
"client_credentials.use_refresh_token": "false",
"display.on.consent.screen": "false",
"token.exchange.grant.enabled": "true",
"client.token.exchange.standard.enabled": "true" // ADDED
}
```
### Verification
After recreating Keycloak with fresh realm import:
```bash
$ docker compose down -v keycloak && docker compose up -d keycloak
$ uv run python tests/manual/test_token_exchange.py
✅ Token Exchange Test PASSED
```
### Current Status
- ✅ RFC 8693 Token Exchange fully functional
- ✅ Service account token acquisition works
- ✅ Token exchange for internal tokens works
- ✅ Exchanged tokens validate with Nextcloud APIs
- ✅ Realm import automatically applies correct configuration
- ⚠️ User impersonation still requires Keycloak Legacy V1
### Files Modified
- `keycloak/realm-export.json` - Added `client.token.exchange.standard.enabled` attribute
- `docs/oauth-impersonation-findings.md` - Updated with resolution
### Testing
Run the complete token exchange flow:
```bash
uv run python tests/manual/test_token_exchange.py
```
+9 -5
View File
@@ -170,7 +170,7 @@ You have two options for managing OAuth clients:
**How it works**:
- MCP server automatically registers an OAuth client on first startup
- Uses Nextcloud's dynamic client registration endpoint
- Saves credentials to SQLite database
- Saves credentials to `.nextcloud_oauth_client.json`
- Reuses stored credentials on subsequent restarts
- Re-registers automatically if credentials expire
@@ -253,6 +253,9 @@ NEXTCLOUD_PASSWORD=
# Optional: MCP server URL (for OAuth callbacks)
NEXTCLOUD_MCP_SERVER_URL=http://localhost:8000
# Optional: Client storage path
NEXTCLOUD_OIDC_CLIENT_STORAGE=.nextcloud_oauth_client.json
EOF
```
@@ -288,6 +291,7 @@ EOF
| `NEXTCLOUD_OIDC_CLIENT_ID` | ⚠️ Mode B only | - | OAuth client ID |
| `NEXTCLOUD_OIDC_CLIENT_SECRET` | ⚠️ Mode B only | - | OAuth client secret |
| `NEXTCLOUD_MCP_SERVER_URL` | ⚠️ Optional | `http://localhost:8000` | MCP server URL for callbacks |
| `NEXTCLOUD_OIDC_CLIENT_STORAGE` | ⚠️ Optional | `.nextcloud_oauth_client.json` | Client credentials storage path |
| `NEXTCLOUD_USERNAME` | ❌ Must be empty | - | Leave empty for OAuth |
| `NEXTCLOUD_PASSWORD` | ❌ Must be empty | - | Leave empty for OAuth |
@@ -330,7 +334,7 @@ INFO OIDC discovery successful
INFO Attempting dynamic client registration...
INFO Dynamic client registration successful
INFO OAuth client ready: <client-id>...
INFO Saved OAuth client credentials to SQLite database
INFO Saved OAuth client credentials to .nextcloud_oauth_client.json
INFO OAuth initialization complete
INFO MCP server ready at http://127.0.0.1:8000
```
@@ -423,9 +427,9 @@ uv run nextcloud-mcp-server --oauth --log-level debug
2. **Secure Credential Storage**
```bash
# Set restrictive permissions on environment file
# Set restrictive permissions
chmod 600 .nextcloud_oauth_client.json
chmod 600 .env
# Database permissions are handled automatically
```
3. **Use HTTPS for MCP Server**
@@ -470,7 +474,7 @@ services:
NEXTCLOUD_OIDC_CLIENT_SECRET: ${NEXTCLOUD_OIDC_CLIENT_SECRET}
NEXTCLOUD_MCP_SERVER_URL: http://your-server:8000
volumes:
- ./data:/app/data # For SQLite database persistence
- ./oauth_client.json:/app/.nextcloud_oauth_client.json
command: ["--oauth", "--transport", "streamable-http"]
restart: unless-stopped
```
+20 -19
View File
@@ -17,7 +17,7 @@ Start here to identify your issue:
| Only seeing Notes tools (7 instead of 90+) | Limited OAuth scopes granted | [Limited Scopes](#limited-scopes---only-seeing-notes-tools) |
| HTTP 401 for Notes API | Bearer token patch missing | [Bearer Token Auth Fails](#bearer-token-authentication-fails) |
| "OIDC discovery failed" | Network or configuration issue | [Discovery Failed](#oidc-discovery-failed) |
| "Database error" on OAuth client storage | Database permissions issue | [Database Permission Error](#database-permission-error) |
| "Permission denied" on .nextcloud_oauth_client.json | File permissions issue | [File Permission Error](#file-permission-error) |
## Configuration Issues
@@ -161,38 +161,39 @@ php occ config:app:set oidc expire_time --value "86400" # 24 hours
---
### Database Permission Error
### File Permission Error
**Error Message**:
```
Permission denied when accessing SQLite database
Database is locked
Permission denied when reading/writing .nextcloud_oauth_client.json
```
**Cause**: The server cannot access the SQLite database file.
**Cause**: The server cannot access the OAuth client storage file.
**Solution**:
```bash
# Check database directory permissions
ls -la /app/data/
# Check file permissions
ls -la .nextcloud_oauth_client.json
# Fix file permissions (owner read/write only)
chmod 600 .nextcloud_oauth_client.json
# Ensure directory is writable
chmod 755 /app/data
chmod 755 $(dirname .nextcloud_oauth_client.json)
# Check if database file exists and has correct permissions
ls -la /app/data/tokens.db
chmod 644 /app/data/tokens.db
# If running in Docker, ensure volume is mounted correctly
docker compose logs mcp-oauth | grep -i "database\|sqlite"
# If file doesn't exist, ensure directory is writable
mkdir -p $(dirname .nextcloud_oauth_client.json)
```
**For Docker deployments**:
Ensure the data directory is properly mounted as a volume:
```yaml
volumes:
- ./data:/app/data # Persistent storage for SQLite database
For custom storage paths:
```bash
# Set custom path in .env
NEXTCLOUD_OIDC_CLIENT_STORAGE=/path/to/custom/oauth_client.json
# Ensure directory exists and is writable
mkdir -p $(dirname /path/to/custom/oauth_client.json)
chmod 755 $(dirname /path/to/custom/oauth_client.json)
```
---
+26 -84
View File
@@ -16,79 +16,35 @@ While the core OAuth flow works, there are **pending upstream improvements** tha
**Status**: 🟡 **Patch Required** (Pending Upstream)
**Affected Component**: **Nextcloud core server** (`CORSMiddleware`)
**Affected Component**: `user_oidc` app
**Issue**: Bearer token authentication fails for app-specific APIs (Notes, Calendar, etc.) with `401 Unauthorized` errors, even though OCS APIs work correctly.
**Root Cause**: The `CORSMiddleware` in Nextcloud core server logs out sessions when CSRF tokens are missing. Bearer token authentication creates a session (via `user_oidc` app), but doesn't include CSRF tokens (stateless authentication). The middleware detects the logged-in session without CSRF token and calls `session->logout()`, invalidating the request.
**Root Cause**: The `CORSMiddleware` in Nextcloud logs out sessions created by Bearer token authentication when CSRF tokens are missing, which breaks API requests.
**Solution**: Allow Bearer token requests to bypass CORS/CSRF checks in `CORSMiddleware`, since Bearer tokens are stateless and don't require CSRF protection.
**Solution**: Set the `app_api` session flag during Bearer token authentication to bypass CSRF checks.
**Upstream PR**: [nextcloud/server#55878](https://github.com/nextcloud/server/pull/55878)
**Upstream PR**: [nextcloud/user_oidc#1221](https://github.com/nextcloud/user_oidc/issues/1221)
**Workaround**: Manually apply the patch to `lib/private/AppFramework/Middleware/Security/CORSMiddleware.php` in Nextcloud core server
**Workaround**: Manually apply the patch to `lib/User/Backend.php` in the `user_oidc` app
**Impact**:
-**Works**: OCS APIs (`/ocs/v2.php/cloud/capabilities`)
-**Requires Patch**: App APIs (`/apps/notes/api/`, `/apps/calendar/`, etc.)
**Files Modified**: `lib/private/AppFramework/Middleware/Security/CORSMiddleware.php` in **Nextcloud core server**
**Files Modified**: `lib/User/Backend.php` in `user_oidc` app
**Patch Summary**:
```php
// Allow Bearer token authentication for CORS requests
// Bearer tokens are stateless and don't require CSRF protection
$authorizationHeader = $this->request->getHeader('Authorization');
if (!empty($authorizationHeader) && str_starts_with($authorizationHeader, 'Bearer ')) {
return;
}
// Add before successful Bearer token authentication returns
$this->session->set('app_api', true);
```
This is added before the CSRF check at line ~73 in `CORSMiddleware.php`.
This is added at lines ~243, ~310, ~315, and ~337 in `Backend.php`.
---
### 2. JWT Token Support, Introspection, and Scope Validation
**Status**: ✅ **Complete** (Merged Upstream)
**Affected Component**: `oidc` app
**Issue**: The OIDC app needed support for JWT tokens, token introspection, and enhanced scope validation for fine-grained authorization.
**Resolution**: Complete JWT and scope validation support has been implemented and merged:
**Upstream PR**: [H2CK/oidc#585](https://github.com/H2CK/oidc/pull/585) - ✅ **Merged**
- **Changes**:
- JWT token generation and validation
- Token introspection endpoint (RFC 7662)
- Enhanced scope validation and parsing
- Custom scope support for Nextcloud apps
- **Status**: Merged and available in v1.10.0+ of the `oidc` app
---
### 3. User Consent Management
**Status**: ✅ **Complete** (Merged Upstream)
**Affected Component**: `oidc` app
**Issue**: The OIDC app needed proper user consent management for OAuth authorization flows.
**Resolution**: Complete user consent management has been implemented and merged:
**Upstream PR**: [H2CK/oidc#586](https://github.com/H2CK/oidc/pull/586) - ✅ **Merged**
- **Changes**:
- User consent UI for OAuth authorization
- Consent expiration and cleanup
- Admin control for user consent settings
- Consent tracking and management
- **Status**: Merged and available in v1.11.0+ of the `oidc` app
---
### 4. PKCE Support (RFC 7636)
### 2. PKCE Support (RFC 7636)
**Status**: ✅ **Complete** (Merged Upstream)
@@ -141,34 +97,24 @@ This is added before the CSRF check at line ~73 in `CORSMiddleware.php`.
| PR/Issue | Component | Status | Priority | Notes |
|----------|-----------|--------|----------|-------|
| [server#55878](https://github.com/nextcloud/server/pull/55878) | Nextcloud core server | 🟡 Open | High | CORSMiddleware patch for Bearer tokens |
| [H2CK/oidc#586](https://github.com/H2CK/oidc/pull/586) | `oidc` | ✅ Merged | Medium | ✅ User consent complete (v1.11.0+) |
| [H2CK/oidc#585](https://github.com/H2CK/oidc/pull/585) | `oidc` | ✅ Merged | Medium | ✅ JWT tokens, introspection, scope validation (v1.10.0+) |
| [H2CK/oidc#584](https://github.com/H2CK/oidc/pull/584) | `oidc` | ✅ Merged | ~~High~~ | ✅ PKCE support (RFC 7636) (v1.10.0+) |
| [user_oidc#1221](https://github.com/nextcloud/user_oidc/issues/1221) | `user_oidc` | 🟡 Open | High | Required for app-specific APIs |
| [H2CK/oidc#584](https://github.com/H2CK/oidc/pull/584) | `oidc` | ✅ Merged | ~~Medium~~ | ✅ PKCE advertisement complete (v1.10.0+) |
## What Works Without Patches
The following functionality works **out of the box** without any patches:
**OAuth Flow** (requires `oidc` app v1.10.0+):
- OIDC discovery with full PKCE support (RFC 7636)
**OAuth Flow**:
- OIDC discovery with full PKCE support (requires `oidc` app v1.10.0+)
- Dynamic client registration
- Authorization code flow with PKCE (S256 and plain methods)
- Token exchange with code_verifier verification
- User consent management
- Userinfo endpoint
**Token Features** (requires `oidc` app v1.10.0+):
- JWT token generation and validation
- Token introspection endpoint (RFC 7662)
- Enhanced scope validation and parsing
- Custom scope support for Nextcloud apps
**MCP Server as Resource Server**:
- Token validation via userinfo
- Per-user client instances
- Token caching
- Scope-based authorization
**Nextcloud OCS APIs**:
- Capabilities endpoint
@@ -178,7 +124,7 @@ The following functionality works **out of the box** without any patches:
The following functionality requires upstream patches:
🟡 **App-Specific APIs** (Requires Nextcloud core server CORSMiddleware patch):
🟡 **App-Specific APIs** (Requires user_oidc#1221):
- Notes API (`/apps/notes/api/`)
- Calendar API (CalDAV)
- Contacts API (CardDAV)
@@ -252,23 +198,19 @@ uv run pytest tests/client/test_oauth_playwright.py --browser firefox -v
## Monitoring Upstream Progress
To track progress on remaining issues:
To track progress on these issues:
1. **Watch the upstream repository**:
- [nextcloud/server](https://github.com/nextcloud/server)
1. **Watch the upstream repositories**:
- [nextcloud/user_oidc](https://github.com/nextcloud/user_oidc)
- [nextcloud/oidc](https://github.com/nextcloud/oidc)
2. **Subscribe to the CORSMiddleware PR**:
- [server#55878](https://github.com/nextcloud/server/pull/55878) - CORSMiddleware Bearer token support
2. **Subscribe to specific issues**:
- [user_oidc#1221](https://github.com/nextcloud/user_oidc/issues/1221) - Bearer token support
3. **Check Nextcloud server release notes** for mentions of:
3. **Check Nextcloud release notes** for mentions of:
- Bearer token authentication improvements
- CORS middleware enhancements
- OAuth/OIDC API compatibility
4. **Completed upstream work** (no monitoring needed):
- ✅ [H2CK/oidc#584](https://github.com/H2CK/oidc/pull/584) - PKCE support (v1.10.0+)
- ✅ [H2CK/oidc#585](https://github.com/H2CK/oidc/pull/585) - JWT, introspection, scopes (v1.10.0+)
- ✅ [H2CK/oidc#586](https://github.com/H2CK/oidc/pull/586) - User consent (v1.11.0+)
- OIDC/OAuth enhancements
- AppAPI compatibility
## Contributing
@@ -295,6 +237,6 @@ Want to help get these patches merged?
---
**Last Updated**: 2025-11-02
**Last Updated**: 2025-10-20
**Next Review**: When Nextcloud server CORSMiddleware PR has activity
**Next Review**: When issue #1221 (Bearer token support) has activity
-258
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@@ -1,258 +0,0 @@
# Observability and Monitoring
The Nextcloud MCP Server includes comprehensive observability features for production deployments:
- **Prometheus metrics** for monitoring performance and health
- **OpenTelemetry distributed tracing** for debugging request flows
- **Structured JSON logging** with trace correlation
- **Kubernetes integration** via ServiceMonitor and PrometheusRule
## Quick Start
### Local Development with Prometheus
```bash
# Enable metrics (enabled by default)
export METRICS_ENABLED=true
export METRICS_PORT=9090
# Enable tracing (optional - tracing is enabled when OTEL_EXPORTER_OTLP_ENDPOINT is set)
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
# Start the server
docker-compose up -d mcp
```
Access metrics at: `http://localhost:9090/metrics`
### Kubernetes Deployment
Metrics are automatically scraped if you have Prometheus Operator installed:
```bash
helm install nextcloud-mcp charts/nextcloud-mcp-server \
--set observability.metrics.enabled=true \
--set observability.tracing.enabled=true \
--set observability.tracing.endpoint=http://opentelemetry-collector:4317 \
--set serviceMonitor.enabled=true
```
## Configuration
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `METRICS_ENABLED` | `true` | Enable Prometheus metrics |
| `METRICS_PORT` | `9090` | Port for metrics endpoint |
| `OTEL_EXPORTER_OTLP_ENDPOINT` | - | OTLP gRPC endpoint (e.g., `http://otel-collector:4317`). Tracing is enabled when this is set. |
| `OTEL_SERVICE_NAME` | `nextcloud-mcp-server` | Service name in traces |
| `OTEL_TRACES_SAMPLER` | `always_on` | Trace sampling strategy |
| `OTEL_TRACES_SAMPLER_ARG` | `1.0` | Sampling rate (0.0-1.0) |
| `LOG_FORMAT` | `json` | Log format (`json` or `text`) |
| `LOG_LEVEL` | `INFO` | Minimum log level |
| `LOG_INCLUDE_TRACE_CONTEXT` | `true` | Include trace IDs in logs |
### Helm Chart Configuration
```yaml
observability:
metrics:
enabled: true
port: 9090
path: /metrics
tracing:
enabled: true
endpoint: "http://opentelemetry-collector:4317"
samplingRate: 1.0
logging:
format: json
level: INFO
includeTraceContext: true
serviceMonitor:
enabled: true
interval: 30s
scrapeTimeout: 10s
```
## Metrics
### HTTP Server Metrics (RED)
- `mcp_http_requests_total` - Total HTTP requests
- `mcp_http_request_duration_seconds` - Request latency histogram
- `mcp_http_requests_in_progress` - In-flight requests gauge
### MCP Tool Metrics
- `mcp_tool_calls_total` - Tool invocation count by status
- `mcp_tool_duration_seconds` - Tool execution latency
- `mcp_tool_errors_total` - Tool errors by type
### Nextcloud API Metrics
- `mcp_nextcloud_api_requests_total` - API calls by app and status
- `mcp_nextcloud_api_duration_seconds` - API latency by app
- `mcp_nextcloud_api_retries_total` - Retry count (429, timeout, etc.)
### OAuth Flow Metrics
- `mcp_oauth_token_validations_total` - Token validation count
- `mcp_oauth_token_exchange_total` - Token exchange operations
- `mcp_oauth_token_cache_hits_total` - Cache hit/miss rate
- `mcp_oauth_refresh_token_operations_total` - Refresh token storage ops
### Vector Sync Metrics (when enabled)
- `mcp_vector_sync_documents_scanned_total` - Documents discovered
- `mcp_vector_sync_documents_processed_total` - Processing results
- `mcp_vector_sync_processing_duration_seconds` - Processing latency
- `mcp_vector_sync_queue_size` - Current queue depth
- `mcp_qdrant_operations_total` - Qdrant DB operations
### Database Metrics
- `mcp_db_operations_total` - DB operations (SQLite, Qdrant)
- `mcp_db_operation_duration_seconds` - DB latency
### Dependency Health
- `mcp_dependency_health` - External dependency status (1=up, 0=down)
- `mcp_dependency_check_duration_seconds` - Health check latency
## Distributed Tracing
### Span Hierarchy
```
HTTP POST /messages
├── mcp.tool.nc_notes_create_note
│ └── nextcloud.api.notes.POST
│ └── httpx request (auto-instrumented)
└── oauth.token.validate (if OAuth mode)
└── httpx request to IdP
```
### Span Attributes
- **MCP tools**: `mcp.tool.name`, `mcp.tool.args` (sanitized)
- **Nextcloud API**: `nextcloud.app`, `http.method`, `http.status_code`
- **OAuth**: `oauth.operation`, `oauth.method`
- **Vector sync**: `vector_sync.operation`, `vector_sync.document_count`
### Trace Context in Logs
When tracing is enabled, all logs include `trace_id` and `span_id`:
```json
{
"timestamp": "2025-01-09T12:34:56.789Z",
"level": "INFO",
"logger": "nextcloud_mcp_server.server.notes",
"message": "Note created successfully",
"trace_id": "a1b2c3d4e5f6...",
"span_id": "123456789abc...",
"note_id": 42
}
```
## Dashboards
### Prometheus Queries
**Request Rate (req/s)**:
```promql
sum(rate(mcp_http_requests_total[5m])) by (method, endpoint)
```
**Error Rate (%)**:
```promql
sum(rate(mcp_http_requests_total{status_code=~"5.."}[5m]))
/ sum(rate(mcp_http_requests_total[5m])) * 100
```
**P95 Latency**:
```promql
histogram_quantile(0.95,
sum(rate(mcp_http_request_duration_seconds_bucket[5m])) by (le, endpoint)
)
```
**Top Tools by Volume**:
```promql
topk(10, sum(rate(mcp_tool_calls_total[5m])) by (tool_name))
```
**Nextcloud API Health**:
```promql
sum(rate(mcp_nextcloud_api_requests_total{status_code!~"2.."}[5m])) by (app)
```
## Alerts
### Recommended Alert Rules
**Critical**:
- Server down for >5min
- Error rate >5% for >5min
- P95 latency >1s for >5min
- Dependency down for >2min
**Warning**:
- Token validation errors >1% for >10min
- Vector sync queue >100 for >15min
- Qdrant slow (p95 >500ms) for >10min
See `charts/nextcloud-mcp-server/templates/prometheusrule.yaml` for complete definitions.
## Troubleshooting
### Metrics Not Appearing
1. Check metrics are enabled: `curl http://localhost:9090/metrics`
2. Verify ServiceMonitor labels match Prometheus selector
3. Check Prometheus target status: `http://prometheus:9090/targets`
### Traces Not Appearing
1. Verify OTLP endpoint is reachable: `curl http://otel-collector:4317`
2. Check collector logs for errors
3. Verify sampling rate is not 0.0
4. Check trace backend (Jaeger/Tempo) connectivity
### High Cardinality Metrics
If you see cardinality warnings:
- Middleware normalizes endpoints (e.g., `/user/123``/user/*`)
- OAuth tokens are never included in metric labels
- User IDs are not tracked (use tracing for per-user debugging)
## Performance Impact
- **Metrics**: <1% overhead (counters/histograms are very fast)
- **Tracing**: ~2-5% overhead at 100% sampling
- **JSON logging**: <1% overhead vs text logging
**Recommendation**: Always enable metrics. Enable tracing in staging/production with 10-50% sampling.
## Architecture
The observability stack integrates at multiple layers:
1. **HTTP Layer**: `ObservabilityMiddleware` tracks all HTTP requests
2. **MCP Layer**: Tools use `@trace_mcp_tool` for span creation
3. **Client Layer**: `BaseNextcloudClient` tracks all API calls
4. **OAuth Layer**: Token operations are traced and metered
5. **Background Tasks**: Vector sync operations emit metrics/traces
All components use shared Prometheus `Registry` and OpenTelemetry `TracerProvider`.
## References
- [Prometheus Best Practices](https://prometheus.io/docs/practices/)
- [OpenTelemetry Python SDK](https://opentelemetry.io/docs/languages/python/)
- [Prometheus Operator](https://prometheus-operator.dev/)
- [Grafana Dashboards](https://grafana.com/docs/grafana/latest/dashboards/)
-921
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@@ -1,921 +0,0 @@
# Semantic Search Architecture
This document explains the architecture of the semantic search feature in the Nextcloud MCP Server, including background synchronization, vector search, and optional AI-generated answers via MCP sampling.
> [!IMPORTANT]
> **Status: Experimental**
> - Disabled by default (`VECTOR_SYNC_ENABLED=false`)
> - Currently supports **Notes app only** (multi-app architecture ready, additional apps planned)
> - Requires additional infrastructure (Qdrant vector database + Ollama embedding service)
> - RAG answer generation requires MCP client sampling support
## Overview
### What is Semantic Search?
**Semantic search** finds information based on **meaning** rather than exact keyword matches. It uses vector embeddings to understand that "car" and "automobile" are similar, or that "bread recipe" matches "how to bake bread."
**Traditional keyword search:**
```
Query: "machine learning"
Matches: Only notes containing "machine learning" exactly
Misses: Notes with "neural networks", "AI models", "deep learning"
```
**Semantic search:**
```
Query: "machine learning"
Matches: Notes about machine learning, neural networks, AI, deep learning, etc.
Understanding: Semantic similarity via vector embeddings
```
### Why It Matters
Semantic search enables:
- **Natural language queries** - Ask questions in plain language
- **Conceptual discovery** - Find related content even with different terminology
- **Cross-reference insights** - Connect ideas across your knowledge base
- **AI-powered answers** - Generate summaries with citations (optional, requires MCP sampling)
### Current Support
- **Supported Apps**: Notes (fully implemented)
- **Planned Apps**: Calendar events, Calendar tasks, Deck cards, Files (with text extraction), Contacts
- **Architecture**: Multi-app plugin system ready, awaiting implementation
## System Components
```mermaid
graph TB
subgraph "MCP Client"
Client[Claude Desktop, IDEs, etc.]
end
subgraph "Nextcloud MCP Server"
MCP[MCP Server]
Scanner[Background Scanner<br/>Hourly Change Detection]
Queue[Document Queue]
Processor[Embedding Processors<br/>Concurrent Workers]
end
subgraph "Infrastructure"
Qdrant[(Qdrant<br/>Vector Database)]
Ollama[Ollama<br/>Embedding Service]
NC[Nextcloud<br/>Notes API, CalDAV, etc.]
end
Client <-->|MCP Protocol| MCP
Scanner -->|Fetch Changes| NC
Scanner -->|Enqueue Documents| Queue
Queue -->|Process Batch| Processor
Processor -->|Generate Embeddings| Ollama
Processor -->|Store Vectors| Qdrant
MCP -->|Search Queries| Qdrant
MCP -->|Verify Access| NC
```
**Component Roles:**
- **MCP Server**: Exposes semantic search tools (`nc_semantic_search`, `nc_semantic_search_answer`, `nc_get_vector_sync_status`)
- **Background Scanner**: Discovers changed documents every hour using ETag-based change detection
- **Document Queue**: Holds pending documents for embedding generation
- **Embedding Processors**: Generate vector embeddings via Ollama (concurrent workers)
- **Qdrant Vector Database**: Stores document vectors with metadata and user_id filtering
- **Ollama Embedding Service**: Converts text to 768-dimensional vectors (default: `nomic-embed-text` model)
- **Nextcloud APIs**: Source of truth for documents and access control verification
## How It Works: Background Synchronization
Background synchronization runs automatically when `VECTOR_SYNC_ENABLED=true`, discovering changes and indexing documents without user intervention.
```mermaid
sequenceDiagram
participant Timer
participant Scanner
participant NC as Nextcloud API
participant Queue
participant Processor
participant Ollama
participant Qdrant
Timer->>Scanner: Trigger (hourly)
Scanner->>NC: Fetch all notes<br/>(Notes API)
NC-->>Scanner: Notes with ETags
Scanner->>Qdrant: Check indexed documents
Qdrant-->>Scanner: Existing ETags
Scanner->>Scanner: Identify changes<br/>(new/modified/deleted)
Scanner->>Queue: Enqueue changed docs
loop Continuous Processing
Processor->>Queue: Fetch batch
Queue-->>Processor: Documents
Processor->>Ollama: Generate embeddings
Ollama-->>Processor: 768-dim vectors
Processor->>Qdrant: Upsert vectors<br/>(with user_id, doc_type)
end
```
### Scanner Behavior
**Hourly Trigger:**
- Runs every hour (configurable)
- Fetches all notes from Nextcloud Notes API
- Compares ETags with Qdrant's indexed state
- Enqueues new/modified documents
**Change Detection:**
- **New documents**: No entry in Qdrant → enqueue for indexing
- **Modified documents**: ETag mismatch → enqueue for re-indexing
- **Deleted documents**: In Qdrant but not in Nextcloud → delete from Qdrant
**Multi-App Plugin Architecture:**
```python
# Each app implements DocumentScanner interface
class NotesScanner(DocumentScanner):
async def scan(self) -> list[Document]:
# Fetch notes, detect changes, return documents
```
Currently only `NotesScanner` is implemented. Future: `CalendarScanner`, `DeckScanner`, `FilesScanner`, etc.
### Queue Processing
**Document Queue:**
- In-memory FIFO queue (not persistent across restarts)
- Holds documents pending embedding generation
- Batch processing for efficiency
**Processor Pool:**
- Concurrent workers using `anyio.TaskGroup`
- Process documents in parallel (default: 4 workers)
- Each worker: fetch document → generate embedding → store in Qdrant
**Backpressure Handling:**
- Queue size limits prevent memory exhaustion
- Slow consumers (Ollama) naturally pace the system
### Vector Storage
**Qdrant Collection Schema:**
```
{
"id": "note_123",
"vector": [768 dimensions],
"payload": {
"user_id": "alice",
"doc_type": "note",
"doc_id": "123",
"title": "Machine Learning Notes",
"content": "Neural networks are...",
"etag": "abc123",
"last_modified": "2025-01-15T10:30:00Z"
}
}
```
**Key Fields:**
- `user_id`: Multi-tenancy filtering (each user's vectors isolated)
- `doc_type`: App identifier ("note", "event", "card", etc.)
- `etag`: Change detection for incremental updates
- `chunk_index`: Position of this chunk within the document (0-indexed)
- `total_chunks`: Total number of chunks for this document
- `excerpt`: First 200 characters of chunk (for display)
### Document Chunking Strategy
Documents are chunked before embedding to handle content larger than the embedding model's context window and to improve search precision.
**Configuration:**
```dotenv
DOCUMENT_CHUNK_SIZE=512 # Words per chunk (default)
DOCUMENT_CHUNK_OVERLAP=50 # Overlapping words between chunks (default)
```
**Chunking Process:**
1. **Text combination**: Document title + content (e.g., `"Note Title\n\nNote content..."`)
2. **Word-based splitting**: Simple whitespace tokenization
3. **Sliding window**: Create overlapping chunks
4. **Individual embedding**: Each chunk gets its own vector
5. **Separate storage**: Each chunk stored as distinct point in Qdrant
**Example:**
```
Document (1000 words):
→ Chunk 0: words 0-511
→ Chunk 1: words 462-973 (overlaps by 50 words)
→ Chunk 2: words 924-999 (last chunk, partial)
Each chunk stored as separate vector with metadata:
- chunk_index: 0, 1, 2
- total_chunks: 3
- excerpt: First 200 chars of each chunk
```
**Search Behavior:**
- **Vector search** operates on chunks (not whole documents)
- **Deduplication** collapses multiple matching chunks from same document
- **Best match** returns highest-scoring chunk's excerpt
- **Access verification** still performed at document level
**Tuning Recommendations:**
- **Small chunks (256-384 words)**: More precise, less context, more storage
- **Large chunks (768-1024 words)**: More context, less precise, less storage
- **Overlap (10-20% of chunk size)**: Preserves context across boundaries
- **Match to embedding model**: Consider model's context window when sizing
**Important**: Changing chunk size requires re-embedding all documents. Use the collection naming strategy to manage different chunking configurations.
### Collection Naming and Model Switching
**Auto-generated collection names:**
- **Format:** `{deployment-id}-{model-name}`
- **Deployment ID:** `OTEL_SERVICE_NAME` (if configured) or `hostname` (fallback)
- **Model name:** `OLLAMA_EMBEDDING_MODEL`
- **Example:** `"my-mcp-server-nomic-embed-text"`, `"mcp-container-all-minilm"`
**Why model-based naming:**
- Ensures each embedding model gets its own collection
- Prevents dimension mismatches when switching models
- Enables safe model experimentation (new model = new collection)
- Supports multi-server deployments (different deployment IDs)
**Switching embedding models:**
Collections are **mutually exclusive** - vectors from one embedding model cannot be used with another. When you change the embedding model:
1. **New collection is created** with the new model's dimensions
2. **Full re-embedding occurs** - scanner processes all documents again
3. **Old collection remains** - can be deleted manually if no longer needed
4. **Dimension validation** - server fails fast if collection dimension doesn't match model
**Example workflow:**
```bash
# Start with nomic-embed-text (768 dimensions)
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# Collection: "my-server-nomic-embed-text"
# → Scanner indexes 1000 notes → 1000 vectors in collection
# Switch to all-minilm (384 dimensions)
OLLAMA_EMBEDDING_MODEL=all-minilm
# Collection: "my-server-all-minilm"
# → Scanner detects 0 indexed documents → re-embeds 1000 notes
# → Old collection "my-server-nomic-embed-text" still exists in Qdrant
```
**Re-embedding performance:**
- CPU-only: 1-5 notes/second
- With GPU: 50-200 notes/second
- 1000 notes: 3-16 minutes (CPU) or 5-20 seconds (GPU)
**Multi-server deployments:**
Multiple MCP servers can share one Qdrant instance safely:
```bash
# Server 1 (Production)
OTEL_SERVICE_NAME=mcp-prod
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-prod-nomic-embed-text"
# Server 2 (Staging with different model)
OTEL_SERVICE_NAME=mcp-staging
OLLAMA_EMBEDDING_MODEL=all-minilm
# → Collection: "mcp-staging-all-minilm"
```
Each deployment gets its own collection - no naming collisions or dimension conflicts.
## How It Works: Semantic Search
Semantic search converts user queries into vectors and finds similar documents using cosine similarity.
```mermaid
sequenceDiagram
participant User
participant MCP as MCP Server
participant Ollama
participant Qdrant
participant NC as Nextcloud API
User->>MCP: nc_semantic_search("machine learning")
MCP->>MCP: Check OAuth scope<br/>(semantic:read)
MCP->>Ollama: Generate query embedding
Ollama-->>MCP: Query vector (768-dim)
MCP->>Qdrant: Search similar vectors<br/>(filter: user_id=alice)
Qdrant-->>MCP: Top K results<br/>(with similarity scores)
loop For each result
MCP->>NC: Verify access<br/>(fetch note by ID)
alt Access granted
NC-->>MCP: Note metadata
else Access denied (404/401)
MCP->>MCP: Filter out result
end
end
MCP-->>User: Search results<br/>(with scores, excerpts)
```
### Dual-Phase Authorization
**Phase 1: OAuth Scope Check**
- Verify user has `semantic:read` scope
- Rejects unauthorized users immediately
**Phase 2: Per-Document Verification**
- For each search result, fetch document via app API (Notes, Calendar, etc.)
- If fetch succeeds (200 OK), user has access
- If fetch fails (404 Not Found, 401 Unauthorized), filter out result
- **Security**: Prevents information leakage from vector search alone
**Rationale:**
- Vector database doesn't know about sharing, permissions changes, or deleted documents
- App APIs are source of truth for access control
- Verification ensures users only see documents they can access
### Search Flow
1. **Query Embedding**: Convert user query to 768-dimensional vector via Ollama
2. **Vector Search**: Find top K similar vectors in Qdrant (cosine similarity)
3. **User Filtering**: Qdrant pre-filters by `user_id` (multi-tenancy)
4. **Access Verification**: Fetch each document via app API to verify current access
5. **Result Ranking**: Return results sorted by similarity score
6. **Response**: Include document excerpts, metadata, and similarity scores
### Performance
- **Query latency**: 50-200ms typical (embedding + vector search + verification)
- **Accuracy**: Depends on embedding model quality (`nomic-embed-text` recommended)
- **Scalability**: Qdrant handles millions of vectors efficiently
## How It Works: RAG with MCP Sampling (Optional)
The `nc_semantic_search_answer` tool generates AI-powered answers with citations using **MCP sampling** - requesting the MCP client's LLM to generate text.
```mermaid
sequenceDiagram
participant User
participant MCP as MCP Server
participant Client as MCP Client<br/>(Claude Desktop)
participant LLM as Client's LLM<br/>(Claude, GPT, etc.)
User->>MCP: nc_semantic_search_answer("What are my Q1 goals?")
MCP->>MCP: Semantic search<br/>(find relevant notes)
MCP->>MCP: Construct prompt<br/>(query + documents + instructions)
MCP->>Client: Sampling request<br/>(MCP Protocol)
Client->>User: Prompt for approval<br/>(optional, client-controlled)
User-->>Client: Approve
Client->>LLM: Generate answer<br/>(with context)
LLM-->>Client: Answer with citations
Client-->>MCP: Sampling response
MCP-->>User: Generated answer<br/>(with source documents)
```
### MCP Sampling Architecture
**Why MCP Sampling?**
- **No server-side LLM**: MCP server has no API keys, doesn't call LLMs directly
- **Client controls everything**: Which model, who pays, user approval prompts
- **Privacy**: Documents stay with the client's LLM provider, not a third-party
- **Flexibility**: Works with any MCP client that supports sampling (Claude Desktop, future clients)
**Prompt Construction:**
```
User Query: {query}
Relevant Documents:
1. Document: {title} (Note)
Content: {excerpt}
2. Document: {title} (Note)
Content: {excerpt}
Instructions:
- Provide a comprehensive answer to the user's query
- Use the documents above as context
- Include citations: "According to Document 1 (title)..."
- If documents don't contain enough information, say so
```
**Graceful Fallback:**
```python
try:
result = await ctx.session.create_message(...)
return answer_with_citations
except Exception as e:
# Fallback: Return documents without generated answer
return SearchResponse(
generated_answer=f"[Sampling unavailable: {e}]",
sources=search_results
)
```
**Client Support:**
- **Requires**: MCP client with sampling capability
- **Known support**: Claude Desktop (as of Claude 3.5+)
- **Graceful degradation**: Returns raw documents if sampling unavailable
## Authentication & Security
### OAuth Scopes
**`semantic:read`** - Search permission
- Allows using `nc_semantic_search` and `nc_semantic_search_answer` tools
- Does NOT grant access to documents (verified via app APIs)
- Required for any semantic search operation
**`semantic:write`** - Sync control permission
- Allows enabling/disabling background sync (`provision_vector_sync`, `deprovision_vector_sync`)
- Controls whether user's documents are indexed
- Currently not implemented in OAuth mode (BasicAuth only)
### Dual-Phase Authorization Pattern
**Phase 1: Scope Check** (semantic:read)
- Verifies user authorized to search
- Prevents unauthorized vector database access
**Phase 2: Document Verification** (app-specific APIs)
- For each search result, fetch via Notes API, CalDAV, etc.
- If user can fetch → include in results
- If user cannot fetch (404/401) → filter out
- **Security**: Vector search cannot leak documents user shouldn't see
**Example Scenario:**
1. Alice creates note "Secret Project X"
2. Background sync indexes note with `user_id=alice`
3. Bob searches for "project"
4. Vector search finds "Secret Project X" (vector similarity)
5. Qdrant filters by `user_id=bob` → no match (Alice's note excluded)
6. Even if Bob somehow got the doc_id, Phase 2 verification would fail (404 Not Found)
### Offline Access for Background Sync
**Why needed:**
- Background scanner runs hourly without user interaction
- Requires valid access tokens to fetch documents from Nextcloud APIs
- User's session token expires after hours/days
**OAuth Mode (ADR-004 Flow 2):**
- User explicitly provisions offline access via `provision_nextcloud_access` tool
- Server requests `offline_access` scope → receives refresh token
- Refresh token stored securely (database, encrypted)
- Background sync uses refresh tokens to obtain access tokens
**BasicAuth Mode:**
- Username/password stored in environment variables
- Always available for background operations
- Simpler but less secure (credentials never expire)
## Deployment Modes
### Authentication Modes
| Mode | Security | Offline Access | Background Sync | Best For |
|------|----------|----------------|-----------------|----------|
| **BasicAuth** | Lower (credentials in env) | Always available | ✅ Works immediately | Single-user, development, testing |
| **OAuth** | Higher (tokens, scopes) | User must provision | ⚠️ Not yet implemented | Multi-user, production |
**BasicAuth:**
- Set `NEXTCLOUD_USERNAME` and `NEXTCLOUD_PASSWORD`
- Background sync works immediately when `VECTOR_SYNC_ENABLED=true`
- Credentials stored in `.env` file (secure server access required)
**OAuth:**
- Client authenticates with `semantic:read` scope
- User must explicitly provision offline access (future: `provision_vector_sync` tool)
- Background sync only works for users who provisioned access
- More secure: tokens expire, user controls access
### Qdrant Deployment Modes
| Mode | Configuration | Persistence | Scalability | Best For |
|------|---------------|-------------|-------------|----------|
| **In-Memory** (default) | `QDRANT_LOCATION=:memory:` | ❌ Lost on restart | Single instance | Testing, development |
| **Persistent Local** | `QDRANT_LOCATION=/data/qdrant` | ✅ Survives restarts | Single instance | Small deployments |
| **Network** | `QDRANT_URL=http://qdrant:6333` | ✅ Dedicated service | ✅ Horizontal scaling | Production |
**In-Memory Mode:**
```bash
VECTOR_SYNC_ENABLED=true
# QDRANT_LOCATION not set → defaults to :memory:
```
- Fastest startup
- No disk I/O
- **Warning**: All vectors lost when server restarts (must re-index)
**Persistent Local Mode:**
```bash
VECTOR_SYNC_ENABLED=true
QDRANT_LOCATION=/var/lib/qdrant
```
- Vectors survive restarts
- Single server only (no distributed setup)
- Disk I/O for durability
**Network Mode (Recommended for Production):**
```bash
VECTOR_SYNC_ENABLED=true
QDRANT_URL=http://qdrant:6333
QDRANT_API_KEY=secret # optional
```
- Dedicated Qdrant service (Docker, Kubernetes)
- Horizontal scaling (multiple MCP servers → one Qdrant)
- High availability options
### Embedding Service Options
| Service | Configuration | Cost | Performance | Best For |
|---------|---------------|------|-------------|----------|
| **Ollama** (recommended) | `OLLAMA_BASE_URL=http://ollama:11434` | Free (self-hosted) | Fast (local GPU) | Production, development |
| **OpenAI** (future) | `OPENAI_API_KEY=sk-...` | Paid (API) | Fast (cloud) | Cloud deployments |
| **Fallback** | No config | Free | Slow (random) | Testing only (not production) |
**Ollama Setup (Recommended):**
```bash
# docker-compose.yml
services:
ollama:
image: ollama/ollama
volumes:
- ollama-data:/root/.ollama
ports:
- "11434:11434"
# Pull embedding model
docker compose exec ollama ollama pull nomic-embed-text
```
**Environment Configuration:**
```bash
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_EMBEDDING_MODEL=nomic-embed-text # 768-dimensional vectors
```
**Model Options:**
- `nomic-embed-text` (default): 768-dim, optimized for semantic search
- `all-minilm`: Smaller, faster, slightly less accurate
- `mxbai-embed-large`: Larger, more accurate, slower
## Configuration Overview
### Key Environment Variables
**Enable Semantic Search:**
```bash
VECTOR_SYNC_ENABLED=true # Default: false (opt-in)
```
**Qdrant Vector Database:**
```bash
# In-memory mode (default if VECTOR_SYNC_ENABLED=true)
# QDRANT_LOCATION not set → uses :memory:
# Persistent local mode
QDRANT_LOCATION=/var/lib/qdrant
# Network mode (production)
QDRANT_URL=http://qdrant:6333
QDRANT_API_KEY=secret # optional
```
**Ollama Embedding Service:**
```bash
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_EMBEDDING_MODEL=nomic-embed-text # Default
```
**Scanner Configuration:**
```bash
VECTOR_SYNC_INTERVAL=3600 # Scan interval in seconds (default: 1 hour)
```
### Resource Requirements
**Qdrant:**
- **Memory**: ~100-200 MB base + ~1 KB per vector (1M vectors ≈ 1 GB)
- **Disk**: Persistent mode only, ~200 bytes per vector
- **CPU**: Low (indexing) to moderate (search)
**Ollama:**
- **Memory**: 2-4 GB for `nomic-embed-text` model
- **CPU**: High during embedding generation, idle otherwise
- **GPU**: Optional but recommended (10-100x faster)
**MCP Server:**
- **Memory**: +50-100 MB for background sync workers
- **CPU**: Moderate during scanning/processing, low otherwise
### Trade-offs
| Consideration | In-Memory Qdrant | Persistent Qdrant | Network Qdrant |
|---------------|------------------|-------------------|----------------|
| Setup complexity | ✅ Minimal | ✅ Easy | ⚠️ Requires separate service |
| Durability | ❌ Lost on restart | ✅ Survives restarts | ✅ Survives restarts |
| Scalability | ❌ Single instance | ❌ Single instance | ✅ Horizontal scaling |
| Performance | ✅ Fastest | ✅ Fast | ⚠️ Network latency |
## Operational Behavior
### What Happens When VECTOR_SYNC_ENABLED=true
**Immediate (Server Startup):**
1. MCP server connects to Qdrant (creates collection if needed)
2. MCP server connects to Ollama (verifies embedding model available)
3. Background scanner starts (schedules hourly runs)
4. Document queue and processors initialize
**First Scan (Within 1 hour):**
1. Scanner fetches all notes from Nextcloud
2. Compares with Qdrant (likely empty on first run)
3. Enqueues all notes for indexing
4. Processors generate embeddings (may take minutes for large note collections)
5. Vectors stored in Qdrant with user_id filtering
**Hourly Thereafter:**
1. Scanner fetches all notes
2. Identifies new/modified/deleted notes (ETag comparison)
3. Enqueues changes only
4. Incremental updates processed
### Performance Expectations
**Embedding Generation:**
- **Without GPU**: 1-5 notes/second (CPU-bound)
- **With GPU**: 50-200 notes/second (highly parallel)
- **Initial indexing**: 100 notes ≈ 20-100 seconds (CPU), 1-2 seconds (GPU)
**Search Query:**
- **Embedding generation**: 50-100ms
- **Vector search**: 10-50ms (depends on collection size)
- **Access verification**: 20-100ms per document (Nextcloud API calls)
- **Total latency**: 100-300ms typical
**Resource Usage:**
- **Idle**: Minimal (background scanner sleeps)
- **Scanning**: Moderate CPU (ETag checks, API calls)
- **Processing**: High CPU/GPU (embedding generation)
- **Searching**: Low to moderate (depends on query frequency)
### Background Sync Behavior
**Scanner Triggers:**
- Hourly (configurable via `VECTOR_SYNC_INTERVAL`)
- Manual trigger via `nc_trigger_vector_sync` (future)
**Queue Processing:**
- Continuous (workers always running)
- Batch processing (fetch 10 documents at a time)
- Concurrent workers (4 by default)
**Error Handling:**
- Individual document failures logged but don't stop scanning
- Retries for transient errors (network timeouts, rate limits)
- Failed documents skipped, re-attempted on next scan
**What Gets Indexed:**
- **Notes**: All notes accessible to the authenticated user
- **Future**: Calendar events, tasks, deck cards, files with text extraction, contacts
## Monitoring & Observability
### MCP Tools
**`nc_get_vector_sync_status`** - Check sync status
```python
{
"total_documents": 1234,
"indexed_documents": 1200,
"pending_documents": 34,
"sync_enabled": true,
"last_scan": "2025-01-15T14:30:00Z",
"status": "syncing" # idle | syncing | error
}
```
**Interpreting Status:**
- `idle`: No pending work, last scan completed successfully
- `syncing`: Currently processing documents
- `error`: Last scan failed (check logs)
### Logs to Check
**Scanner Logs:**
```
[INFO] Vector sync scanner started (interval: 3600s)
[INFO] Scanning notes: found 150 documents
[INFO] Changes detected: 5 new, 2 modified, 1 deleted
[INFO] Enqueued 7 documents for processing
```
**Processor Logs:**
```
[INFO] Processing document: note_123
[DEBUG] Generated embedding (768 dimensions)
[INFO] Stored vector in Qdrant: note_123
```
**Error Logs:**
```
[ERROR] Failed to generate embedding for note_123: Connection timeout
[WARN] Qdrant connection lost, retrying...
[ERROR] Ollama embedding failed: Model not found
```
**Log Locations:**
- **Docker**: `docker compose logs mcp`
- **Local**: stdout (redirect to file if needed)
- **Kubernetes**: `kubectl logs -f deployment/nextcloud-mcp-server`
### Metrics to Monitor
**Indexing Progress:**
- Total documents vs indexed documents
- Pending queue size
- Processing rate (docs/second)
**Search Performance:**
- Query latency (p50, p95, p99)
- Results per query
- Verification overhead (API calls per query)
**Resource Usage:**
- Qdrant memory/disk usage
- Ollama CPU/GPU usage
- MCP server memory
For detailed observability setup, see [docs/observability.md](observability.md).
## Troubleshooting from Architecture Perspective
### Documents Not Appearing in Search
**Diagnosis Flow:**
1. Check sync status: `nc_get_vector_sync_status`
- `sync_enabled: false` → Enable with `VECTOR_SYNC_ENABLED=true`
- `status: error` → Check scanner logs for failures
2. Check queue size:
- `pending_documents > 0` → Processing in progress, wait
- `pending_documents == 0` but `indexed_documents` low → Scan hasn't run yet (wait up to 1 hour)
3. Check Qdrant:
- Connection errors in logs → Verify `QDRANT_URL` or `QDRANT_LOCATION`
- Collection empty → First scan hasn't completed
4. Check Ollama:
- Embedding errors in logs → Verify `OLLAMA_BASE_URL`
- Model not found → Pull model: `ollama pull nomic-embed-text`
**Common Causes:**
- Sync disabled (default): Enable `VECTOR_SYNC_ENABLED=true`
- Ollama not running: Start Ollama service
- Qdrant not accessible: Check network/URL
- First scan in progress: Wait up to 1 hour + processing time
### Slow Search Performance
**Diagnosis:**
1. **Query embedding slow (>500ms)**:
- Ollama overloaded or CPU-bound
- Solution: Use GPU, upgrade CPU, or reduce concurrent requests
2. **Vector search slow (>200ms)**:
- Large collection (millions of vectors)
- Solution: Use network Qdrant with SSDs, add indexing
3. **Verification slow (>500ms)**:
- Many results to verify (10+ documents)
- Nextcloud API slow or overloaded
- Solution: Reduce `limit` parameter, optimize Nextcloud
**Performance Tuning:**
- Reduce search `limit` (default: 10 results)
- Use network Qdrant for large collections
- Enable Ollama GPU acceleration
- Check Nextcloud API response times
### Background Sync Stopped
**Diagnosis:**
1. Check logs for errors:
- Authentication failures (401/403) → Token expired (OAuth) or credentials invalid (BasicAuth)
- Connection timeouts → Network issues with Nextcloud/Qdrant/Ollama
- Rate limiting (429) → Reduce scan frequency
2. Check `nc_get_vector_sync_status`:
- `status: error` → See logs for details
- `last_scan` timestamp old (>2 hours) → Scanner may have crashed
3. Verify services:
- Qdrant accessible: `curl http://qdrant:6333/`
- Ollama accessible: `curl http://ollama:11434/api/tags`
- Nextcloud accessible: Check API health
**OAuth Mode (Future):**
- Offline access token expired → Re-provision via `provision_vector_sync`
- User deprovisioned access → Sync stops intentionally
### Out of Memory
**Diagnosis:**
1. Check Qdrant mode:
- In-memory mode with large collection → Switch to persistent or network mode
2. Check embedding batch size:
- Too many documents processed simultaneously → Reduce worker count
3. Check Ollama memory:
- Large models loaded → Use smaller embedding model
**Solutions:**
- Use persistent or network Qdrant (frees server memory)
- Reduce concurrent processor workers
- Use smaller embedding model (`all-minilm` instead of `nomic-embed-text`)
- Increase server memory allocation
## Limitations & Future Work
### Current Limitations
1. **Notes App Only**
- Architecture supports multiple apps (plugin system ready)
- Only `NotesScanner` and `NotesProcessor` implemented
- Future: Calendar, Deck, Files, Contacts
2. **MCP Sampling Support**
- `nc_semantic_search_answer` requires client sampling capability
- Not all MCP clients support sampling yet
- Graceful fallback: Returns documents without generated answer
3. **OAuth Background Sync**
- User-controlled background jobs not yet implemented
- Currently works in BasicAuth mode only
- Future: Users opt-in via `provision_vector_sync` tool
4. **No Incremental Updates**
- Document changes trigger full re-embedding
- Cannot update just modified paragraphs
- Future: Paragraph-level chunking and incremental updates
5. **No Query Caching**
- Each search generates new query embedding
- Repeated queries re-search Qdrant
- Future: Cache recent query embeddings and results
6. **Single Embedding Model**
- Uses one model for all documents and queries
- Cannot customize per app or user
- Future: App-specific or user-selected models
### Future Enhancements
**Multi-App Support** (In Progress):
- Scanner plugins for Calendar, Deck, Files, Contacts
- Unified vector search across all apps
- App-specific metadata in vector payloads
**User-Controlled Sync (OAuth Mode)**:
- `provision_vector_sync` and `deprovision_vector_sync` tools
- Per-user background job scheduling
- User dashboard for sync status and controls
**Advanced Search Features**:
- Hybrid search (vector + keyword combined)
- Filtering by date range, app type, tags
- Aggregations and faceted search
- Search result explanations (why this matched)
**Performance Optimizations**:
- Query caching for repeated searches
- Incremental document updates (paragraph-level)
- Batch query processing
- Qdrant HNSW indexing tuning
**Embedding Improvements**:
- Support for OpenAI embeddings (ada-002, text-embedding-3)
- Multi-language embedding models
- Fine-tuned models for Nextcloud content
- Paragraph-level chunking for long documents
## References
### Architecture Decision Records (ADRs)
- **[ADR-003: Vector Database Semantic Search](ADR-003-vector-database-semantic-search.md)** - Qdrant selection rationale, embedding strategy, hybrid search (superseded by ADR-007 but technical decisions remain valid)
- **[ADR-007: Background Vector Sync Job Management](ADR-007-background-vector-sync-job-management.md)** - Current implementation, Scanner-Queue-Processor architecture, plugin system
- **[ADR-008: MCP Sampling for Semantic Search](ADR-008-mcp-sampling-for-semantic-search.md)** - RAG with MCP sampling, client-server separation, prompt construction
- **[ADR-009: Semantic Search OAuth Scope](ADR-009-semantic-search-oauth-scope.md)** - OAuth scope model, dual-phase authorization, security rationale
### Configuration & Setup
- **[Configuration Guide](configuration.md)** - Environment variables, Qdrant setup, Ollama setup, detailed configuration options
- **[Installation Guide](installation.md)** - Deployment options (Docker, Kubernetes, local)
- **[Running the Server](running.md)** - Starting the server, transport options, testing
### Monitoring & Troubleshooting
- **[Observability Guide](observability.md)** - Logging, metrics, tracing, debugging
- **[Troubleshooting](troubleshooting.md)** - General issues and solutions
### Related Documentation
- **[OAuth Architecture](oauth-architecture.md)** - OAuth flows, scopes, token management
- **[Comparison with Context Agent](comparison-context-agent.md)** - When to use Nextcloud MCP Server vs Context Agent
---
**Questions or Issues?**
- [Open an issue](https://github.com/cbcoutinho/nextcloud-mcp-server/issues)
- [Contribute improvements](https://github.com/cbcoutinho/nextcloud-mcp-server/pulls)
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@@ -136,27 +136,24 @@ A patch for the `user_oidc` app is required to fix Bearer token support. See [oa
---
### Issue: "Permission denied" or "Database is locked" when accessing OAuth client storage
### Issue: "Permission denied" when reading/writing OAuth client credentials file
**Cause:** The server cannot access the SQLite database for OAuth client credentials storage.
**Cause:** The server cannot access the OAuth client storage file (default: `.nextcloud_oauth_client.json`).
**Solution:**
```bash
# Check database directory permissions
ls -la data/
# Check file permissions
ls -la .nextcloud_oauth_client.json
# Ensure directory is writable
chmod 755 data/
# Fix file permissions (should be 0600 - owner read/write only)
chmod 600 .nextcloud_oauth_client.json
# Check if database file exists and has correct permissions
ls -la data/tokens.db
chmod 644 data/tokens.db
# Ensure the directory is writable
chmod 755 $(dirname .nextcloud_oauth_client.json)
# For Docker deployments, ensure volume is mounted correctly:
# docker-compose.yml should have:
# volumes:
# - ./data:/app/data
# If the file doesn't exist, ensure the directory is writable so it can be created
mkdir -p $(dirname .nextcloud_oauth_client.json)
```
---
+1 -102
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@@ -8,41 +8,12 @@ NEXTCLOUD_HOST=
# - Requires Nextcloud OIDC app installed and configured
# - Admin must enable "Dynamic Client Registration" in OIDC app settings
# - Leave NEXTCLOUD_USERNAME and NEXTCLOUD_PASSWORD empty to use OAuth mode
# - OAuth client credentials are stored encrypted in SQLite (TOKEN_STORAGE_DB)
# - Optional: Pre-register client and provide credentials (otherwise auto-registers)
NEXTCLOUD_OIDC_CLIENT_ID=
NEXTCLOUD_OIDC_CLIENT_SECRET=
NEXTCLOUD_OIDC_CLIENT_STORAGE=.nextcloud_oauth_client.json
NEXTCLOUD_MCP_SERVER_URL=http://localhost:8000
# OAuth Storage Configuration (SQLite storage for OAuth clients and refresh tokens)
# TOKEN_ENCRYPTION_KEY: Required for encrypting OAuth client secrets and refresh tokens
# Generate with: python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
#TOKEN_ENCRYPTION_KEY=
# TOKEN_STORAGE_DB: Path to SQLite database (default: /app/data/tokens.db)
#TOKEN_STORAGE_DB=/app/data/tokens.db
# ===== ADR-004 PROGRESSIVE CONSENT CONFIGURATION =====
# Enable Progressive Consent mode (dual OAuth flows)
# When enabled: Flow 1 for client auth, Flow 2 for Nextcloud resource access
# When disabled: Uses existing hybrid flow (backward compatible)
# MCP Server OAuth Client Configuration
# The MCP server's own OAuth client credentials for Flow 2
# If not set, will use dynamic client registration
#MCP_SERVER_CLIENT_ID=
#MCP_SERVER_CLIENT_SECRET=
# Allowed MCP Client IDs (comma-separated list)
# Client IDs that are allowed to authenticate in Flow 1
# Examples: claude-desktop,continue-dev,zed-editor
#ALLOWED_MCP_CLIENTS=claude-desktop,continue-dev,zed-editor
# Token cache configuration for Token Broker Service
# Cache TTL in seconds (default: 300 = 5 minutes)
#TOKEN_CACHE_TTL=300
# Early refresh threshold in seconds (default: 30)
#TOKEN_CACHE_EARLY_REFRESH=30
# Option 2: Basic Authentication (LEGACY - Less Secure)
# - Requires username and password
# - Credentials stored in environment variables
@@ -124,75 +95,3 @@ ENABLE_CUSTOM_PROCESSOR=false
# Comma-separated MIME types your processor supports
#CUSTOM_PROCESSOR_TYPES=application/pdf,image/jpeg,image/png
# ============================================
# Semantic Search & Vector Sync Configuration
# ============================================
# EXPERIMENTAL: Semantic search for Notes app (multi-app support planned)
# Requires: Qdrant vector database + Ollama embedding service
# Disabled by default
# Enable background vector indexing
VECTOR_SYNC_ENABLED=false
# Document scan interval in seconds (default: 300 = 5 minutes)
# How often to check for new/updated documents
#VECTOR_SYNC_SCAN_INTERVAL=300
# Concurrent indexing workers (default: 3)
# Number of parallel workers for embedding generation
#VECTOR_SYNC_PROCESSOR_WORKERS=3
# Max queued documents (default: 10000)
# Maximum documents waiting to be processed
#VECTOR_SYNC_QUEUE_MAX_SIZE=10000
# ============================================
# Qdrant Vector Database Configuration
# ============================================
# Choose ONE of three modes:
# 1. In-memory mode (default): Set neither QDRANT_URL nor QDRANT_LOCATION
# 2. Persistent local: Set QDRANT_LOCATION=/path/to/data
# 3. Network mode: Set QDRANT_URL=http://qdrant:6333
# Network mode: URL to Qdrant service
#QDRANT_URL=http://qdrant:6333
# Local mode: Path to store vectors (use :memory: for in-memory)
#QDRANT_LOCATION=:memory:
# API key for network mode (optional)
#QDRANT_API_KEY=
# Collection name (optional - auto-generated if not set)
# Auto-generation format: {deployment-id}-{model-name}
# Allows safe model switching and multi-server deployments
#QDRANT_COLLECTION=nextcloud_content
# ============================================
# Ollama Embedding Service Configuration
# ============================================
# Ollama endpoint for embeddings (if not set, uses SimpleEmbeddingProvider fallback)
#OLLAMA_BASE_URL=http://ollama:11434
# Embedding model to use (default: nomic-embed-text, 768 dimensions)
# Changing this creates a new collection (requires re-embedding all documents)
#OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# Verify SSL certificates (default: true)
#OLLAMA_VERIFY_SSL=true
# ============================================
# Document Chunking Configuration
# ============================================
# Configure how documents are split before embedding
# Words per chunk (default: 512)
# Smaller chunks (256-384): More precise, less context, more storage
# Larger chunks (768-1024): More context, less precise, less storage
#DOCUMENT_CHUNK_SIZE=512
# Overlapping words between chunks (default: 50)
# Recommended: 10-20% of chunk size
# Preserves context across chunk boundaries
#DOCUMENT_CHUNK_OVERLAP=50
+64
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@@ -0,0 +1,64 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Nextcloud MCP Server Helm Chart</title>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
max-width: 800px;
margin: 50px auto;
padding: 20px;
line-height: 1.6;
}
code {
background: #f4f4f4;
padding: 2px 6px;
border-radius: 3px;
font-family: "Monaco", "Courier New", monospace;
}
pre {
background: #f4f4f4;
padding: 15px;
border-radius: 5px;
overflow-x: auto;
}
h1, h2 { color: #0082c9; }
a { color: #0082c9; text-decoration: none; }
a:hover { text-decoration: underline; }
</style>
</head>
<body>
<h1>Nextcloud MCP Server Helm Chart</h1>
<p>A Helm chart for deploying the Nextcloud MCP (Model Context Protocol) Server on Kubernetes, enabling AI assistants to interact with your Nextcloud instance.</p>
<h2>Installation</h2>
<p>Add the Helm repository:</p>
<pre><code>helm repo add nextcloud-mcp https://cbcoutinho.github.io/nextcloud-mcp-server/
helm repo update</code></pre>
<p>Install the chart:</p>
<pre><code>helm install nextcloud-mcp nextcloud-mcp/nextcloud-mcp-server \
--set nextcloud.host=https://cloud.example.com \
--set auth.basic.username=myuser \
--set auth.basic.password=mypassword</code></pre>
<h2>Documentation</h2>
<ul>
<li><a href="README.md">Chart README</a> - Full documentation for the Helm chart</li>
<li><a href="https://github.com/cbcoutinho/nextcloud-mcp-server">GitHub Repository</a> - Source code and issues</li>
<li><a href="index.yaml">Helm Repository Index</a> - Chart metadata</li>
</ul>
<h2>Quick Start</h2>
<p>See the <a href="README.md">full documentation</a> for detailed configuration options, examples, and troubleshooting guides.</p>
<hr>
<p><small>Generated by <a href="https://github.com/helm/chart-releaser">chart-releaser</a></small></p>
</body>
</html>
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-852
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@@ -1,852 +0,0 @@
{
"id": "nextcloud-mcp",
"realm": "nextcloud-mcp",
"notBefore": 0,
"defaultSignatureAlgorithm": "RS256",
"revokeRefreshToken": false,
"refreshTokenMaxReuse": 0,
"accessTokenLifespan": 300,
"accessTokenLifespanForImplicitFlow": 900,
"ssoSessionIdleTimeout": 1800,
"ssoSessionMaxLifespan": 36000,
"offlineSessionIdleTimeout": 2592000,
"offlineSessionMaxLifespanEnabled": false,
"offlineSessionMaxLifespan": 5184000,
"accessCodeLifespan": 60,
"accessCodeLifespanUserAction": 300,
"accessCodeLifespanLogin": 1800,
"enabled": true,
"sslRequired": "external",
"registrationAllowed": false,
"loginWithEmailAllowed": true,
"duplicateEmailsAllowed": false,
"resetPasswordAllowed": false,
"editUsernameAllowed": false,
"bruteForceProtected": false,
"attributes": {
"frontendUrl": "http://localhost:8888"
},
"roles": {
"realm": [
{
"name": "offline_access",
"description": "${role_offline-access}",
"composite": false,
"clientRole": false
},
{
"name": "uma_authorization",
"description": "${role_uma_authorization}",
"composite": false,
"clientRole": false
},
{
"name": "default-roles-nextcloud-mcp",
"description": "${role_default-roles}",
"composite": true,
"composites": {
"realm": [
"offline_access",
"uma_authorization"
]
},
"clientRole": false
}
]
},
"users": [
{
"username": "admin",
"enabled": true,
"email": "admin@example.com",
"emailVerified": true,
"firstName": "Admin",
"lastName": "User",
"credentials": [
{
"type": "password",
"value": "admin",
"temporary": false
}
],
"realmRoles": [
"default-roles-nextcloud-mcp",
"offline_access"
],
"attributes": {
"quota": [
"1073741824"
]
}
},
{
"username": "test_read_only",
"enabled": true,
"email": "readonly@example.com",
"emailVerified": true,
"firstName": "Read",
"lastName": "Only",
"credentials": [
{
"type": "password",
"value": "test123",
"temporary": false
}
],
"realmRoles": [
"default-roles-nextcloud-mcp",
"offline_access"
],
"attributes": {
"quota": [
"1073741824"
]
}
},
{
"username": "test_write_only",
"enabled": true,
"email": "writeonly@example.com",
"emailVerified": true,
"firstName": "Write",
"lastName": "Only",
"credentials": [
{
"type": "password",
"value": "test123",
"temporary": false
}
],
"realmRoles": [
"default-roles-nextcloud-mcp",
"offline_access"
],
"attributes": {
"quota": [
"1073741824"
]
}
},
{
"username": "test_no_scopes",
"enabled": true,
"email": "noscopes@example.com",
"emailVerified": true,
"firstName": "No",
"lastName": "Scopes",
"credentials": [
{
"type": "password",
"value": "test123",
"temporary": false
}
],
"realmRoles": [
"default-roles-nextcloud-mcp",
"offline_access"
],
"attributes": {
"quota": [
"1073741824"
]
}
},
{
"username": "service-account-nextcloud-mcp-server",
"enabled": true,
"serviceAccountClientId": "nextcloud-mcp-server",
"clientRoles": {
"realm-management": [
"impersonation"
]
}
}
],
"clients": [
{
"clientId": "nextcloud",
"name": "Nextcloud Resource Server",
"description": "Resource server for Nextcloud APIs - used by user_oidc app for bearer token validation and as token exchange target",
"enabled": true,
"clientAuthenticatorType": "client-secret",
"secret": "nextcloud-secret-change-in-production",
"redirectUris": [],
"webOrigins": [],
"bearerOnly": false,
"consentRequired": false,
"standardFlowEnabled": false,
"implicitFlowEnabled": false,
"directAccessGrantsEnabled": false,
"serviceAccountsEnabled": true,
"authorizationServicesEnabled": true,
"publicClient": false,
"protocol": "openid-connect",
"attributes": {
"display.on.consent.screen": "false",
"token.exchange.grant.enabled": "true",
"client.token.exchange.standard.enabled": "true",
"standard.token.exchange.enabled": "true"
},
"authorizationSettings": {
"allowRemoteResourceManagement": true,
"policyEnforcementMode": "ENFORCING",
"resources": [
{
"name": "token-exchange",
"type": "urn:keycloak:token-exchange",
"ownerManagedAccess": false,
"displayName": "Token Exchange",
"attributes": {},
"uris": [],
"scopes": [
{
"name": "token-exchange"
}
]
}
],
"policies": [
{
"name": "allow-nextcloud-mcp-server-to-exchange",
"description": "",
"type": "client",
"logic": "POSITIVE",
"decisionStrategy": "UNANIMOUS",
"config": {
"clients": "[\"nextcloud-mcp-server\",\"nextcloud\"]"
}
},
{
"name": "token-exchange-permission",
"description": "",
"type": "scope",
"logic": "POSITIVE",
"decisionStrategy": "AFFIRMATIVE",
"config": {
"resources": "[\"token-exchange\"]",
"scopes": "[\"token-exchange\"]",
"applyPolicies": "[\"allow-nextcloud-mcp-server-to-exchange\"]"
}
}
],
"scopes": [
{
"name": "token-exchange",
"displayName": "Token Exchange"
}
],
"decisionStrategy": "UNANIMOUS"
},
"fullScopeAllowed": true,
"nodeReRegistrationTimeout": -1
},
{
"clientId": "nextcloud-mcp-server",
"name": "Nextcloud MCP Server",
"enabled": true,
"clientAuthenticatorType": "client-secret",
"secret": "mcp-secret-change-in-production",
"redirectUris": [
"http://localhost:*",
"http://127.0.0.1:*",
"http://localhost:*/callback",
"http://127.0.0.1:*/callback"
],
"webOrigins": [
"+"
],
"bearerOnly": false,
"consentRequired": false,
"standardFlowEnabled": true,
"implicitFlowEnabled": false,
"directAccessGrantsEnabled": true,
"serviceAccountsEnabled": true,
"publicClient": false,
"frontchannelLogout": false,
"protocol": "openid-connect",
"attributes": {
"pkce.code.challenge.method": "S256",
"use.refresh.tokens": "true",
"backchannel.logout.session.required": "true",
"backchannel.logout.url": "http://app:80/index.php/apps/user_oidc/backchannel-logout/keycloak",
"oauth2.device.authorization.grant.enabled": "false",
"oidc.ciba.grant.enabled": "false",
"client_credentials.use_refresh_token": "false",
"display.on.consent.screen": "false",
"token.exchange.grant.enabled": "true",
"client.token.exchange.standard.enabled": "true",
"standard.token.exchange.enabled": "true"
},
"fullScopeAllowed": true,
"nodeReRegistrationTimeout": -1,
"protocolMappers": [
{
"name": "mcp-server-audience",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"consentRequired": false,
"config": {
"included.client.audience": "nextcloud-mcp-server",
"access.token.claim": "true",
"id.token.claim": "false",
"introspection.token.claim": "true"
}
},
{
"name": "nextcloud-audience",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"consentRequired": false,
"config": {
"included.client.audience": "nextcloud",
"access.token.claim": "true",
"id.token.claim": "false",
"introspection.token.claim": "true"
}
},
{
"name": "sub",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "username",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "sub",
"jsonType.label": "String"
}
},
{
"name": "full name",
"protocol": "openid-connect",
"protocolMapper": "oidc-full-name-mapper",
"consentRequired": false,
"config": {
"id.token.claim": "true",
"access.token.claim": "true",
"userinfo.token.claim": "true"
}
},
{
"name": "email",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "email",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "email",
"jsonType.label": "String"
}
},
{
"name": "preferred_username",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "username",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "preferred_username",
"jsonType.label": "String"
}
},
{
"name": "quota",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-attribute-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "quota",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "quota",
"jsonType.label": "String"
}
}
],
"defaultClientScopes": [
"web-origins",
"profile",
"roles",
"email"
],
"optionalClientScopes": [
"address",
"phone",
"offline_access",
"microprofile-jwt",
"notes:read",
"notes:write",
"calendar:read",
"calendar:write",
"contacts:read",
"contacts:write",
"cookbook:read",
"cookbook:write",
"deck:read",
"deck:write",
"tables:read",
"tables:write",
"files:read",
"files:write",
"sharing:read",
"sharing:write",
"todo:read",
"todo:write"
]
}
],
"clientScopes": [
{
"name": "offline_access",
"description": "OpenID Connect built-in scope: offline_access",
"protocol": "openid-connect",
"attributes": {
"consent.screen.text": "${offlineAccessScopeConsentText}",
"display.on.consent.screen": "true"
}
},
{
"name": "profile",
"description": "OpenID Connect built-in scope: profile",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true"
},
"protocolMappers": [
{
"name": "full name",
"protocol": "openid-connect",
"protocolMapper": "oidc-full-name-mapper",
"consentRequired": false,
"config": {
"id.token.claim": "true",
"access.token.claim": "true",
"userinfo.token.claim": "true"
}
},
{
"name": "username",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "username",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "preferred_username",
"jsonType.label": "String"
}
},
{
"name": "given name",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "firstName",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "given_name",
"jsonType.label": "String"
}
},
{
"name": "family name",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "lastName",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "family_name",
"jsonType.label": "String"
}
}
]
},
{
"name": "email",
"description": "OpenID Connect built-in scope: email",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true"
},
"protocolMappers": [
{
"name": "email",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "email",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "email",
"jsonType.label": "String"
}
},
{
"name": "email verified",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-property-mapper",
"consentRequired": false,
"config": {
"userinfo.token.claim": "true",
"user.attribute": "emailVerified",
"id.token.claim": "true",
"access.token.claim": "true",
"claim.name": "email_verified",
"jsonType.label": "boolean"
}
}
]
},
{
"name": "roles",
"description": "OpenID Connect scope for add user roles to the access token",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "false",
"display.on.consent.screen": "true"
},
"protocolMappers": [
{
"name": "realm roles",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-realm-role-mapper",
"consentRequired": false,
"config": {
"user.attribute": "foo",
"access.token.claim": "true",
"claim.name": "realm_access.roles",
"jsonType.label": "String",
"multivalued": "true"
}
},
{
"name": "client roles",
"protocol": "openid-connect",
"protocolMapper": "oidc-usermodel-client-role-mapper",
"consentRequired": false,
"config": {
"user.attribute": "foo",
"access.token.claim": "true",
"claim.name": "resource_access.${client_id}.roles",
"jsonType.label": "String",
"multivalued": "true"
}
}
]
},
{
"name": "web-origins",
"description": "OpenID Connect scope for add allowed web origins to the access token",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "false",
"display.on.consent.screen": "false"
},
"protocolMappers": [
{
"name": "allowed web origins",
"protocol": "openid-connect",
"protocolMapper": "oidc-allowed-origins-mapper",
"consentRequired": false,
"config": {}
}
]
},
{
"name": "notes:read",
"description": "Nextcloud Notes read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your notes"
}
},
{
"name": "notes:write",
"description": "Nextcloud Notes write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete your notes"
}
},
{
"name": "calendar:read",
"description": "Nextcloud Calendar read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your calendars and events"
}
},
{
"name": "calendar:write",
"description": "Nextcloud Calendar write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete calendars and events"
}
},
{
"name": "contacts:read",
"description": "Nextcloud Contacts read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your contacts"
}
},
{
"name": "contacts:write",
"description": "Nextcloud Contacts write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete contacts"
}
},
{
"name": "cookbook:read",
"description": "Nextcloud Cookbook read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your recipes"
}
},
{
"name": "cookbook:write",
"description": "Nextcloud Cookbook write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete recipes"
}
},
{
"name": "deck:read",
"description": "Nextcloud Deck read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your boards and cards"
}
},
{
"name": "deck:write",
"description": "Nextcloud Deck write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete boards and cards"
}
},
{
"name": "tables:read",
"description": "Nextcloud Tables read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your tables and rows"
}
},
{
"name": "tables:write",
"description": "Nextcloud Tables write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete tables and rows"
}
},
{
"name": "files:read",
"description": "Nextcloud Files read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your files"
}
},
{
"name": "files:write",
"description": "Nextcloud Files write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Upload, update, and delete files"
}
},
{
"name": "sharing:read",
"description": "Nextcloud Sharing read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "View shared resources"
}
},
{
"name": "sharing:write",
"description": "Nextcloud Sharing write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create and manage shares"
}
},
{
"name": "todo:read",
"description": "Nextcloud Tasks/Todo read access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Read your tasks"
}
},
{
"name": "todo:write",
"description": "Nextcloud Tasks/Todo write access",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "true",
"display.on.consent.screen": "true",
"consent.screen.text": "Create, update, and delete tasks"
}
},
{
"name": "default-audience",
"protocol": "openid-connect",
"attributes": {
"include.in.token.scope": "false",
"display.on.consent.screen": "false",
"gui.order": "",
"consent.screen.text": ""
},
"protocolMappers": [
{
"name": "mcp-server-audience",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"consentRequired": false,
"config": {
"included.client.audience": "nextcloud-mcp-server",
"access.token.claim": "true",
"id.token.claim": "false"
}
},
{
"name": "mcp-url-audience",
"protocol": "openid-connect",
"protocolMapper": "oidc-audience-mapper",
"consentRequired": false,
"config": {
"included.custom.audience": "http://localhost:8002",
"access.token.claim": "true",
"id.token.claim": "false"
}
}
]
}
],
"components": {
"org.keycloak.services.clientregistration.policy.ClientRegistrationPolicy": [
{
"name": "Trusted Hosts",
"providerId": "trusted-hosts",
"subType": "anonymous",
"subComponents": {},
"config": {
"trusted-hosts": [
"localhost",
"127.0.0.1",
"172.19.0.1"
],
"host-sending-registration-request-must-match": [
"false"
],
"client-uris-must-match": [
"true"
]
}
},
{
"name": "Max Clients",
"providerId": "max-clients",
"subType": "anonymous",
"subComponents": {},
"config": {
"max-clients": [
"200"
]
}
}
]
},
"defaultDefaultClientScopes": [
"profile",
"email",
"roles",
"web-origins",
"default-audience"
],
"defaultOptionalClientScopes": [
"offline_access",
"notes:read",
"notes:write",
"calendar:read",
"calendar:write",
"contacts:read",
"contacts:write",
"cookbook:read",
"cookbook:write",
"deck:read",
"deck:write",
"tables:read",
"tables:write",
"files:read",
"files:write",
"sharing:read",
"sharing:write",
"todo:read",
"todo:write"
]
}
+414 -1008
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+4 -4
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@@ -1,7 +1,7 @@
"""OAuth authentication components for Nextcloud MCP server."""
from .bearer_auth import BearerAuth
from .client_registration import ensure_oauth_client, register_client
from .client_registration import load_or_register_client, register_client
from .context_helper import get_client_from_context
from .scope_authorization import (
InsufficientScopeError,
@@ -14,13 +14,13 @@ from .scope_authorization import (
is_jwt_token,
require_scopes,
)
from .unified_verifier import UnifiedTokenVerifier
from .token_verifier import NextcloudTokenVerifier
__all__ = [
"BearerAuth",
"UnifiedTokenVerifier",
"NextcloudTokenVerifier",
"register_client",
"ensure_oauth_client",
"load_or_register_client",
"get_client_from_context",
"require_scopes",
"ScopeAuthorizationError",
@@ -1,420 +0,0 @@
"""Browser-based OAuth login routes for admin UI.
Separate from MCP OAuth flow - these routes establish browser sessions
for accessing admin UI endpoints like /app.
"""
import hashlib
import logging
import os
import secrets
from base64 import urlsafe_b64encode
from urllib.parse import urlencode
import httpx
import jwt
from starlette.requests import Request
from starlette.responses import HTMLResponse, JSONResponse, RedirectResponse
from nextcloud_mcp_server.auth.userinfo_routes import (
_get_userinfo_endpoint,
_query_idp_userinfo,
)
logger = logging.getLogger(__name__)
async def oauth_login(request: Request) -> RedirectResponse | JSONResponse:
"""Browser OAuth login endpoint - redirects to IdP for authentication.
This is separate from the MCP OAuth flow (/oauth/authorize).
Creates a browser session with refresh token for admin UI access.
Query parameters:
next: Optional URL to redirect to after login (default: /user/page)
Returns:
302 redirect to IdP authorization endpoint
"""
oauth_ctx = request.app.state.oauth_context
if not oauth_ctx:
# BasicAuth mode - no login needed, redirect to app
return RedirectResponse("/app", status_code=302)
storage = oauth_ctx["storage"]
oauth_client = oauth_ctx["oauth_client"]
oauth_config = oauth_ctx["config"]
# Debug: Log oauth_config contents
logger.info(f"oauth_login called - oauth_config keys: {oauth_config.keys()}")
logger.info(f"oauth_login called - client_id: {oauth_config.get('client_id')}")
logger.info(f"oauth_login called - oauth_client: {oauth_client is not None}")
# Generate state for CSRF protection
state = secrets.token_urlsafe(32)
# Build OAuth authorization URL
mcp_server_url = oauth_config["mcp_server_url"]
callback_uri = f"{mcp_server_url}/oauth/callback"
# Request only basic OIDC scopes for browser session
# Note: Nextcloud app scopes (notes:read, etc.) are for MCP client access tokens,
# not for the MCP server's own browser authentication
scopes = "openid profile email offline_access"
# Generate PKCE values for ALL modes (both external and integrated IdP require PKCE)
code_verifier = secrets.token_urlsafe(32)
digest = hashlib.sha256(code_verifier.encode()).digest()
code_challenge = urlsafe_b64encode(digest).decode().rstrip("=")
# Store code_verifier in session for retrieval during callback (using state as key)
await storage.store_oauth_session(
session_id=state, # Use state as session ID
client_id="browser-ui",
client_redirect_uri="/app",
state=state,
code_challenge=code_challenge,
code_challenge_method="S256",
mcp_authorization_code=code_verifier, # Store code_verifier here temporarily
flow_type="browser",
ttl_seconds=600, # 10 minutes
)
if oauth_client:
# External IdP mode (Keycloak)
if not oauth_client.authorization_endpoint:
await oauth_client.discover()
idp_params = {
"client_id": oauth_client.client_id,
"redirect_uri": callback_uri,
"response_type": "code",
"scope": scopes,
"state": state,
"code_challenge": code_challenge,
"code_challenge_method": "S256",
"prompt": "consent", # Ensure refresh token
}
auth_url = f"{oauth_client.authorization_endpoint}?{urlencode(idp_params)}"
logger.info(f"Redirecting to external IdP login: {auth_url.split('?')[0]}")
else:
# Integrated mode (Nextcloud OIDC)
discovery_url = oauth_config.get("discovery_url")
if not discovery_url:
return JSONResponse(
{
"error": "server_error",
"error_description": "OAuth discovery URL not configured",
},
status_code=500,
)
# Fetch authorization endpoint
async with httpx.AsyncClient() as http_client:
response = await http_client.get(discovery_url)
response.raise_for_status()
discovery = response.json()
authorization_endpoint = discovery["authorization_endpoint"]
# Replace internal Docker hostname with public URL
public_issuer = os.getenv("NEXTCLOUD_PUBLIC_ISSUER_URL")
if public_issuer:
from urllib.parse import urlparse as parse_url
internal_parsed = parse_url(oauth_config["nextcloud_host"])
auth_parsed = parse_url(authorization_endpoint)
if auth_parsed.hostname == internal_parsed.hostname:
public_parsed = parse_url(public_issuer)
authorization_endpoint = (
f"{public_parsed.scheme}://{public_parsed.netloc}{auth_parsed.path}"
)
idp_params = {
"client_id": oauth_config["client_id"],
"redirect_uri": callback_uri,
"response_type": "code",
"scope": scopes,
"state": state,
"code_challenge": code_challenge,
"code_challenge_method": "S256",
"prompt": "consent", # Ensure refresh token
}
# Debug: Log full parameters
logger.info(f"Building Nextcloud OIDC auth URL with params: {idp_params}")
auth_url = f"{authorization_endpoint}?{urlencode(idp_params)}"
logger.info(f"Redirecting to Nextcloud OIDC login: {auth_url}")
return RedirectResponse(auth_url, status_code=302)
async def oauth_login_callback(request: Request) -> RedirectResponse | HTMLResponse:
"""Browser OAuth callback - IdP redirects here after authentication.
Exchanges authorization code for tokens, stores refresh token,
sets session cookie, and redirects to original destination.
Query parameters:
code: Authorization code from IdP
state: State parameter
error: Error code (if authorization failed)
Returns:
302 redirect to next URL with session cookie
"""
# Check for errors
error = request.query_params.get("error")
if error:
error_description = request.query_params.get(
"error_description", "Authorization failed"
)
logger.error(f"OAuth login error: {error} - {error_description}")
login_url = str(request.url_for("oauth_login"))
return HTMLResponse(
f"""
<!DOCTYPE html>
<html>
<head><title>Login Failed</title></head>
<body>
<h1>Login Failed</h1>
<p>Error: {error}</p>
<p>{error_description}</p>
<p><a href="{login_url}">Try again</a></p>
</body>
</html>
""",
status_code=400,
)
# Extract code and state
code = request.query_params.get("code")
state = request.query_params.get("state")
if not code or not state:
return HTMLResponse(
"""
<!DOCTYPE html>
<html>
<head><title>Invalid Request</title></head>
<body>
<h1>Invalid Request</h1>
<p>Missing code or state parameter</p>
</body>
</html>
""",
status_code=400,
)
# Get OAuth context
oauth_ctx = request.app.state.oauth_context
storage = oauth_ctx["storage"]
oauth_client = oauth_ctx["oauth_client"]
oauth_config = oauth_ctx["config"]
# Retrieve code_verifier from session storage (PKCE required for all modes)
code_verifier = ""
oauth_session = await storage.get_oauth_session(state)
if oauth_session:
# code_verifier was stored in mcp_authorization_code field
code_verifier = oauth_session.get("mcp_authorization_code", "")
# Clean up the temporary session
# Note: We don't have delete_oauth_session method, but it will expire after TTL
# Exchange authorization code for tokens
mcp_server_url = oauth_config["mcp_server_url"]
callback_uri = f"{mcp_server_url}/oauth/callback"
try:
if oauth_client:
# External IdP mode (Keycloak)
# Use PKCE if we have a code_verifier
if not oauth_client.token_endpoint:
await oauth_client.discover()
token_params = {
"grant_type": "authorization_code",
"code": code,
"redirect_uri": callback_uri,
"client_id": oauth_client.client_id,
"client_secret": oauth_client.client_secret,
}
# Add code_verifier if we have one (PKCE)
if code_verifier:
token_params["code_verifier"] = code_verifier
async with httpx.AsyncClient() as http_client:
response = await http_client.post(
oauth_client.token_endpoint,
data=token_params,
)
response.raise_for_status()
token_data = response.json()
else:
# Integrated mode (Nextcloud OIDC)
discovery_url = oauth_config.get("discovery_url")
async with httpx.AsyncClient() as http_client:
response = await http_client.get(discovery_url)
response.raise_for_status()
discovery = response.json()
token_endpoint = discovery["token_endpoint"]
token_params = {
"grant_type": "authorization_code",
"code": code,
"redirect_uri": callback_uri,
"client_id": oauth_config["client_id"],
"client_secret": oauth_config["client_secret"],
}
# Add code_verifier for PKCE (required by Nextcloud OIDC)
if code_verifier:
token_params["code_verifier"] = code_verifier
async with httpx.AsyncClient() as http_client:
response = await http_client.post(
token_endpoint,
data=token_params,
)
response.raise_for_status()
token_data = response.json()
except httpx.HTTPStatusError as e:
error_body = (
e.response.text if hasattr(e.response, "text") else str(e.response.content)
)
logger.error(
f"Token exchange failed: HTTP {e.response.status_code} - {error_body}"
)
return HTMLResponse(
f"""
<!DOCTYPE html>
<html>
<head><title>Login Failed</title></head>
<body>
<h1>Login Failed</h1>
<p>Failed to exchange authorization code for tokens</p>
<p>HTTP {e.response.status_code}: {error_body}</p>
</body>
</html>
""",
status_code=500,
)
except Exception as e:
logger.error(f"Token exchange failed: {e}")
return HTMLResponse(
f"""
<!DOCTYPE html>
<html>
<head><title>Login Failed</title></head>
<body>
<h1>Login Failed</h1>
<p>Failed to exchange authorization code for tokens</p>
<p>Error: {e}</p>
</body>
</html>
""",
status_code=500,
)
refresh_token = token_data.get("refresh_token")
id_token = token_data.get("id_token")
logger.info(f"Token exchange response keys: {token_data.keys()}")
logger.info(f"Refresh token present: {refresh_token is not None}")
logger.info(f"ID token present: {id_token is not None}")
# Decode ID token to get user info
try:
userinfo = jwt.decode(id_token, options={"verify_signature": False})
user_id = userinfo.get("sub")
username = userinfo.get("preferred_username") or userinfo.get("email")
logger.info(f"Browser login successful: {username} (sub={user_id})")
except Exception as e:
logger.warning(f"Failed to decode ID token: {e}")
user_id = f"user-{secrets.token_hex(8)}"
username = "unknown"
# Store refresh token (for background jobs ONLY)
if refresh_token:
logger.info(f"Storing refresh token for user_id: {user_id}")
logger.info(f" State parameter (provisioning_client_id): {state[:16]}...")
await storage.store_refresh_token(
user_id=user_id,
refresh_token=refresh_token,
expires_at=None,
flow_type="browser", # Browser-based login flow
provisioning_client_id=state, # Store state for unified session lookup
)
logger.info(f"✓ Refresh token stored successfully for user_id: {user_id}")
logger.info(
f" Token can now be found via provisioning_client_id={state[:16]}..."
)
else:
logger.warning("No refresh token in token response - cannot store session")
# Query and cache user profile (for browser UI display)
access_token = token_data.get("access_token")
if access_token:
try:
# Get the OAuth context to determine correct userinfo endpoint
oauth_ctx = getattr(request.app.state, "oauth_context", {})
userinfo_endpoint = await _get_userinfo_endpoint(oauth_ctx)
if userinfo_endpoint:
# Query userinfo endpoint with fresh access token
profile_data = await _query_idp_userinfo(
access_token, userinfo_endpoint
)
if profile_data:
# Cache profile for browser UI (no token needed to display)
await storage.store_user_profile(user_id, profile_data)
logger.info(f"✓ User profile cached for {user_id}")
else:
logger.warning(f"Failed to query userinfo endpoint for {user_id}")
else:
logger.warning("Could not determine userinfo endpoint")
except Exception as e:
logger.error(f"Error caching user profile: {e}")
# Continue anyway - profile cache is optional for browser UI
# Create response and set session cookie
response = RedirectResponse("/app", status_code=302)
response.set_cookie(
key="mcp_session",
value=user_id,
max_age=86400 * 30, # 30 days
httponly=True,
secure=False, # Set to True in production with HTTPS
samesite="lax",
)
logger.info(f"Session cookie set for user: {username}")
return response
async def oauth_logout(request: Request) -> RedirectResponse:
"""Browser OAuth logout - clears session cookie.
Query parameters:
next: Optional URL to redirect to after logout (default: /oauth/login)
Returns:
302 redirect with cleared session cookie
"""
next_url = request.query_params.get("next", "/oauth/login")
# TODO: Optionally revoke refresh token from storage
# session_id = request.cookies.get("mcp_session")
# if session_id:
# await storage.delete_refresh_token(session_id)
response = RedirectResponse(next_url, status_code=302)
response.delete_cookie("mcp_session")
logger.info("User logged out, session cookie cleared")
return response
@@ -1,15 +1,16 @@
"""Dynamic client registration for Nextcloud OIDC."""
import datetime as dt
import json
import logging
import os
import time
from pathlib import Path
from typing import Any
import anyio
import httpx
from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
logger = logging.getLogger(__name__)
@@ -79,23 +80,18 @@ async def register_client(
client_name: str = "Nextcloud MCP Server",
redirect_uris: list[str] | None = None,
scopes: str = "openid profile email",
token_type: str | None = "Bearer",
resource_url: str | None = None,
token_type: str = "Bearer",
) -> ClientInfo:
"""
Register a new OAuth client using RFC 7591 Dynamic Client Registration.
This function supports both Nextcloud OIDC and standard OIDC providers like Keycloak.
Register a new OAuth client with Nextcloud OIDC using dynamic client registration.
Args:
nextcloud_url: Base URL of the OIDC provider
nextcloud_url: Base URL of the Nextcloud instance
registration_endpoint: Full URL to the registration endpoint
client_name: Name of the client application
redirect_uris: List of redirect URIs (default: http://localhost:8000/oauth/callback)
scopes: Space-separated list of scopes to request
token_type: Type of access tokens (default: "Bearer", supports "JWT" for Nextcloud).
Set to None to omit this field (required for Keycloak and other standard providers).
resource_url: OAuth 2.0 Protected Resource URL (RFC 9728) - used for token introspection authorization
token_type: Type of access tokens to issue (default: "Bearer", also supports "JWT")
Returns:
ClientInfo with registration details
@@ -103,11 +99,6 @@ async def register_client(
Raises:
httpx.HTTPStatusError: If registration fails
ValueError: If response is invalid
Note:
The token_type parameter is a Nextcloud-specific extension and is not part of RFC 7591.
Standard OIDC providers like Keycloak do not accept this field and will return a 400 error
if it's included. Set token_type=None when registering with Keycloak or other standard providers.
"""
if redirect_uris is None:
redirect_uris = ["http://localhost:8000/oauth/callback"]
@@ -119,16 +110,9 @@ async def register_client(
"grant_types": ["authorization_code", "refresh_token"],
"response_types": ["code"],
"scope": scopes,
"token_type": token_type,
}
# Add token_type if provided (Nextcloud-specific, not RFC 7591 standard)
if token_type is not None:
client_metadata["token_type"] = token_type
# Add resource_url if provided (RFC 9728)
if resource_url:
client_metadata["resource_url"] = resource_url
logger.info(f"Registering OAuth client with Nextcloud: {client_name}")
logger.debug(f"Registration endpoint: {registration_endpoint}")
@@ -186,6 +170,72 @@ async def register_client(
raise ValueError(f"Invalid registration response: missing {e}")
def load_client_from_file(storage_path: Path) -> ClientInfo | None:
"""
Load client credentials from storage file.
Args:
storage_path: Path to the JSON file containing client credentials
Returns:
ClientInfo if file exists and is valid, None otherwise
"""
if not storage_path.exists():
logger.debug(f"Client storage file not found: {storage_path}")
return None
try:
with open(storage_path, "r") as f:
data = json.load(f)
client_info = ClientInfo.from_dict(data)
if client_info.is_expired:
logger.warning(
f"Stored client has expired (expired at {client_info.client_secret_expires_at})"
)
return None
logger.info(f"Loaded client from storage: {client_info.client_id[:16]}...")
if client_info.expires_soon:
logger.warning("Client expires soon (within 5 minutes)")
return client_info
except (json.JSONDecodeError, KeyError, ValueError) as e:
logger.error(f"Failed to load client from file: {e}")
return None
def save_client_to_file(client_info: ClientInfo, storage_path: Path):
"""
Save client credentials to storage file.
Args:
client_info: Client information to save
storage_path: Path to save the JSON file
Raises:
OSError: If file cannot be written
"""
try:
# Create directory if it doesn't exist
storage_path.parent.mkdir(parents=True, exist_ok=True)
# Write client info
with open(storage_path, "w") as f:
json.dump(client_info.to_dict(), f, indent=2)
# Set restrictive permissions (owner read/write only)
os.chmod(storage_path, 0o600)
logger.info(f"Saved client credentials to {storage_path}")
except OSError as e:
logger.error(f"Failed to save client credentials: {e}")
raise
async def delete_client(
nextcloud_url: str,
client_id: str,
@@ -312,34 +362,32 @@ async def delete_client(
return False
async def ensure_oauth_client(
async def load_or_register_client(
nextcloud_url: str,
registration_endpoint: str,
storage: RefreshTokenStorage,
storage_path: str | Path,
client_name: str = "Nextcloud MCP Server",
redirect_uris: list[str] | None = None,
scopes: str = "openid profile email",
token_type: str = "Bearer",
resource_url: str | None = None,
) -> ClientInfo:
"""
Ensure OAuth client exists in SQLite storage.
Load client from storage or register a new one if not found/expired.
This function:
1. Checks for existing client credentials in SQLite storage
1. Checks for existing client credentials in storage
2. Validates the credentials are not expired
3. Registers a new client if needed (no stored credentials or expired)
4. Saves the new client credentials to SQLite
4. Saves the new client credentials
Args:
nextcloud_url: Base URL of the Nextcloud instance
registration_endpoint: Full URL to the registration endpoint
storage: RefreshTokenStorage instance for SQLite storage
storage_path: Path to store client credentials
client_name: Name of the client application
redirect_uris: List of redirect URIs
scopes: Space-separated list of scopes to request (default: "openid profile email")
token_type: Type of access tokens to issue (default: "Bearer", also supports "JWT")
resource_url: OAuth 2.0 Protected Resource URL (RFC 9728) - used for token introspection authorization
Returns:
ClientInfo with valid credentials
@@ -348,18 +396,15 @@ async def ensure_oauth_client(
httpx.HTTPStatusError: If registration fails
ValueError: If response is invalid
"""
# Try to load existing client from SQLite
client_data = await storage.get_oauth_client()
if client_data:
logger.info(
f"Loaded OAuth client from SQLite: {client_data['client_id'][:16]}..."
)
return ClientInfo.from_dict(client_data)
storage_path = Path(storage_path)
# Try to load existing client
client_info = load_client_from_file(storage_path)
if client_info:
return client_info
# Register new client
logger.info("Registering new OAuth client...")
if resource_url:
logger.info(f" with resource_url: {resource_url}")
client_info = await register_client(
nextcloud_url=nextcloud_url,
registration_endpoint=registration_endpoint,
@@ -367,18 +412,9 @@ async def ensure_oauth_client(
redirect_uris=redirect_uris,
scopes=scopes,
token_type=token_type,
resource_url=resource_url,
)
# Save to SQLite storage
await storage.store_oauth_client(
client_id=client_info.client_id,
client_secret=client_info.client_secret,
client_id_issued_at=client_info.client_id_issued_at,
client_secret_expires_at=client_info.client_secret_expires_at,
redirect_uris=client_info.redirect_uris,
registration_access_token=client_info.registration_access_token,
registration_client_uri=client_info.registration_client_uri,
)
# Save to storage
save_client_to_file(client_info, storage_path)
return client_info
@@ -1,239 +0,0 @@
"""
MCP Client Registry for ADR-004 Progressive Consent Architecture.
This module manages the registry of allowed MCP clients that can authenticate
via Flow 1. In production, this would integrate with Dynamic Client Registration
(DCR) or a database of pre-registered clients.
"""
import logging
import os
from dataclasses import dataclass
from typing import Dict, List, Optional
logger = logging.getLogger(__name__)
@dataclass
class MCPClientInfo:
"""Information about a registered MCP client."""
client_id: str
name: str
redirect_uris: List[str]
allowed_scopes: List[str]
is_public: bool = True # Native clients are public (no client_secret)
metadata: Optional[Dict] = None
class ClientRegistry:
"""
Registry for MCP clients allowed to authenticate via Flow 1.
In production, this would:
1. Support Dynamic Client Registration (DCR) per RFC 7591
2. Integrate with IdP client registry
3. Store client metadata in database
4. Support client updates and revocation
"""
def __init__(self, allow_dynamic_registration: bool = False):
"""
Initialize the client registry.
Args:
allow_dynamic_registration: Whether to allow DCR for new clients
"""
self.allow_dynamic_registration = allow_dynamic_registration
self._clients: Dict[str, MCPClientInfo] = {}
self._load_static_clients()
def _load_static_clients(self):
"""Load statically configured clients from environment."""
# Load from ALLOWED_MCP_CLIENTS environment variable
allowed_clients = os.getenv("ALLOWED_MCP_CLIENTS", "").strip()
if allowed_clients:
# Parse comma-separated list
for client_id in allowed_clients.split(","):
client_id = client_id.strip()
if client_id:
# Create basic client info
# In production, would load full metadata from database
self._clients[client_id] = MCPClientInfo(
client_id=client_id,
name=self._get_client_name(client_id),
redirect_uris=["http://localhost:*", "http://127.0.0.1:*"],
allowed_scopes=["openid", "profile", "email", "mcp-server:api"],
is_public=True,
)
logger.info(f"Registered static client: {client_id}")
# Add well-known clients if not explicitly configured
if not self._clients:
self._add_well_known_clients()
def _get_client_name(self, client_id: str) -> str:
"""Get human-readable name for client_id."""
known_names = {
"claude-desktop": "Claude Desktop",
"continue-dev": "Continue IDE Extension",
"zed-editor": "Zed Editor",
"vscode-mcp": "VS Code MCP Extension",
"test-mcp-client": "Test MCP Client",
}
return known_names.get(client_id, client_id.replace("-", " ").title())
def _add_well_known_clients(self):
"""Add well-known MCP clients for testing and development."""
well_known = [
MCPClientInfo(
client_id="claude-desktop",
name="Claude Desktop",
redirect_uris=["http://localhost:*", "http://127.0.0.1:*"],
allowed_scopes=["openid", "profile", "email", "mcp-server:api"],
is_public=True,
metadata={"vendor": "Anthropic"},
),
MCPClientInfo(
client_id="test-mcp-client",
name="Test MCP Client",
redirect_uris=["http://localhost:*", "http://127.0.0.1:*"],
allowed_scopes=["openid", "profile", "email", "mcp-server:api"],
is_public=True,
metadata={"purpose": "testing"},
),
]
for client in well_known:
self._clients[client.client_id] = client
logger.info(f"Registered well-known client: {client.client_id}")
def validate_client(
self,
client_id: str,
redirect_uri: Optional[str] = None,
scopes: Optional[List[str]] = None,
) -> tuple[bool, Optional[str]]:
"""
Validate a client_id and optionally its redirect_uri and scopes.
Args:
client_id: The client identifier to validate
redirect_uri: Optional redirect URI to validate
scopes: Optional list of scopes to validate
Returns:
Tuple of (is_valid, error_message)
"""
# Check if client exists
client = self._clients.get(client_id)
if not client:
if self.allow_dynamic_registration:
# In production, would attempt DCR here
logger.info(f"Unknown client {client_id}, would attempt DCR")
return True, None
else:
return False, f"Unknown client_id: {client_id}"
# Validate redirect_uri if provided
if redirect_uri:
if not self._validate_redirect_uri(client, redirect_uri):
return False, f"Invalid redirect_uri for client {client_id}"
# Validate scopes if provided
if scopes:
invalid_scopes = set(scopes) - set(client.allowed_scopes)
if invalid_scopes:
return False, f"Invalid scopes for client {client_id}: {invalid_scopes}"
return True, None
def _validate_redirect_uri(self, client: MCPClientInfo, redirect_uri: str) -> bool:
"""
Validate redirect_uri against client's registered URIs.
Args:
client: The client info
redirect_uri: The URI to validate
Returns:
True if valid, False otherwise
"""
# Parse the redirect URI
from urllib.parse import urlparse
parsed = urlparse(redirect_uri)
# Check against registered patterns
for pattern in client.redirect_uris:
if "*" in pattern:
# Handle wildcard port (localhost:*)
pattern_base = pattern.replace(":*", "")
if redirect_uri.startswith(pattern_base + ":"):
# Validate it's localhost with a port
if parsed.hostname in ["localhost", "127.0.0.1"]:
return True
elif redirect_uri == pattern:
return True
return False
def register_client(self, client_info: MCPClientInfo) -> bool:
"""
Register a new MCP client (DCR support).
Args:
client_info: Client information to register
Returns:
True if registered successfully
"""
if not self.allow_dynamic_registration:
logger.warning(f"DCR disabled, cannot register {client_info.client_id}")
return False
if client_info.client_id in self._clients:
logger.warning(f"Client {client_info.client_id} already registered")
return False
self._clients[client_info.client_id] = client_info
logger.info(f"Dynamically registered client: {client_info.client_id}")
# In production, would persist to database
return True
def get_client(self, client_id: str) -> Optional[MCPClientInfo]:
"""
Get client information.
Args:
client_id: The client identifier
Returns:
Client info if found, None otherwise
"""
return self._clients.get(client_id)
def list_clients(self) -> List[MCPClientInfo]:
"""
List all registered clients.
Returns:
List of client information
"""
return list(self._clients.values())
# Global registry instance
_registry: Optional[ClientRegistry] = None
def get_client_registry() -> ClientRegistry:
"""Get the global client registry instance."""
global _registry
if _registry is None:
# Check if DCR is enabled
allow_dcr = os.getenv("ENABLE_DCR", "false").lower() == "true"
_registry = ClientRegistry(allow_dynamic_registration=allow_dcr)
return _registry
+13 -157
View File
@@ -1,55 +1,43 @@
"""Helper functions for extracting OAuth context from MCP requests.
"""Helper functions for extracting OAuth context from MCP requests."""
ADR-005 compliant implementation with token exchange caching.
"""
import hashlib
import logging
import time
from mcp.server.auth.provider import AccessToken
from mcp.server.fastmcp import Context
from ..client import NextcloudClient
from ..config import get_settings
from ..observability.metrics import (
oauth_token_cache_hits_total,
oauth_token_exchange_total,
)
from .token_exchange import exchange_token_for_audience
logger = logging.getLogger(__name__)
# Token exchange cache: token_hash -> (exchanged_token, expiry_timestamp)
_exchange_cache: dict[str, tuple[str, float]] = {}
def get_client_from_context(ctx: Context, base_url: str) -> NextcloudClient:
"""
Create NextcloudClient for multi-audience mode (no exchange needed).
Extract authenticated user context from MCP request and create NextcloudClient.
ADR-005 Mode 1: Use multi-audience tokens directly.
The UnifiedTokenVerifier validated MCP audience per RFC 7519.
Nextcloud will independently validate its own audience.
This function retrieves the OAuth access token from the MCP context,
extracts the username from the token's resource field (where we stored it
during token verification), and creates a NextcloudClient with bearer auth.
Args:
ctx: MCP request context containing session info
base_url: Nextcloud base URL
Returns:
NextcloudClient configured with multi-audience token
NextcloudClient configured with bearer token auth
Raises:
AttributeError: If context doesn't contain expected OAuth session data
ValueError: If username cannot be extracted from token
"""
try:
# Extract validated access token from MCP context
# In Starlette with FastMCP OAuth, the authenticated user info is stored in request.user
# The FastMCP auth middleware sets request.user to an AuthenticatedUser object
# which contains the access_token
if hasattr(ctx.request_context.request, "user") and hasattr(
ctx.request_context.request.user, "access_token"
):
access_token: AccessToken = ctx.request_context.request.user.access_token
logger.debug("Retrieved multi-audience token from request.user")
logger.debug("Retrieved access token from request.user for OAuth request")
else:
logger.error(
"OAuth authentication failed: No access token found in request"
@@ -57,20 +45,16 @@ def get_client_from_context(ctx: Context, base_url: str) -> NextcloudClient:
raise AttributeError("No access token found in OAuth request context")
# Extract username from resource field (RFC 8707)
# UnifiedTokenVerifier stored the username here during validation
# We stored the username here during token verification
username = access_token.resource
if not username:
logger.error("No username found in access token resource field")
raise ValueError("Username not available in OAuth token context")
logger.debug(
f"Creating NextcloudClient for user {username} with multi-audience token "
f"(no exchange needed)"
)
logger.debug(f"Creating OAuth NextcloudClient for user: {username}")
# Token was validated to have MCP audience
# Nextcloud will validate its own audience independently
# Create client with bearer token
return NextcloudClient.from_token(
base_url=base_url, token=access_token.token, username=username
)
@@ -79,131 +63,3 @@ def get_client_from_context(ctx: Context, base_url: str) -> NextcloudClient:
logger.error(f"Failed to extract OAuth context: {e}")
logger.error("This may indicate the server is not running in OAuth mode")
raise
async def get_session_client_from_context(
ctx: Context, base_url: str
) -> NextcloudClient:
"""
Create NextcloudClient using RFC 8693 token exchange with caching.
ADR-005 Mode 2: Exchange MCP token for Nextcloud token via RFC 8693.
This implements the token exchange pattern where:
1. Extract MCP token from context (validated by UnifiedTokenVerifier)
2. Check cache for existing exchanged token
3. If not cached or expired, exchange via RFC 8693
4. Cache the exchanged token to minimize exchange frequency
5. Create client with exchanged token
CRITICAL: This is where token exchange happens, NOT in the verifier.
The verifier already validated the MCP audience; now we exchange for Nextcloud.
Note: Nextcloud doesn't support OAuth scopes natively. Scopes are enforced
by the MCP server via @require_scopes decorator, not by the IdP. Therefore,
we don't pass scopes to the token exchange - the MCP server already validated
permissions before calling this function.
Args:
ctx: MCP request context containing session info
base_url: Nextcloud base URL
Returns:
NextcloudClient configured with ephemeral exchanged token
Raises:
AttributeError: If context doesn't contain expected OAuth session data
RuntimeError: If token exchange fails
"""
settings = get_settings()
try:
# Extract MCP token from context
if hasattr(ctx.request_context.request, "user") and hasattr(
ctx.request_context.request.user, "access_token"
):
access_token: AccessToken = ctx.request_context.request.user.access_token
mcp_token = access_token.token
username = access_token.resource # Username from UnifiedTokenVerifier
logger.debug(f"Retrieved MCP token for user: {username}")
else:
logger.error("No MCP token found in request context")
raise AttributeError("No access token found in OAuth request context")
if not username:
logger.error("No username found in access token resource field")
raise ValueError("Username not available in OAuth token context")
# Check cache for existing exchanged token
cache_key = hashlib.sha256(mcp_token.encode()).hexdigest()
if cache_key in _exchange_cache:
cached_token, expiry = _exchange_cache[cache_key]
if time.time() < expiry:
logger.debug(
f"Using cached exchanged token (expires in {expiry - time.time():.1f}s)"
)
oauth_token_cache_hits_total.labels(hit="true").inc()
return NextcloudClient.from_token(
base_url=base_url, token=cached_token, username=username
)
else:
logger.debug("Cached token expired, removing from cache")
del _exchange_cache[cache_key]
oauth_token_cache_hits_total.labels(hit="false").inc()
# Perform RFC 8693 token exchange
logger.info(f"Exchanging MCP token for Nextcloud API token (user: {username})")
try:
# Exchange for Nextcloud resource URI audience
exchanged_token, expires_in = await exchange_token_for_audience(
subject_token=mcp_token,
requested_audience=settings.nextcloud_resource_uri or "nextcloud",
requested_scopes=None, # Nextcloud doesn't support scopes
)
oauth_token_exchange_total.labels(status="success").inc()
logger.info(f"Token exchange successful. Token expires in {expires_in}s")
except Exception:
oauth_token_exchange_total.labels(status="error").inc()
raise
# Cache the exchanged token
# Use the minimum of exchange TTL and configured cache TTL
cache_ttl = min(expires_in, settings.token_exchange_cache_ttl)
_exchange_cache[cache_key] = (exchanged_token, time.time() + cache_ttl)
logger.debug(f"Cached exchanged token for {cache_ttl}s")
# Clean up expired cache entries
_cleanup_exchange_cache()
# Create client with exchanged token
return NextcloudClient.from_token(
base_url=base_url, token=exchanged_token, username=username
)
except AttributeError as e:
logger.error(f"Failed to extract OAuth context: {e}")
raise
except Exception as e:
logger.error(f"Token exchange failed: {e}")
raise RuntimeError(f"Token exchange required but failed: {e}") from e
def _cleanup_exchange_cache():
"""Remove expired entries from the token exchange cache."""
global _exchange_cache
now = time.time()
expired_keys = [k for k, (_, expiry) in _exchange_cache.items() if expiry <= now]
for key in expired_keys:
del _exchange_cache[key]
if expired_keys:
logger.debug(f"Cleaned up {len(expired_keys)} expired cache entries")
def clear_exchange_cache():
"""Clear the entire token exchange cache. Useful for testing."""
global _exchange_cache
_exchange_cache.clear()
logger.debug("Token exchange cache cleared")
-583
View File
@@ -1,583 +0,0 @@
"""
Keycloak OAuth 2.0 / OIDC Client
Handles OAuth flows with Keycloak as the identity provider, including:
- OIDC Discovery
- Authorization Code Flow with PKCE
- Token refresh using refresh tokens (ADR-002 Tier 1)
- Integration with RefreshTokenStorage
"""
import hashlib
import logging
import os
import secrets
from typing import Optional
from urllib.parse import urlencode, urlparse
import httpx
logger = logging.getLogger(__name__)
class KeycloakOAuthClient:
"""OAuth 2.0 client for Keycloak integration"""
def __init__(
self,
keycloak_url: str,
realm: str,
client_id: str,
client_secret: str,
redirect_uri: str,
scopes: Optional[list[str]] = None,
):
"""
Initialize Keycloak OAuth client.
Args:
keycloak_url: Base URL of Keycloak (e.g., http://keycloak:8080)
realm: Keycloak realm name
client_id: OAuth client ID
client_secret: OAuth client secret
redirect_uri: OAuth redirect URI
scopes: List of scopes to request (default: openid, profile, email, offline_access)
"""
self.keycloak_url = keycloak_url.rstrip("/")
self.realm = realm
self.client_id = client_id
self.client_secret = client_secret
self.redirect_uri = redirect_uri
self.scopes = scopes or ["openid", "profile", "email", "offline_access"]
# Discovered endpoints (populated by discover())
self.authorization_endpoint: Optional[str] = None
self.token_endpoint: Optional[str] = None
self.userinfo_endpoint: Optional[str] = None
self.jwks_uri: Optional[str] = None
self.end_session_endpoint: Optional[str] = None
self._http_client: Optional[httpx.AsyncClient] = None
@classmethod
def from_env(cls) -> "KeycloakOAuthClient":
"""
Create client from environment variables.
Environment variables:
KEYCLOAK_URL: Keycloak base URL
KEYCLOAK_REALM: Realm name
KEYCLOAK_CLIENT_ID: Client ID
KEYCLOAK_CLIENT_SECRET: Client secret
NEXTCLOUD_MCP_SERVER_URL: MCP server URL (for redirect URI)
Returns:
KeycloakOAuthClient instance
Raises:
ValueError: If required environment variables are missing
"""
keycloak_url = os.getenv("KEYCLOAK_URL")
realm = os.getenv("KEYCLOAK_REALM")
client_id = os.getenv("KEYCLOAK_CLIENT_ID")
client_secret = os.getenv("KEYCLOAK_CLIENT_SECRET")
server_url = os.getenv("NEXTCLOUD_MCP_SERVER_URL", "http://localhost:8000")
if not all([keycloak_url, realm, client_id, client_secret]):
raise ValueError(
"Missing required environment variables: "
"KEYCLOAK_URL, KEYCLOAK_REALM, KEYCLOAK_CLIENT_ID, KEYCLOAK_CLIENT_SECRET"
)
# Parse server URL to construct redirect URI
# Note: This is for OAuth client initialization, not used for actual redirects
# since this client is used for backend token operations (exchange, refresh)
parsed_url = urlparse(server_url)
redirect_uri = f"{parsed_url.scheme}://{parsed_url.netloc}/oauth/callback"
return cls(
keycloak_url=keycloak_url,
realm=realm,
client_id=client_id,
client_secret=client_secret,
redirect_uri=redirect_uri,
)
async def _get_http_client(self) -> httpx.AsyncClient:
"""Get or create HTTP client"""
if self._http_client is None:
self._http_client = httpx.AsyncClient(timeout=30.0)
return self._http_client
async def close(self) -> None:
"""Close HTTP client"""
if self._http_client:
await self._http_client.aclose()
self._http_client = None
async def discover(self) -> None:
"""
Perform OIDC discovery to get endpoint URLs.
Raises:
httpx.HTTPError: If discovery fails
"""
discovery_url = (
f"{self.keycloak_url}/realms/{self.realm}/.well-known/openid-configuration"
)
logger.info(f"Discovering Keycloak endpoints at {discovery_url}")
client = await self._get_http_client()
response = await client.get(discovery_url)
response.raise_for_status()
discovery_data = response.json()
self.authorization_endpoint = discovery_data["authorization_endpoint"]
self.token_endpoint = discovery_data["token_endpoint"]
self.userinfo_endpoint = discovery_data["userinfo_endpoint"]
self.jwks_uri = discovery_data.get("jwks_uri")
self.end_session_endpoint = discovery_data.get("end_session_endpoint")
logger.info(
f"✓ Discovered Keycloak endpoints:\n"
f" Authorization: {self.authorization_endpoint}\n"
f" Token: {self.token_endpoint}\n"
f" Userinfo: {self.userinfo_endpoint}\n"
f" JWKS: {self.jwks_uri}"
)
def generate_pkce_challenge(self) -> tuple[str, str]:
"""
Generate PKCE code verifier and challenge.
Returns:
Tuple of (code_verifier, code_challenge)
"""
import base64
# Generate code verifier (43-128 characters)
code_verifier = secrets.token_urlsafe(32)
# Generate code challenge using S256 method (base64url-encoded SHA256)
digest = hashlib.sha256(code_verifier.encode()).digest()
code_challenge = base64.urlsafe_b64encode(digest).decode().rstrip("=")
return code_verifier, code_challenge
async def get_authorization_url(
self,
state: str,
code_challenge: str,
extra_params: Optional[dict[str, str]] = None,
) -> str:
"""
Build authorization URL for OAuth flow.
Args:
state: CSRF protection state parameter
code_challenge: PKCE code challenge
extra_params: Additional query parameters
Returns:
Authorization URL
Raises:
RuntimeError: If discover() hasn't been called
"""
if not self.authorization_endpoint:
await self.discover()
if not self.authorization_endpoint:
raise RuntimeError("Authorization endpoint not discovered")
params = {
"client_id": self.client_id,
"response_type": "code",
"redirect_uri": self.redirect_uri,
"scope": " ".join(self.scopes),
"state": state,
"code_challenge": code_challenge,
"code_challenge_method": "S256",
}
if extra_params:
params.update(extra_params)
return f"{self.authorization_endpoint}?{urlencode(params)}"
async def exchange_authorization_code(
self,
code: str,
code_verifier: str,
) -> dict:
"""
Exchange authorization code for tokens.
Args:
code: Authorization code from OAuth callback
code_verifier: PKCE code verifier
Returns:
Token response dictionary with keys:
- access_token: Access token
- refresh_token: Refresh token (if offline_access scope requested)
- id_token: ID token (JWT)
- expires_in: Access token lifetime in seconds
- refresh_expires_in: Refresh token lifetime in seconds (optional)
- token_type: Token type (Bearer)
Raises:
httpx.HTTPError: If token exchange fails
"""
if not self.token_endpoint:
await self.discover()
if not self.token_endpoint:
raise RuntimeError("Token endpoint not discovered")
logger.debug(
f"Exchanging authorization code for tokens at {self.token_endpoint}"
)
client = await self._get_http_client()
response = await client.post(
self.token_endpoint,
data={
"grant_type": "authorization_code",
"code": code,
"redirect_uri": self.redirect_uri,
"code_verifier": code_verifier,
},
auth=(self.client_id, self.client_secret),
)
response.raise_for_status()
token_data = response.json()
logger.info("✓ Successfully exchanged authorization code for tokens")
if "refresh_token" in token_data:
logger.info(" Received refresh token (offline_access granted)")
return token_data
async def refresh_access_token(self, refresh_token: str) -> dict:
"""
Refresh access token using refresh token.
Args:
refresh_token: Refresh token
Returns:
Token response dictionary (same format as exchange_authorization_code)
Raises:
httpx.HTTPError: If token refresh fails
"""
if not self.token_endpoint:
await self.discover()
if not self.token_endpoint:
raise RuntimeError("Token endpoint not discovered")
logger.debug("Refreshing access token")
client = await self._get_http_client()
response = await client.post(
self.token_endpoint,
data={
"grant_type": "refresh_token",
"refresh_token": refresh_token,
},
auth=(self.client_id, self.client_secret),
)
response.raise_for_status()
token_data = response.json()
logger.debug("✓ Successfully refreshed access token")
return token_data
async def get_userinfo(self, access_token: str) -> dict:
"""
Get user information using access token.
Args:
access_token: Access token
Returns:
Userinfo response dictionary with claims like:
- sub: Subject (user ID)
- name: Full name
- preferred_username: Username
- email: Email address
- email_verified: Email verification status
Raises:
httpx.HTTPError: If userinfo request fails
"""
if not self.userinfo_endpoint:
await self.discover()
if not self.userinfo_endpoint:
raise RuntimeError("Userinfo endpoint not discovered")
logger.debug("Fetching user info")
client = await self._get_http_client()
response = await client.get(
self.userinfo_endpoint,
headers={"Authorization": f"Bearer {access_token}"},
)
response.raise_for_status()
userinfo = response.json()
logger.debug(f"✓ Retrieved user info for subject: {userinfo.get('sub')}")
return userinfo
async def get_service_account_token(self, scopes: list[str] | None = None) -> dict:
"""
Get a service account token using client_credentials grant.
**WARNING: DO NOT USE FOR DIRECT API ACCESS IN OAUTH MODE**
This method creates a service account user in Nextcloud which VIOLATES
OAuth "act on-behalf-of" principles. Using this token directly for API
access will:
- Create a Nextcloud user: `service-account-{client_id}`
- Attribute all actions to service account instead of real user
- Break audit trail and user attribution
- Create stateful server identity in Nextcloud
- Violate OAuth security model
**Valid Use Case**: ONLY as subject_token for RFC 8693 token exchange
(ADR-002 Tier 2) where it's immediately exchanged for a user token.
**Invalid Use Case**: Direct API access with this token (ADR-002 rejected
this as "Tier 1" - see docs/ADR-002-vector-sync-authentication.md).
**Alternative**: Use token exchange (impersonation/delegation) for
background operations, or use BasicAuth mode if truly need service account.
This requires the client to have serviceAccountsEnabled=true in provider.
Args:
scopes: Optional list of scopes to request (default: openid profile email)
Returns:
Token response dictionary with:
- access_token: Service account access token
- token_type: Bearer
- expires_in: Token lifetime in seconds
- scope: Granted scopes
Raises:
httpx.HTTPError: If token request fails
See Also:
- ADR-002 "Will Not Implement" section for detailed critique
- exchange_token_for_user() for proper token exchange usage
"""
if not self.token_endpoint:
await self.discover()
if not self.token_endpoint:
raise RuntimeError("Token endpoint not discovered")
# Default scopes
if scopes is None:
scopes = ["openid", "profile", "email"]
scope_str = " ".join(scopes)
logger.info(f"Requesting service account token with scopes: {scope_str}")
client = await self._get_http_client()
response = await client.post(
self.token_endpoint,
data={
"grant_type": "client_credentials",
"scope": scope_str,
},
auth=(self.client_id, self.client_secret),
)
response.raise_for_status()
token_data = response.json()
logger.info("✓ Service account token acquired")
return token_data
async def exchange_token_for_user(
self,
subject_token: str,
target_user_id: str | None = None,
audience: str | None = None,
scopes: list[str] | None = None,
) -> dict:
"""
Exchange a token for a user-scoped token using RFC 8693 Token Exchange.
This allows the MCP server (with a service account token) to obtain
user-scoped access tokens for background operations without needing
refresh tokens.
Args:
subject_token: The token being exchanged (service account or user token)
target_user_id: Optional user ID to impersonate/exchange for
audience: Optional target audience (client ID)
scopes: Optional list of scopes for the new token
Returns:
Token response dictionary with:
- access_token: User-scoped access token
- issued_token_type: urn:ietf:params:oauth:token-type:access_token
- token_type: Bearer
- expires_in: Token lifetime in seconds
Raises:
httpx.HTTPError: If token exchange fails (403 if not authorized)
Example:
# Get service account token
service_token = await client.get_service_account_token()
# Exchange for user-scoped token
user_token = await client.exchange_token_for_user(
subject_token=service_token["access_token"],
target_user_id="admin", # Username or sub claim
audience="nextcloud",
scopes=["notes:read", "files:read"]
)
Note:
This implements BOTH ADR-002 tiers:
**Tier 2 (Delegation - Recommended)**: When target_user_id is None
- Uses Keycloak Standard V2 (production-ready)
- Service account maintains its identity (sub claim unchanged)
- No special permissions required
**Tier 1 (Impersonation - Advanced)**: When target_user_id is provided
- Requires Keycloak Legacy V1 (--features=preview)
- Subject claim changes to target user
- Requires impersonation role granted via Keycloak CLI:
```
kcadm.sh add-roles -r <realm> \
--uusername service-account-<client-id> \
--cclientid realm-management \
--rolename impersonation
```
Both tiers require:
- Client has token.exchange.grant.enabled=true
- Client has serviceAccountsEnabled=true
"""
if not self.token_endpoint:
await self.discover()
if not self.token_endpoint:
raise RuntimeError("Token endpoint not discovered")
# Build token exchange request
data = {
"grant_type": "urn:ietf:params:oauth:grant-type:token-exchange",
"subject_token": subject_token,
"subject_token_type": "urn:ietf:params:oauth:token-type:access_token",
"requested_token_type": "urn:ietf:params:oauth:token-type:access_token",
}
# Add optional parameters
if audience:
data["audience"] = audience
if scopes:
data["scope"] = " ".join(scopes)
if target_user_id:
# Tier 1: Impersonation (Legacy V1)
# Use requested_subject for user impersonation
data["requested_subject"] = target_user_id
logger.info(
f"Exchanging token with impersonation (Tier 1): target_user={target_user_id}"
)
else:
# Tier 2: Delegation (Standard V2)
logger.info(
"Exchanging token with delegation (Tier 2): service account identity preserved"
)
client = await self._get_http_client()
response = await client.post(
self.token_endpoint,
data=data,
auth=(self.client_id, self.client_secret),
)
if response.status_code != 200:
error_data = (
response.json()
if response.headers.get("content-type", "").startswith(
"application/json"
)
else {"error": "unknown"}
)
logger.error(f"Token exchange failed: {response.status_code}")
logger.error(f"Error response: {error_data}")
response.raise_for_status()
token_data = response.json()
logger.info(
f"✓ Token exchange successful, issued_token_type: {token_data.get('issued_token_type')}"
)
return token_data
async def check_token_exchange_support(self) -> bool:
"""
Check if Keycloak supports RFC 8693 token exchange.
Returns:
True if token exchange is supported
Note:
This is ADR-002 Tier 2. Most Keycloak installations don't
have token exchange enabled by default.
"""
if not self.token_endpoint:
await self.discover()
# Try to get discovery document and check for token exchange grant
discovery_url = (
f"{self.keycloak_url}/realms/{self.realm}/.well-known/openid-configuration"
)
try:
client = await self._get_http_client()
response = await client.get(discovery_url)
response.raise_for_status()
discovery_data = response.json()
grant_types = discovery_data.get("grant_types_supported", [])
supported = "urn:ietf:params:oauth:grant-type:token-exchange" in grant_types
if supported:
logger.info("✓ Token exchange (RFC 8693) is supported")
else:
logger.info("Token exchange (RFC 8693) is not supported")
return supported
except Exception as e:
logger.warning(f"Failed to check token exchange support: {e}")
return False
__all__ = ["KeycloakOAuthClient"]
-640
View File
@@ -1,640 +0,0 @@
"""
OAuth 2.0 Login Routes for ADR-004 (Offline Access Architecture)
Implements dual OAuth flows with optional offline access provisioning:
Flow 1: Client Authentication - MCP client authenticates directly to IdP
- Client requests: Nextcloud MCP resource scopes (notes:*, calendar:*, etc.)
- Token audience (aud): "mcp-server"
- No server interception - IdP redirects directly to client
- Client receives resource-scoped token for MCP session
Flow 2: Resource Provisioning - MCP server gets delegated Nextcloud access
- Triggered by user calling provision_nextcloud_access tool
- Server requests: openid, profile, email scopes, offline_access
- Separate login flow outside MCP session, results in browser login for user
- Token audience (aud): "nextcloud", redirect/callback to mcp server
- Server receives refresh token for offline access
- Client never sees this token
"""
import hashlib
import logging
import os
import secrets
from base64 import urlsafe_b64encode
from urllib.parse import urlencode
import httpx
import jwt
from starlette.requests import Request
from starlette.responses import JSONResponse, RedirectResponse
from nextcloud_mcp_server.auth.client_registry import get_client_registry
from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
logger = logging.getLogger(__name__)
async def oauth_authorize(request: Request) -> RedirectResponse | JSONResponse:
"""
OAuth authorization endpoint for Flow 1: Client Authentication.
The client authenticates directly to the IdP with its own client_id.
The server validates the client is authorized but does NOT intercept the callback.
IdP redirects directly back to the client's redirect_uri.
Query parameters:
response_type: Must be "code"
client_id: MCP client identifier (required)
redirect_uri: Client's localhost redirect URI (required)
scope: Requested scopes (optional, defaults to "openid profile email")
state: CSRF protection state (required)
code_challenge: PKCE code challenge from client (required)
code_challenge_method: PKCE method, must be "S256" (required)
Returns:
302 redirect to IdP authorization endpoint
"""
# Extract parameters
response_type = request.query_params.get("response_type")
client_id = request.query_params.get("client_id")
redirect_uri = request.query_params.get("redirect_uri")
state = request.query_params.get("state")
code_challenge = request.query_params.get("code_challenge")
code_challenge_method = request.query_params.get("code_challenge_method", "S256")
# Validate required parameters
if response_type != "code":
return JSONResponse(
{
"error": "unsupported_response_type",
"error_description": "Only 'code' response_type is supported",
},
status_code=400,
)
if not redirect_uri:
return JSONResponse(
{
"error": "invalid_request",
"error_description": "redirect_uri is required",
},
status_code=400,
)
# Validate redirect_uri is localhost (RFC 8252 for native clients)
if not redirect_uri.startswith(("http://localhost:", "http://127.0.0.1:")):
return JSONResponse(
{
"error": "invalid_request",
"error_description": "redirect_uri must be localhost for native clients",
},
status_code=400,
)
if not state:
return JSONResponse(
{
"error": "invalid_request",
"error_description": "state parameter is required for CSRF protection",
},
status_code=400,
)
if not code_challenge:
return JSONResponse(
{
"error": "invalid_request",
"error_description": "code_challenge is required (PKCE)",
},
status_code=400,
)
if code_challenge_method != "S256":
return JSONResponse(
{
"error": "invalid_request",
"error_description": "code_challenge_method must be S256",
},
status_code=400,
)
# Validate client_id (required for Flow 1)
if not client_id:
return JSONResponse(
{
"error": "invalid_request",
"error_description": "client_id is required",
},
status_code=400,
)
# Validate client using registry
registry = get_client_registry()
is_valid, error_msg = registry.validate_client(
client_id=client_id,
redirect_uri=redirect_uri,
scopes=request.query_params.get("scope", "").split()
if request.query_params.get("scope")
else None,
)
if not is_valid:
logger.warning(f"Client validation failed: {error_msg}")
return JSONResponse(
{
"error": "unauthorized_client",
"error_description": error_msg,
},
status_code=401,
)
# Get OAuth context from app state
oauth_ctx = request.app.state.oauth_context
if not oauth_ctx:
return JSONResponse(
{
"error": "server_error",
"error_description": "OAuth not configured on server",
},
status_code=500,
)
oauth_client = oauth_ctx["oauth_client"]
oauth_config = oauth_ctx["config"]
# Flow 1: Client authenticates directly to IdP WITHOUT server interception
# CRITICAL: This is a direct pass-through to IdP
# The IdP will redirect directly back to the client's callback
# The MCP server does NOT see the IdP authorization code!
logger.info(
f"Starting Flow 1 - no server session needed, "
f"client will handle IdP response directly at {redirect_uri}"
)
# Use client's redirect_uri for DIRECT callback (bypasses server)
callback_uri = redirect_uri
# Request resource scopes for MCP tools access
# The token will have aud: "mcp-server" claim
# Build scopes from NEXTCLOUD_OIDC_SCOPES config
default_scopes = "openid profile email"
resource_scopes = oauth_config.get("scopes", "")
scopes = f"{default_scopes} {resource_scopes}".strip()
# Pass through client's state directly
idp_state = state
# Use client's own client_id (client must be pre-registered at IdP)
idp_client_id = client_id
logger.info("Flow 1: Direct client auth to IdP")
logger.info(f" Client ID: {client_id}")
logger.info(f" Client will receive IdP code directly at: {callback_uri}")
logger.info(f" Scopes: {scopes} (resource access for MCP tools)")
# Get authorization endpoint from OAuth client
if oauth_client:
# External IdP mode (Keycloak) - use oauth_client
auth_url = await oauth_client.get_authorization_url(
state=idp_state,
code_challenge="", # Server doesn't use PKCE with IdP
)
logger.info(f"Redirecting to external IdP: {auth_url.split('?')[0]}")
else:
# Integrated mode (Nextcloud OIDC) - build URL directly
discovery_url = oauth_config.get("discovery_url")
if not discovery_url:
return JSONResponse(
{
"error": "server_error",
"error_description": "OAuth discovery URL not configured",
},
status_code=500,
)
# Fetch authorization endpoint from discovery
async with httpx.AsyncClient() as http_client:
response = await http_client.get(discovery_url)
response.raise_for_status()
discovery = response.json()
authorization_endpoint = discovery["authorization_endpoint"]
# IMPORTANT: Replace internal Docker hostname with public URL for browser access
# The discovery endpoint returns http://app/apps/oidc/authorize (internal)
# But browsers need http://localhost:8080/apps/oidc/authorize (public)
from urllib.parse import urlparse as parse_url
public_issuer = os.getenv("NEXTCLOUD_PUBLIC_ISSUER_URL")
if public_issuer:
# Parse internal and authorization endpoint to compare hostnames
internal_parsed = parse_url(oauth_config["nextcloud_host"])
auth_parsed = parse_url(authorization_endpoint)
# Check if authorization endpoint uses internal hostname
if auth_parsed.hostname == internal_parsed.hostname:
# Replace internal hostname+port with public URL
# Keep the path from authorization_endpoint
public_parsed = parse_url(public_issuer)
authorization_endpoint = (
f"{public_parsed.scheme}://{public_parsed.netloc}{auth_parsed.path}"
)
if auth_parsed.query:
authorization_endpoint += f"?{auth_parsed.query}"
logger.info(
f"Rewrote authorization endpoint for browser access: {authorization_endpoint}"
)
idp_params = {
"client_id": idp_client_id,
"redirect_uri": callback_uri,
"response_type": "code",
"scope": scopes,
"state": idp_state,
"prompt": "consent", # Ensure refresh token
"resource": f"{oauth_config['mcp_server_url']}/mcp", # MCP server audience
}
auth_url = f"{authorization_endpoint}?{urlencode(idp_params)}"
logger.info(f"Redirecting to Nextcloud OIDC: {auth_url.split('?')[0]}")
return RedirectResponse(auth_url, status_code=302)
async def oauth_authorize_nextcloud(
request: Request,
) -> RedirectResponse | JSONResponse:
"""
OAuth authorization endpoint for Flow 2: Resource Provisioning.
This endpoint is used by the provision_nextcloud_access MCP tool
to initiate delegated resource access to Nextcloud. Requires a separate
login flow outside of the MCP session.
Query parameters:
state: Session state for tracking
Returns:
302 redirect to IdP authorization endpoint
"""
state = request.query_params.get("state")
if not state:
return JSONResponse(
{
"error": "invalid_request",
"error_description": "state parameter is required",
},
status_code=400,
)
# Get OAuth context
oauth_ctx = request.app.state.oauth_context
if not oauth_ctx:
return JSONResponse(
{
"error": "server_error",
"error_description": "OAuth not configured on server",
},
status_code=500,
)
oauth_config = oauth_ctx["config"]
# Get MCP server's OAuth client credentials
mcp_server_client_id = os.getenv(
"MCP_SERVER_CLIENT_ID", oauth_config.get("client_id")
)
if not mcp_server_client_id:
return JSONResponse(
{
"error": "server_error",
"error_description": "MCP server OAuth client not configured",
},
status_code=500,
)
mcp_server_url = oauth_config["mcp_server_url"]
callback_uri = f"{mcp_server_url}/oauth/callback"
# Flow 2: Server only needs identity + offline access (no resource scopes)
# Resource scopes are requested by client in Flow 1
scopes = "openid profile email offline_access"
# Generate PKCE values (required by Nextcloud OIDC)
code_verifier = secrets.token_urlsafe(32)
digest = hashlib.sha256(code_verifier.encode()).digest()
code_challenge = urlsafe_b64encode(digest).decode().rstrip("=")
# Store code_verifier in session for retrieval during callback
storage = oauth_ctx["storage"]
await storage.store_oauth_session(
session_id=state,
client_id=mcp_server_client_id,
client_redirect_uri=callback_uri,
state=state,
code_challenge=code_challenge,
code_challenge_method="S256",
mcp_authorization_code=code_verifier, # Store code_verifier here temporarily
flow_type="flow2",
ttl_seconds=600, # 10 minutes
)
# Get authorization endpoint
discovery_url = oauth_config.get("discovery_url")
if not discovery_url:
return JSONResponse(
{
"error": "server_error",
"error_description": "OAuth discovery URL not configured",
},
status_code=500,
)
async with httpx.AsyncClient() as http_client:
response = await http_client.get(discovery_url)
response.raise_for_status()
discovery = response.json()
authorization_endpoint = discovery["authorization_endpoint"]
# Fix internal hostname for browser access
public_issuer = os.getenv("NEXTCLOUD_PUBLIC_ISSUER_URL")
if public_issuer:
from urllib.parse import urlparse as parse_url
internal_parsed = parse_url(oauth_config["nextcloud_host"])
auth_parsed = parse_url(authorization_endpoint)
if auth_parsed.hostname == internal_parsed.hostname:
public_parsed = parse_url(public_issuer)
authorization_endpoint = (
f"{public_parsed.scheme}://{public_parsed.netloc}{auth_parsed.path}"
)
# Build authorization URL
idp_params = {
"client_id": mcp_server_client_id,
"redirect_uri": callback_uri,
"response_type": "code",
"scope": scopes,
"state": state,
"code_challenge": code_challenge,
"code_challenge_method": "S256",
"prompt": "consent", # Force consent to show resource access
"access_type": "offline", # Request refresh token
"resource": oauth_config["nextcloud_resource_uri"], # Nextcloud audience
}
auth_url = f"{authorization_endpoint}?{urlencode(idp_params)}"
logger.info("Flow 2: Redirecting to IdP for resource provisioning")
return RedirectResponse(auth_url, status_code=302)
async def oauth_callback_nextcloud(request: Request):
"""
OAuth callback endpoint for Flow 2: Resource Provisioning.
The IdP redirects here after user grants delegated resource access.
Server stores the master refresh token for offline access.
Query parameters:
code: Authorization code from IdP
state: State parameter (session identifier)
error: Error code (if authorization failed)
Returns:
JSON response or HTML success page
"""
# Check for errors from IdP
error = request.query_params.get("error")
if error:
error_description = request.query_params.get(
"error_description", "Authorization failed"
)
logger.error(f"Flow 2 authorization error: {error} - {error_description}")
return JSONResponse(
{
"error": error,
"error_description": error_description,
},
status_code=400,
)
code = request.query_params.get("code")
state = request.query_params.get("state")
if not code or not state:
return JSONResponse(
{
"error": "invalid_request",
"error_description": "code and state parameters are required",
},
status_code=400,
)
# Get OAuth context
oauth_ctx = request.app.state.oauth_context
storage: RefreshTokenStorage = oauth_ctx["storage"]
oauth_config = oauth_ctx["config"]
# Retrieve code_verifier from session storage (PKCE required by Nextcloud OIDC)
code_verifier = ""
oauth_session = await storage.get_oauth_session(state)
if oauth_session:
# code_verifier was stored in mcp_authorization_code field
code_verifier = oauth_session.get("mcp_authorization_code", "")
logger.info(
f"Retrieved code_verifier for Flow 2 callback (state={state[:16]}...)"
)
# Exchange code for tokens
mcp_server_client_id = os.getenv(
"MCP_SERVER_CLIENT_ID", oauth_config.get("client_id")
)
mcp_server_client_secret = os.getenv(
"MCP_SERVER_CLIENT_SECRET", oauth_config.get("client_secret")
)
mcp_server_url = oauth_config["mcp_server_url"]
callback_uri = f"{mcp_server_url}/oauth/callback"
discovery_url = oauth_config.get("discovery_url")
async with httpx.AsyncClient() as http_client:
response = await http_client.get(discovery_url)
response.raise_for_status()
discovery = response.json()
token_endpoint = discovery["token_endpoint"]
# Build token exchange params
token_params = {
"grant_type": "authorization_code",
"code": code,
"redirect_uri": callback_uri,
"client_id": mcp_server_client_id,
"client_secret": mcp_server_client_secret,
}
# Add code_verifier for PKCE (required by Nextcloud OIDC)
if code_verifier:
token_params["code_verifier"] = code_verifier
# Exchange code for tokens
async with httpx.AsyncClient() as http_client:
response = await http_client.post(
token_endpoint,
data=token_params,
)
response.raise_for_status()
token_data = response.json()
refresh_token = token_data.get("refresh_token")
id_token = token_data.get("id_token")
# Decode ID token to get user info
logger.info("=" * 60)
logger.info("oauth_callback_nextcloud: Extracting user_id from ID token")
logger.info("=" * 60)
try:
userinfo = jwt.decode(id_token, options={"verify_signature": False})
user_id = userinfo.get("sub")
username = userinfo.get("preferred_username") or userinfo.get("email")
logger.info(" ✓ ID token decode SUCCESSFUL")
logger.info(f" Extracted user_id: {user_id}")
logger.info(f" Username: {username}")
logger.info(f" ID token payload keys: {list(userinfo.keys())}")
logger.info(f"Flow 2: User {username} provisioned resource access")
except Exception as e:
logger.error(f" ✗ ID token decode FAILED: {type(e).__name__}: {e}")
user_id = "unknown"
logger.error(f" Using fallback user_id: {user_id}")
# Store master refresh token for Flow 2
if refresh_token:
# Parse granted scopes from token response
granted_scopes = (
token_data.get("scope", "").split() if token_data.get("scope") else None
)
logger.info("Storing refresh token:")
logger.info(f" user_id: {user_id}")
logger.info(" flow_type: flow2")
logger.info(" token_audience: nextcloud")
logger.info(f" provisioning_client_id: {state[:16]}...")
logger.info(f" scopes: {granted_scopes}")
await storage.store_refresh_token(
user_id=user_id,
refresh_token=refresh_token,
flow_type="flow2",
token_audience="nextcloud",
provisioning_client_id=state, # Store which client initiated provisioning
scopes=granted_scopes,
expires_at=None, # Refresh tokens typically don't expire
)
logger.info(f"✓ Stored Flow 2 master refresh token for user {user_id}")
logger.info("=" * 60)
# Return success HTML page
success_html = """
<!DOCTYPE html>
<html>
<head>
<title>Nextcloud Access Provisioned</title>
<style>
body { font-family: Arial, sans-serif; text-align: center; margin-top: 50px; }
.success { color: green; }
.info { margin-top: 20px; color: #666; }
</style>
</head>
<body>
<h1 class="success"> Nextcloud Access Provisioned</h1>
<p>The MCP server now has offline access to your Nextcloud resources.</p>
<p class="info">You can close this window and return to your MCP client.</p>
</body>
</html>
"""
from starlette.responses import HTMLResponse
return HTMLResponse(content=success_html, status_code=200)
async def oauth_callback(request: Request):
"""
Unified OAuth callback endpoint supporting multiple flows.
This endpoint consolidates all OAuth callback handling into a single URL.
The flow type is determined by looking up the OAuth session using the
state parameter.
This simplifies IdP configuration by requiring only one callback URL
to be registered: /oauth/callback
Query parameters:
code: Authorization code from IdP
state: CSRF protection state (also used to lookup flow type)
error: Error code (if authorization failed)
Returns:
Response from the appropriate flow handler
"""
# Get state parameter to lookup OAuth session
state = request.query_params.get("state")
if not state:
logger.warning("Unified callback called without state parameter")
return JSONResponse(
{
"error": "invalid_request",
"error_description": "state parameter is required",
},
status_code=400,
)
# Lookup OAuth session to determine flow type
oauth_ctx = request.app.state.oauth_context
if not oauth_ctx:
logger.error("OAuth context not available")
return JSONResponse(
{
"error": "server_error",
"error_description": "OAuth not configured on server",
},
status_code=500,
)
storage = oauth_ctx["storage"]
oauth_session = await storage.get_oauth_session(state)
# Determine flow type from session, default to "browser" for backwards compatibility
flow_type = (
oauth_session.get("flow_type", "browser") if oauth_session else "browser"
)
logger.info(f"Unified callback: flow_type={flow_type} (from session lookup)")
if flow_type == "flow2":
# Flow 2: Resource Provisioning - MCP server gets delegated Nextcloud access
logger.info("Routing to Flow 2 (resource provisioning)")
return await oauth_callback_nextcloud(request)
elif flow_type == "browser":
# Browser UI Login - establish browser session for /user/page access
logger.info("Routing to browser login flow")
from nextcloud_mcp_server.auth.browser_oauth_routes import (
oauth_login_callback,
)
return await oauth_login_callback(request)
else:
# Unknown flow type
logger.warning(f"Unknown flow_type in OAuth session: {flow_type}")
return JSONResponse(
{
"error": "invalid_request",
"error_description": f"Unknown flow type: {flow_type}",
},
status_code=400,
)
-54
View File
@@ -1,54 +0,0 @@
"""Permission checking utilities for Nextcloud admin operations."""
import logging
from httpx import AsyncClient
from starlette.requests import Request
from nextcloud_mcp_server.client.users import UsersClient
logger = logging.getLogger(__name__)
async def is_nextcloud_admin(request: Request, http_client: AsyncClient) -> bool:
"""Check if the authenticated user is a Nextcloud administrator.
This function extracts the username from the session/request context
and checks if the user is a member of the "admin" group in Nextcloud.
Args:
request: Starlette request object with authenticated user
http_client: Authenticated HTTP client for Nextcloud API calls
Returns:
True if user is admin, False otherwise
Example:
```python
if await is_nextcloud_admin(request, http_client):
# Show admin-only features
pass
```
"""
try:
# Extract username from authenticated session
username = request.user.display_name
if not username:
logger.warning("No username found in authenticated session")
return False
# Query Nextcloud for user's group memberships
users_client = UsersClient(http_client, username)
user_groups = await users_client.get_user_groups(username)
# Check if user is in the admin group
is_admin = "admin" in user_groups
logger.debug(
f"Admin check for user '{username}': {is_admin} (groups: {user_groups})"
)
return is_admin
except Exception as e:
logger.error(f"Error checking admin permissions: {e}", exc_info=True)
return False
@@ -1,194 +0,0 @@
"""
Provisioning decorator for ADR-004 (Offline Access Architecture).
This decorator ensures users have completed Flow 2 (Resource Provisioning)
before accessing Nextcloud resources when offline access is enabled.
"""
import functools
import logging
from typing import Callable
from mcp.server.fastmcp import Context
from mcp.shared.exceptions import McpError
from mcp.types import ErrorData
from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
logger = logging.getLogger(__name__)
def require_provisioning(func: Callable) -> Callable:
"""
Decorator that checks if user has provisioned Nextcloud access (Flow 2).
This decorator:
1. Extracts user_id from the MCP token (Flow 1)
2. Checks if user has completed Flow 2 provisioning
3. Returns helpful error message if not provisioned
4. Allows access if provisioned
Usage:
@mcp.tool()
@require_provisioning
async def list_notes(ctx: Context):
# Tool implementation
pass
"""
@functools.wraps(func)
async def wrapper(*args, **kwargs):
# Extract context from arguments
ctx = None
for arg in args:
if isinstance(arg, Context):
ctx = arg
break
if not ctx:
ctx = kwargs.get("ctx")
if not ctx:
raise McpError(
ErrorData(
code=-1,
message="Context not found - cannot verify provisioning",
)
)
# Check if we're in BasicAuth mode - if so, skip provisioning check
# In BasicAuth mode, there's no OAuth and no provisioning needed
lifespan_ctx = ctx.request_context.lifespan_context
if hasattr(lifespan_ctx, "client"):
# BasicAuth mode - no provisioning needed, just proceed
logger.debug("BasicAuth mode detected - skipping provisioning check")
return await func(*args, **kwargs)
# Check if we're in token exchange mode - if so, skip provisioning check
# In token exchange mode, tokens are exchanged per-request (no stored refresh tokens)
from nextcloud_mcp_server.config import get_settings
settings = get_settings()
if hasattr(lifespan_ctx, "nextcloud_host") and settings.enable_token_exchange:
# Token exchange mode - per-request exchange, no provisioning needed
logger.debug("Token exchange mode detected - skipping provisioning check")
return await func(*args, **kwargs)
# Offline access mode - check if user has completed Flow 2 provisioning
# Get user_id from authorization token
user_id = None
if hasattr(ctx, "authorization") and ctx.authorization:
try:
import jwt
token = ctx.authorization.token
payload = jwt.decode(token, options={"verify_signature": False})
user_id = payload.get("sub")
logger.debug(f"Checking provisioning for user: {user_id}")
except Exception as e:
logger.warning(f"Failed to extract user_id from token: {e}")
if not user_id:
raise McpError(
ErrorData(
code=-1,
message="Cannot determine user identity for provisioning check",
)
)
# Check provisioning status
storage = RefreshTokenStorage.from_env()
await storage.initialize()
refresh_data = await storage.get_refresh_token(user_id)
if not refresh_data:
# User has not completed Flow 2 - provide helpful error
logger.info(
f"User {user_id} attempted to use Nextcloud tool without provisioning"
)
raise McpError(
ErrorData(
code=-1,
message=(
"Nextcloud access not provisioned. "
"Please run the 'provision_nextcloud_access' tool first to authorize "
"the MCP server to access Nextcloud on your behalf. "
"This is a one-time setup required for security."
),
)
)
logger.debug(
f"User {user_id} has provisioned access - proceeding with tool execution"
)
# User has provisioned - allow access
return await func(*args, **kwargs)
return wrapper
def require_provisioning_or_suggest(func: Callable) -> Callable:
"""
Softer version that suggests provisioning but doesn't block.
This decorator:
1. Checks provisioning status
2. Logs a warning if not provisioned
3. Still allows the function to proceed
4. Can be used for read-only operations that might work without explicit provisioning
Usage:
@mcp.tool()
@require_provisioning_or_suggest
async def list_tools(ctx: Context):
# Tool implementation
pass
"""
@functools.wraps(func)
async def wrapper(*args, **kwargs):
# Extract context from arguments
ctx = None
for arg in args:
if isinstance(arg, Context):
ctx = arg
break
if not ctx:
ctx = kwargs.get("ctx")
if ctx:
# Try to check provisioning status
try:
# Get user_id from authorization token
user_id = None
if hasattr(ctx, "authorization") and ctx.authorization:
import jwt
token = ctx.authorization.token
payload = jwt.decode(token, options={"verify_signature": False})
user_id = payload.get("sub")
if user_id:
# Check provisioning status
storage = RefreshTokenStorage.from_env()
await storage.initialize()
refresh_data = await storage.get_refresh_token(user_id)
if not refresh_data:
logger.info(
f"User {user_id} has not provisioned Nextcloud access. "
"Some features may not work. Consider running "
"'provision_nextcloud_access' tool."
)
else:
logger.debug(f"User {user_id} has provisioned access")
except Exception as e:
logger.debug(f"Could not check provisioning status: {e}")
# Always proceed with the function
return await func(*args, **kwargs)
return wrapper
@@ -1,9 +1,8 @@
"""Scope-based authorization for MCP tools."""
import logging
import os
from functools import wraps
from typing import Any, Callable
from typing import Callable
from mcp.server.auth.middleware.auth_context import get_access_token
from mcp.server.auth.provider import AccessToken
@@ -34,23 +33,6 @@ class InsufficientScopeError(ScopeAuthorizationError):
)
class ProvisioningRequiredError(ScopeAuthorizationError):
"""Raised when Nextcloud resource access requires provisioning (Flow 2).
In Progressive Consent mode, users must explicitly provision Nextcloud
access using the provision_nextcloud_access MCP tool.
"""
def __init__(self, message: str | None = None):
super().__init__(
message
or (
"Nextcloud resource access not provisioned. "
"Please run the 'provision_nextcloud_access' tool to grant access."
)
)
def require_scopes(*required_scopes: str):
"""
Decorator to require specific OAuth scopes for MCP tool execution.
@@ -88,18 +70,15 @@ def require_scopes(*required_scopes: str):
ScopeAuthorizationError: If required scopes are not present in the access token
"""
def decorator(func: Callable) -> Callable:
def decorator(func: Callable):
# Store scope requirements as function metadata for dynamic filtering
func._required_scopes = list(required_scopes) # type: ignore[attr-defined]
# Get function name for logging (works for any callable)
func_name = getattr(func, "__name__", repr(func))
func._required_scopes = list(required_scopes) # type: ignore
# Find which parameter receives the Context (FastMCP injects it by name)
context_param_name = find_context_parameter(func)
@wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> Any:
async def wrapper(*args, **kwargs):
# Extract context from kwargs (where FastMCP injected it)
ctx: Context | None = (
kwargs.get(context_param_name) if context_param_name else None
@@ -109,7 +88,7 @@ def require_scopes(*required_scopes: str):
# No context parameter found - likely BasicAuth mode
# In BasicAuth mode, all operations are allowed
logger.debug(
f"No context parameter for {func_name} - allowing (BasicAuth mode)"
f"No context parameter for {func.__name__} - allowing (BasicAuth mode)"
)
return await func(*args, **kwargs)
@@ -122,7 +101,7 @@ def require_scopes(*required_scopes: str):
# Not in OAuth mode (BasicAuth or no auth)
# In BasicAuth mode, all operations are allowed
logger.debug(
f"No access token present for {func_name} - allowing (BasicAuth mode)"
f"No access token present for {func.__name__} - allowing (BasicAuth mode)"
)
return await func(*args, **kwargs)
@@ -130,63 +109,11 @@ def require_scopes(*required_scopes: str):
token_scopes = set(access_token.scopes or [])
required_scopes_set = set(required_scopes)
# Check if offline access is enabled
enable_offline_access = (
os.getenv("ENABLE_OFFLINE_ACCESS", "false").lower() == "true"
)
# In offline access mode, check if Nextcloud scopes require provisioning
if enable_offline_access:
# Check if any required scopes are Nextcloud-specific
nextcloud_scopes = [
s
for s in required_scopes
if any(
s.startswith(prefix)
for prefix in [
"notes:",
"calendar:",
"contacts:",
"files:",
"tables:",
"deck:",
]
)
]
if nextcloud_scopes:
# Check if user has completed Flow 2 provisioning
# This would be indicated by having a stored refresh token
# In production, we'd check the token broker or storage
# For now, we check if the token has the required scopes
# (Flow 1 tokens won't have Nextcloud scopes)
has_nextcloud_scopes = any(
s.startswith(prefix)
for s in token_scopes
for prefix in [
"notes:",
"calendar:",
"contacts:",
"files:",
"tables:",
"deck:",
]
)
if not has_nextcloud_scopes:
error_msg = (
f"Access denied to {func_name}: "
f"Nextcloud resource access not provisioned. "
f"Please run the 'provision_nextcloud_access' tool first."
)
logger.warning(error_msg)
raise ProvisioningRequiredError(error_msg)
# Check if all required scopes are present
missing_scopes = required_scopes_set - token_scopes
if missing_scopes:
error_msg = (
f"Access denied to {func_name}: "
f"Access denied to {func.__name__}: "
f"Missing required scopes: {', '.join(sorted(missing_scopes))}. "
f"Token has scopes: {', '.join(sorted(token_scopes)) if token_scopes else 'none'}"
)
@@ -195,7 +122,7 @@ def require_scopes(*required_scopes: str):
# All required scopes present - allow execution
logger.debug(
f"Scope authorization passed for {func_name}: {required_scopes}"
f"Scope authorization passed for {func.__name__}: {required_scopes}"
)
return await func(*args, **kwargs)
@@ -1,96 +0,0 @@
"""Session-based authentication backend for Starlette routes.
Provides browser-based authentication for admin UI routes, separate from
MCP's OAuth authentication flow.
"""
import logging
import os
from starlette.authentication import (
AuthCredentials,
AuthenticationBackend,
SimpleUser,
)
from starlette.requests import HTTPConnection
logger = logging.getLogger(__name__)
class SessionAuthBackend(AuthenticationBackend):
"""Authentication backend using signed session cookies.
For BasicAuth mode: Always authenticates as the configured user.
For OAuth mode: Checks for valid session cookie with stored refresh token.
"""
def __init__(self, oauth_enabled: bool = False):
"""Initialize session authentication backend.
Args:
oauth_enabled: Whether OAuth mode is enabled
"""
self.oauth_enabled = oauth_enabled
async def authenticate(
self, conn: HTTPConnection
) -> tuple[AuthCredentials, SimpleUser] | None:
"""Authenticate the request based on session cookie or BasicAuth mode.
This backend is only applied to browser routes (/user/*) via a separate
Starlette app mount. FastMCP routes use their own OAuth Bearer token
authentication.
Args:
conn: HTTP connection
Returns:
Tuple of (credentials, user) if authenticated, None otherwise
"""
# BasicAuth mode: Always authenticated as the configured user
if not self.oauth_enabled:
username = os.getenv("NEXTCLOUD_USERNAME", "admin")
return AuthCredentials(["authenticated", "admin"]), SimpleUser(username)
# OAuth mode: Check for session cookie
session_id = conn.cookies.get("mcp_session")
logger.info(
f"Session authentication check - cookie present: {session_id is not None}, path: {conn.url.path}"
)
if not session_id:
logger.info("No session cookie found - redirecting to login")
return None
logger.info(f"Found session cookie: {session_id[:16]}...")
# Get OAuth context from app state
oauth_context = getattr(conn.app.state, "oauth_context", None)
if not oauth_context:
logger.warning("OAuth context not available in app state")
return None
# Validate session
storage = oauth_context.get("storage")
if not storage:
logger.warning("OAuth storage not available")
return None
try:
# Check if user has refresh token (indicates logged-in session)
logger.info(f"Looking up refresh token for session: {session_id[:16]}...")
token_data = await storage.get_refresh_token(session_id)
if not token_data:
logger.warning(
f"No refresh token found for session {session_id[:16]}..."
)
return None
# Session is valid - use session_id (which is user_id from ID token) as username
username = session_id
logger.info(f"✓ Session authenticated successfully: {username[:16]}...")
return AuthCredentials(["authenticated"]), SimpleUser(username)
except Exception as e:
logger.warning(f"Session validation error: {e}")
return None
File diff suppressed because it is too large Load Diff
-588
View File
@@ -1,588 +0,0 @@
"""
Token Broker Service for ADR-004 Progressive Consent Architecture.
This service manages the lifecycle of Nextcloud access tokens, implementing
the dual OAuth flow pattern where:
1. MCP clients authenticate to MCP server with aud:"mcp-server" tokens
2. MCP server uses stored refresh tokens to obtain aud:"nextcloud" tokens
The Token Broker provides:
- Automatic token refresh when expired
- Short-lived token caching (5-minute TTL)
- Master refresh token rotation
- Audience-specific token validation
- Session vs background token separation (RFC 8693)
"""
import asyncio
import logging
from datetime import datetime, timedelta, timezone
from typing import Dict, Optional, Tuple
import httpx
import jwt
from cryptography.fernet import Fernet
from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
from nextcloud_mcp_server.auth.token_exchange import exchange_token_for_delegation
logger = logging.getLogger(__name__)
class TokenCache:
"""In-memory cache for short-lived Nextcloud access tokens."""
def __init__(self, ttl_seconds: int = 300, early_refresh_seconds: int = 30):
"""
Initialize the token cache.
Args:
ttl_seconds: Default TTL for cached tokens (5 minutes default)
early_refresh_seconds: How many seconds before expiry to trigger early refresh (30s default)
"""
self._cache: Dict[str, Tuple[str, datetime]] = {}
self._ttl = timedelta(seconds=ttl_seconds)
self._early_refresh = timedelta(seconds=early_refresh_seconds)
self._lock = asyncio.Lock()
async def get(self, user_id: str) -> Optional[str]:
"""Get cached token if valid."""
async with self._lock:
if user_id not in self._cache:
return None
token, expiry = self._cache[user_id]
now = datetime.now(timezone.utc)
# Check if token has expired
if now >= expiry:
del self._cache[user_id]
logger.debug(f"Cached token expired for user {user_id}")
return None
# Check if token will expire soon (refresh early)
if now >= expiry - self._early_refresh:
logger.debug(f"Cached token expiring soon for user {user_id}")
return None
logger.debug(f"Using cached token for user {user_id}")
return token
async def set(self, user_id: str, token: str, expires_in: int | None = None):
"""Store token in cache."""
async with self._lock:
# Use provided expiry or default TTL
if expires_in:
expiry = datetime.now(timezone.utc) + timedelta(seconds=expires_in)
else:
expiry = datetime.now(timezone.utc) + self._ttl
self._cache[user_id] = (token, expiry)
logger.debug(f"Cached token for user {user_id} until {expiry}")
async def invalidate(self, user_id: str):
"""Remove token from cache."""
async with self._lock:
if user_id in self._cache:
del self._cache[user_id]
logger.debug(f"Invalidated cached token for user {user_id}")
class TokenBrokerService:
"""
Manages token lifecycle for the Progressive Consent architecture.
This service handles:
- Getting or refreshing Nextcloud access tokens
- Managing a short-lived token cache
- Refreshing master refresh tokens periodically
- Validating token audiences
"""
def __init__(
self,
storage: RefreshTokenStorage,
oidc_discovery_url: str,
nextcloud_host: str,
encryption_key: str,
cache_ttl: int = 300,
cache_early_refresh: int = 30,
):
"""
Initialize the Token Broker Service.
Args:
storage: Database storage for refresh tokens
oidc_discovery_url: OIDC provider discovery URL
nextcloud_host: Nextcloud server URL
encryption_key: Fernet key for token encryption
cache_ttl: Cache TTL in seconds (default: 5 minutes)
cache_early_refresh: Early refresh threshold in seconds (default: 30 seconds)
"""
self.storage = storage
self.oidc_discovery_url = oidc_discovery_url
self.nextcloud_host = nextcloud_host
self.fernet = Fernet(
encryption_key.encode()
if isinstance(encryption_key, str)
else encryption_key
)
self.cache = TokenCache(cache_ttl, cache_early_refresh)
self._oidc_config = None
self._http_client = None
async def _get_http_client(self) -> httpx.AsyncClient:
"""Get or create HTTP client."""
if self._http_client is None:
self._http_client = httpx.AsyncClient(
timeout=httpx.Timeout(30.0), follow_redirects=True
)
return self._http_client
async def _get_oidc_config(self) -> dict:
"""Get OIDC configuration from discovery endpoint."""
if self._oidc_config is None:
client = await self._get_http_client()
response = await client.get(self.oidc_discovery_url)
response.raise_for_status()
self._oidc_config = response.json()
return self._oidc_config
async def get_nextcloud_token(self, user_id: str) -> Optional[str]:
"""
Get a valid Nextcloud access token for the user.
DEPRECATED: This method uses the old pattern of stored refresh tokens
for all operations. Use get_session_token() or get_background_token()
instead for proper session/background separation.
This method:
1. Checks the cache for a valid token
2. If not cached, checks for stored refresh token
3. If refresh token exists, obtains new access token
4. Caches the new token for future requests
Args:
user_id: The user identifier
Returns:
Valid Nextcloud access token or None if not provisioned
"""
# Check cache first
cached_token = await self.cache.get(user_id)
if cached_token:
return cached_token
# Get stored refresh token
refresh_data = await self.storage.get_refresh_token(user_id)
if not refresh_data:
logger.info(f"No refresh token found for user {user_id}")
return None
try:
# Decrypt refresh token
encrypted_token = refresh_data["refresh_token"]
refresh_token = self.fernet.decrypt(encrypted_token.encode()).decode()
# Exchange refresh token for new access token
access_token, expires_in = await self._refresh_access_token(refresh_token)
# Cache the new token
await self.cache.set(user_id, access_token, expires_in)
return access_token
except Exception as e:
logger.error(f"Failed to get Nextcloud token for user {user_id}: {e}")
# Invalidate cache on error
await self.cache.invalidate(user_id)
return None
async def get_session_token(
self,
flow1_token: str,
required_scopes: list[str],
requested_audience: str = "nextcloud",
) -> Optional[str]:
"""
Get ephemeral token for MCP session operations (on-demand).
This implements the correct Progressive Consent pattern where:
1. Client provides Flow 1 token (aud: "mcp-server")
2. Server exchanges it for ephemeral Nextcloud token
3. Token is NOT stored, only used for current operation
Key properties:
- On-demand generation during tool execution
- Ephemeral (not stored, discarded after use)
- Limited scopes (only what tool needs)
- Short-lived (5 minutes)
Args:
flow1_token: The MCP session token (aud: "mcp-server")
required_scopes: Minimal scopes needed for this operation
requested_audience: Target audience (usually "nextcloud")
Returns:
Ephemeral Nextcloud access token or None if exchange fails
"""
try:
# Perform RFC 8693 token exchange
delegated_token, expires_in = await exchange_token_for_delegation(
flow1_token=flow1_token,
requested_scopes=required_scopes,
requested_audience=requested_audience,
)
# NOTE: We intentionally do NOT cache session tokens
# They are ephemeral and should be discarded after use
logger.info(
f"Generated ephemeral session token with scopes: {required_scopes}, "
f"expires in {expires_in}s"
)
return delegated_token
except Exception as e:
logger.error(f"Failed to get session token: {e}")
return None
async def get_background_token(
self, user_id: str, required_scopes: list[str]
) -> Optional[str]:
"""
Get token for background job operations (uses stored refresh token).
This is for background/offline operations that run without user interaction.
Uses the stored refresh token from Flow 2 provisioning.
Key properties:
- Uses stored refresh token from Flow 2
- Different scopes than session tokens
- Longer-lived for background operations
- Can be cached for efficiency
Args:
user_id: The user identifier
required_scopes: Scopes needed for background operation
Returns:
Nextcloud access token for background operations or None if not provisioned
"""
# Check cache first (background tokens can be cached)
cache_key = f"{user_id}:background:{','.join(sorted(required_scopes))}"
cached_token = await self.cache.get(cache_key)
if cached_token:
return cached_token
# Get stored refresh token
refresh_data = await self.storage.get_refresh_token(user_id)
if not refresh_data:
logger.info(f"No refresh token found for user {user_id}")
return None
try:
# Decrypt refresh token
encrypted_token = refresh_data["refresh_token"]
refresh_token = self.fernet.decrypt(encrypted_token.encode()).decode()
# Get token with specific scopes for background operation
access_token, expires_in = await self._refresh_access_token_with_scopes(
refresh_token, required_scopes
)
# Cache the background token
await self.cache.set(cache_key, access_token, expires_in)
logger.info(
f"Generated background token for user {user_id} with scopes: {required_scopes}"
)
return access_token
except Exception as e:
logger.error(f"Failed to get background token for user {user_id}: {e}")
await self.cache.invalidate(cache_key)
return None
async def _refresh_access_token(self, refresh_token: str) -> Tuple[str, int]:
"""
Exchange refresh token for new access token.
DEPRECATED: Use _refresh_access_token_with_scopes() for scope-specific requests.
Args:
refresh_token: The refresh token
Returns:
Tuple of (access_token, expires_in_seconds)
"""
config = await self._get_oidc_config()
token_endpoint = config["token_endpoint"]
client = await self._get_http_client()
# Request new access token using refresh token
data = {
"grant_type": "refresh_token",
"refresh_token": refresh_token,
"scope": "openid profile email notes:read notes:write calendar:read calendar:write",
}
response = await client.post(
token_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
if response.status_code != 200:
logger.error(
f"Token refresh failed: {response.status_code} - {response.text}"
)
raise Exception(f"Token refresh failed: {response.status_code}")
token_data = response.json()
access_token = token_data["access_token"]
expires_in = token_data.get("expires_in", 3600) # Default 1 hour
# Validate audience
await self._validate_token_audience(access_token, "nextcloud")
logger.info(f"Refreshed access token (expires in {expires_in}s)")
return access_token, expires_in
async def _refresh_access_token_with_scopes(
self, refresh_token: str, required_scopes: list[str]
) -> Tuple[str, int]:
"""
Exchange refresh token for new access token with specific scopes.
This method implements scope downscoping for least privilege.
Args:
refresh_token: The refresh token
required_scopes: Minimal scopes needed for this operation
Returns:
Tuple of (access_token, expires_in_seconds)
"""
config = await self._get_oidc_config()
token_endpoint = config["token_endpoint"]
client = await self._get_http_client()
# Always include basic OpenID scopes
scopes = list(set(["openid", "profile", "email"] + required_scopes))
# Request new access token with specific scopes
data = {
"grant_type": "refresh_token",
"refresh_token": refresh_token,
"scope": " ".join(scopes),
}
response = await client.post(
token_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
if response.status_code != 200:
logger.error(
f"Token refresh with scopes failed: {response.status_code} - {response.text}"
)
raise Exception(f"Token refresh failed: {response.status_code}")
token_data = response.json()
access_token = token_data["access_token"]
expires_in = token_data.get("expires_in", 3600) # Default 1 hour
# Validate audience
await self._validate_token_audience(access_token, "nextcloud")
logger.info(
f"Refreshed access token with scopes {scopes} (expires in {expires_in}s)"
)
return access_token, expires_in
async def _validate_token_audience(self, token: str, expected_audience: str):
"""
Validate that token has correct audience claim.
Args:
token: JWT token to validate
expected_audience: Expected audience value
Raises:
ValueError: If audience doesn't match
"""
try:
# Decode without verification to check claims
# In production, should verify signature
claims = jwt.decode(token, options={"verify_signature": False})
audience = claims.get("aud", [])
if isinstance(audience, str):
audience = [audience]
if expected_audience not in audience:
raise ValueError(
f"Token audience {audience} doesn't include {expected_audience}"
)
except jwt.DecodeError as e:
# Token might be opaque, skip validation
logger.debug(f"Cannot decode token for audience validation: {e}")
async def refresh_master_token(self, user_id: str) -> bool:
"""
Refresh the master refresh token (periodic rotation).
This should be called periodically (e.g., daily) to rotate
refresh tokens for security.
Args:
user_id: The user identifier
Returns:
True if refresh successful, False otherwise
"""
refresh_data = await self.storage.get_refresh_token(user_id)
if not refresh_data:
logger.warning(f"No refresh token to rotate for user {user_id}")
return False
try:
# Decrypt current refresh token
encrypted_token = refresh_data["refresh_token"]
current_refresh_token = self.fernet.decrypt(
encrypted_token.encode()
).decode()
# Get OIDC configuration
config = await self._get_oidc_config()
token_endpoint = config["token_endpoint"]
client = await self._get_http_client()
# Request new refresh token
data = {
"grant_type": "refresh_token",
"refresh_token": current_refresh_token,
"scope": "openid profile email offline_access notes:read notes:write calendar:read calendar:write",
}
response = await client.post(
token_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
if response.status_code != 200:
logger.error(f"Master token refresh failed: {response.status_code}")
return False
token_data = response.json()
new_refresh_token = token_data.get("refresh_token")
if new_refresh_token and new_refresh_token != current_refresh_token:
# Encrypt and store new refresh token
encrypted_new = self.fernet.encrypt(new_refresh_token.encode()).decode()
await self.storage.store_refresh_token(
user_id=user_id,
refresh_token=encrypted_new,
expires_at=datetime.now(timezone.utc)
+ timedelta(days=90), # 90-day expiry
)
logger.info(f"Rotated master refresh token for user {user_id}")
# Invalidate cached access token
await self.cache.invalidate(user_id)
return True
return True
except Exception as e:
logger.error(f"Failed to refresh master token for user {user_id}: {e}")
return False
async def has_nextcloud_provisioning(self, user_id: str) -> bool:
"""
Check if user has provisioned Nextcloud access (Flow 2).
Args:
user_id: The user identifier
Returns:
True if user has stored refresh token, False otherwise
"""
refresh_data = await self.storage.get_refresh_token(user_id)
return refresh_data is not None
async def revoke_nextcloud_access(self, user_id: str) -> bool:
"""
Revoke stored Nextcloud access for a user.
This removes stored refresh tokens and clears cache.
Args:
user_id: The user identifier
Returns:
True if revocation successful
"""
try:
# Get refresh token for revocation at IdP
refresh_data = await self.storage.get_refresh_token(user_id)
if refresh_data:
try:
# Attempt to revoke at IdP
encrypted_token = refresh_data["refresh_token"]
refresh_token = self.fernet.decrypt(
encrypted_token.encode()
).decode()
await self._revoke_token_at_idp(refresh_token)
except Exception as e:
logger.warning(f"Failed to revoke at IdP: {e}")
# Remove from storage
await self.storage.delete_refresh_token(user_id)
# Clear cache
await self.cache.invalidate(user_id)
logger.info(f"Revoked Nextcloud access for user {user_id}")
return True
except Exception as e:
logger.error(f"Failed to revoke access for user {user_id}: {e}")
return False
async def _revoke_token_at_idp(self, token: str):
"""Revoke token at the IdP if revocation endpoint exists."""
config = await self._get_oidc_config()
revocation_endpoint = config.get("revocation_endpoint")
if not revocation_endpoint:
logger.debug("No revocation endpoint available")
return
client = await self._get_http_client()
data = {"token": token, "token_type_hint": "refresh_token"}
response = await client.post(
revocation_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
if response.status_code == 200:
logger.info("Token revoked at IdP")
else:
logger.warning(f"Token revocation returned {response.status_code}")
async def close(self):
"""Clean up resources."""
if self._http_client:
await self._http_client.aclose()
-595
View File
@@ -1,595 +0,0 @@
"""RFC 8693 Token Exchange implementation for ADR-004 Progressive Consent.
This module implements the token exchange pattern to convert Flow 1 MCP tokens
(aud: "mcp-server") into ephemeral delegated Nextcloud tokens (aud: "nextcloud")
for session operations.
Key Properties:
- On-demand generation during tool execution
- Ephemeral tokens (NOT stored, discarded after use)
- Limited scopes (only what tool needs)
- Short-lived (5 minutes default)
"""
import logging
import time
from typing import Any, Dict, Optional, Tuple
from urllib.parse import urljoin
import httpx
import jwt
from ..config import get_settings
from .storage import RefreshTokenStorage
logger = logging.getLogger(__name__)
class TokenExchangeService:
"""Implements RFC 8693 OAuth 2.0 Token Exchange."""
# RFC 8693 Grant Type
TOKEN_EXCHANGE_GRANT = "urn:ietf:params:oauth:grant-type:token-exchange"
# RFC 8693 Token Type Identifiers
TOKEN_TYPE_ACCESS_TOKEN = "urn:ietf:params:oauth:token-type:access_token"
TOKEN_TYPE_JWT = "urn:ietf:params:oauth:token-type:jwt"
TOKEN_TYPE_ID_TOKEN = "urn:ietf:params:oauth:token-type:id_token"
def __init__(
self,
oidc_discovery_url: Optional[str] = None,
client_id: Optional[str] = None,
client_secret: Optional[str] = None,
nextcloud_host: Optional[str] = None,
):
"""Initialize token exchange service.
Args:
oidc_discovery_url: OIDC discovery endpoint URL
client_id: OAuth client ID for token exchange
client_secret: OAuth client secret
nextcloud_host: Nextcloud instance URL
"""
settings = get_settings()
self.oidc_discovery_url = oidc_discovery_url or settings.oidc_discovery_url
self.client_id = client_id or settings.oidc_client_id
self.client_secret = client_secret or settings.oidc_client_secret
self.nextcloud_host = nextcloud_host or settings.nextcloud_host
self._token_endpoint: Optional[str] = None
self._jwks_uri: Optional[str] = None
self._discovery_cache: Optional[Dict[str, Any]] = None
self._discovery_cache_time: float = 0
self._discovery_cache_ttl: float = 3600 # 1 hour
# Storage for Progressive Consent (refresh tokens) - only needed for delegation
# NOT needed for pure RFC 8693 exchange (MCP tools)
self.storage: Optional[RefreshTokenStorage] = None
# Create HTTP client
self.http_client = httpx.AsyncClient(
timeout=30.0,
follow_redirects=True,
)
async def __aenter__(self):
"""Async context manager entry."""
if self.storage:
await self.storage.initialize()
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
"""Async context manager exit."""
await self.close()
async def close(self):
"""Close HTTP client and storage."""
await self.http_client.aclose()
# RefreshTokenStorage doesn't have a close method
async def _ensure_storage(self):
"""Lazily initialize storage for Progressive Consent operations.
Only needed for delegation operations that use refresh tokens.
NOT needed for pure RFC 8693 exchange (MCP tools).
"""
if self.storage is None:
self.storage = RefreshTokenStorage.from_env()
await self.storage.initialize()
async def _discover_endpoints(self) -> Dict[str, Any]:
"""Discover OIDC endpoints from discovery URL.
Returns:
Discovery document containing endpoint URLs
"""
# Check cache
if (
self._discovery_cache
and (time.time() - self._discovery_cache_time) < self._discovery_cache_ttl
):
return self._discovery_cache
if not self.oidc_discovery_url:
# Fallback to Nextcloud OIDC if no discovery URL
self.oidc_discovery_url = urljoin(
self.nextcloud_host, # type: ignore[arg-type]
"/.well-known/openid-configuration",
)
try:
response = await self.http_client.get(self.oidc_discovery_url)
response.raise_for_status()
self._discovery_cache = response.json()
self._discovery_cache_time = time.time()
# Cache frequently used endpoints
self._token_endpoint = self._discovery_cache.get("token_endpoint")
self._jwks_uri = self._discovery_cache.get("jwks_uri")
return self._discovery_cache
except Exception as e:
logger.error(f"Failed to discover OIDC endpoints: {e}")
raise
async def exchange_token_for_delegation(
self,
flow1_token: str,
requested_scopes: list[str],
requested_audience: str = "nextcloud",
) -> Tuple[str, int]:
"""Exchange Flow 1 MCP token for delegated Nextcloud token.
This implements RFC 8693 Token Exchange for on-behalf-of delegation.
Args:
flow1_token: The MCP session token (aud: "mcp-server")
requested_scopes: Scopes needed for this operation
requested_audience: Target audience (usually "nextcloud")
Returns:
Tuple of (delegated_token, expires_in)
Raises:
ValueError: If token validation fails
RuntimeError: If provisioning not completed or exchange fails
"""
# 1. Validate Flow 1 token audience
await self._validate_flow1_token(flow1_token)
# 2. Extract user ID from token
user_id = self._extract_user_id(flow1_token)
# 3. Check user has provisioned Nextcloud access (Flow 2)
if not await self._check_provisioning(user_id):
raise RuntimeError(
"Nextcloud access not provisioned. "
"User must complete Flow 2 provisioning first."
)
# 4. Get stored refresh token for user (from Flow 2)
refresh_token = await self._get_user_refresh_token(user_id)
if not refresh_token:
raise RuntimeError(
"No refresh token found. User must complete provisioning."
)
# 5. Perform token exchange with IdP
delegated_token, expires_in = await self._perform_token_exchange(
subject_token=flow1_token,
refresh_token=refresh_token,
requested_scopes=requested_scopes,
requested_audience=requested_audience,
)
# 6. Log the exchange for audit trail
logger.info(
f"Token exchange completed for user {user_id}: "
f"scopes={requested_scopes}, audience={requested_audience}, "
f"expires_in={expires_in}s"
)
return delegated_token, expires_in
async def exchange_token_for_audience(
self,
subject_token: str,
requested_audience: str = "nextcloud",
requested_scopes: list[str] | None = None,
) -> Tuple[str, int]:
"""
Pure RFC 8693 token exchange (no refresh tokens required).
This implements stateless per-request token exchange where:
1. Client token has aud: <client-id> (e.g., "nextcloud-mcp-server")
2. Exchange for token with aud: "nextcloud" (for API access)
3. NO refresh tokens or provisioning required
Use case: All MCP tool calls (request-time operations).
NOT for background jobs (which use refresh tokens separately).
Args:
subject_token: Token being exchanged (from MCP client)
requested_audience: Target audience (usually "nextcloud")
requested_scopes: Optional scopes (may not be supported by all IdPs)
Returns:
Tuple of (access_token, expires_in)
Raises:
ValueError: If token validation fails
RuntimeError: If exchange fails
"""
# 1. Validate subject token (accepts both "mcp-server" and client_id)
await self._validate_flow1_token(subject_token)
# 2. Extract user ID for logging
user_id = self._extract_user_id(subject_token)
# 3. Discover token endpoint
discovery = await self._discover_endpoints()
token_endpoint = discovery.get("token_endpoint")
if not token_endpoint:
raise RuntimeError("No token endpoint found in discovery")
# 4. Build pure RFC 8693 exchange request (subject_token ONLY)
data = {
"grant_type": self.TOKEN_EXCHANGE_GRANT,
"subject_token": subject_token,
"subject_token_type": self.TOKEN_TYPE_ACCESS_TOKEN,
"requested_token_type": self.TOKEN_TYPE_ACCESS_TOKEN,
"audience": requested_audience,
}
# Add scopes if provided (may not be supported by all providers)
if requested_scopes:
data["scope"] = " ".join(requested_scopes)
# Add client credentials
if self.client_id and self.client_secret:
data["client_id"] = self.client_id
data["client_secret"] = self.client_secret
try:
# Perform exchange
logger.debug(f"Exchanging token for audience={requested_audience}")
response = await self.http_client.post(
token_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
response.raise_for_status()
result = response.json()
access_token = result.get("access_token")
expires_in = result.get("expires_in", 300)
if not access_token:
raise RuntimeError("No access token in exchange response")
logger.info(
f"Pure RFC 8693 token exchange successful for user {user_id}: "
f"audience={requested_audience}, expires_in={expires_in}s"
)
return access_token, expires_in
except httpx.HTTPStatusError as e:
logger.error(f"Token exchange failed: {e.response.text}")
raise RuntimeError(f"Token exchange failed: {e}")
except Exception as e:
logger.error(f"Token exchange error: {e}")
raise
async def _validate_flow1_token(self, token: str):
"""Validate that token has correct audience for MCP server.
Accepts either:
- "mcp-server" (Progressive Consent legacy)
- self.client_id (external IdP, e.g., "nextcloud-mcp-server")
Args:
token: JWT token to validate
Raises:
ValueError: If token is invalid or has wrong audience
"""
try:
# Decode without verification first to check audience
# In production, should verify signature against JWKS
payload = jwt.decode(token, options={"verify_signature": False})
# Check audience
audience = payload.get("aud", [])
if isinstance(audience, str):
audience = [audience]
# Accept either "mcp-server" (Progressive Consent) or client_id (external IdP)
valid_audiences = ["mcp-server"]
if self.client_id:
valid_audiences.append(self.client_id)
if not any(aud in audience for aud in valid_audiences):
raise ValueError(
f"Invalid token audience. Expected one of {valid_audiences}, got {audience}"
)
# Check expiration
exp = payload.get("exp", 0)
if exp < time.time():
raise ValueError("Token has expired")
except jwt.DecodeError as e:
raise ValueError(f"Invalid JWT token: {e}")
def _extract_user_id(self, token: str) -> str:
"""Extract user ID from JWT token.
Args:
token: JWT token
Returns:
User ID from token
"""
try:
payload = jwt.decode(token, options={"verify_signature": False})
# Try standard claims in order of preference
user_id = (
payload.get("sub")
or payload.get("preferred_username")
or payload.get("email")
or payload.get("name")
)
if not user_id:
raise ValueError("No user identifier in token")
return user_id
except jwt.DecodeError as e:
raise ValueError(f"Failed to extract user ID: {e}")
async def _check_provisioning(self, user_id: str) -> bool:
"""Check if user has completed Flow 2 provisioning.
Args:
user_id: User identifier
Returns:
True if provisioned, False otherwise
"""
await self._ensure_storage()
assert self.storage is not None # _ensure_storage() ensures this
token_data = await self.storage.get_refresh_token(user_id)
return token_data is not None
async def _get_user_refresh_token(self, user_id: str) -> Optional[str]:
"""Get stored refresh token for user from Flow 2 provisioning.
Args:
user_id: User identifier
Returns:
Refresh token if found, None otherwise
"""
await self._ensure_storage()
assert self.storage is not None # _ensure_storage() ensures this
token_data = await self.storage.get_refresh_token(user_id)
if token_data:
return token_data.get("refresh_token")
return None
async def _perform_token_exchange(
self,
subject_token: str,
refresh_token: str,
requested_scopes: list[str],
requested_audience: str,
) -> Tuple[str, int]:
"""Perform RFC 8693 token exchange with IdP.
Args:
subject_token: The token being exchanged (Flow 1 token)
refresh_token: User's stored refresh token for delegation
requested_scopes: Minimal scopes for this operation
requested_audience: Target audience
Returns:
Tuple of (access_token, expires_in)
"""
# Discover token endpoint
discovery = await self._discover_endpoints()
token_endpoint = discovery.get("token_endpoint")
if not token_endpoint:
raise RuntimeError("No token endpoint found in discovery")
# Build token exchange request per RFC 8693
data = {
# Token exchange grant type
"grant_type": "urn:ietf:params:oauth:grant-type:token-exchange",
# The token we're exchanging (Flow 1 MCP token)
"subject_token": subject_token,
"subject_token_type": self.TOKEN_TYPE_ACCESS_TOKEN,
# Use refresh token as actor token (proves we have delegation rights)
"actor_token": refresh_token,
"actor_token_type": self.TOKEN_TYPE_ACCESS_TOKEN,
# Requested token properties
"requested_token_type": self.TOKEN_TYPE_ACCESS_TOKEN,
"audience": requested_audience,
"scope": " ".join(requested_scopes),
}
# Add client credentials if configured
if self.client_id and self.client_secret:
data["client_id"] = self.client_id
data["client_secret"] = self.client_secret
try:
# Attempt RFC 8693 token exchange
response = await self.http_client.post(
token_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
if response.status_code == 400:
# Token exchange might not be supported, fall back to refresh grant
logger.info(
"Token exchange not supported, falling back to refresh grant"
)
return await self._fallback_refresh_grant(
refresh_token=refresh_token,
requested_scopes=requested_scopes,
token_endpoint=token_endpoint,
)
response.raise_for_status()
result = response.json()
access_token = result.get("access_token")
expires_in = result.get("expires_in", 300) # Default 5 minutes
if not access_token:
raise RuntimeError("No access token in exchange response")
return access_token, expires_in
except httpx.HTTPStatusError as e:
logger.error(f"Token exchange failed: {e.response.text}")
raise RuntimeError(f"Token exchange failed: {e}")
except Exception as e:
logger.error(f"Token exchange error: {e}")
raise
async def _fallback_refresh_grant(
self, refresh_token: str, requested_scopes: list[str], token_endpoint: str
) -> Tuple[str, int]:
"""Fallback to standard refresh token grant if token exchange not supported.
This is less secure than token exchange but provides compatibility.
Args:
refresh_token: User's stored refresh token
requested_scopes: Minimal scopes for this operation
token_endpoint: Token endpoint URL
Returns:
Tuple of (access_token, expires_in)
"""
data = {
"grant_type": "refresh_token",
"refresh_token": refresh_token,
"scope": " ".join(requested_scopes), # Request minimal scopes
}
# Add client credentials if configured
if self.client_id and self.client_secret:
data["client_id"] = self.client_id
data["client_secret"] = self.client_secret
try:
response = await self.http_client.post(
token_endpoint,
data=data,
headers={"Content-Type": "application/x-www-form-urlencoded"},
)
response.raise_for_status()
result = response.json()
access_token = result.get("access_token")
expires_in = result.get("expires_in", 300) # Default 5 minutes
if not access_token:
raise RuntimeError("No access token in refresh response")
# Log that we're using fallback
logger.warning(
f"Using refresh grant fallback for token exchange. "
f"Scopes: {requested_scopes}"
)
return access_token, expires_in
except httpx.HTTPStatusError as e:
logger.error(f"Refresh grant failed: {e.response.text}")
raise RuntimeError(f"Refresh grant failed: {e}")
except Exception as e:
logger.error(f"Refresh grant error: {e}")
raise
# Singleton instance
_token_exchange_service: Optional[TokenExchangeService] = None
async def get_token_exchange_service() -> TokenExchangeService:
"""Get or create the singleton token exchange service.
Note: Storage is initialized lazily only when needed for delegation operations.
Pure RFC 8693 exchange (MCP tools) doesn't require storage.
Returns:
TokenExchangeService instance
"""
global _token_exchange_service
if _token_exchange_service is None:
_token_exchange_service = TokenExchangeService()
# Storage is initialized lazily via _ensure_storage() when needed
return _token_exchange_service
async def exchange_token_for_delegation(
flow1_token: str, requested_scopes: list[str], requested_audience: str = "nextcloud"
) -> Tuple[str, int]:
"""Convenience function to exchange tokens (Progressive Consent with refresh tokens).
NOTE: This is for background jobs only. For MCP tool calls, use exchange_token_for_audience().
Args:
flow1_token: The MCP session token (aud: "mcp-server")
requested_scopes: Scopes needed for this operation
requested_audience: Target audience (usually "nextcloud")
Returns:
Tuple of (delegated_token, expires_in)
"""
service = await get_token_exchange_service()
return await service.exchange_token_for_delegation(
flow1_token=flow1_token,
requested_scopes=requested_scopes,
requested_audience=requested_audience,
)
async def exchange_token_for_audience(
subject_token: str,
requested_audience: str = "nextcloud",
requested_scopes: list[str] | None = None,
) -> Tuple[str, int]:
"""Convenience function for pure RFC 8693 token exchange (no refresh tokens).
Use this for ALL MCP tool calls (request-time operations).
Args:
subject_token: Token being exchanged (from MCP client)
requested_audience: Target audience (usually "nextcloud")
requested_scopes: Optional scopes (may not be supported by all IdPs)
Returns:
Tuple of (access_token, expires_in)
"""
service = await get_token_exchange_service()
return await service.exchange_token_for_audience(
subject_token=subject_token,
requested_audience=requested_audience,
requested_scopes=requested_scopes,
)
+482
View File
@@ -0,0 +1,482 @@
"""Token verification using Nextcloud OIDC userinfo endpoint."""
import logging
import time
from typing import Any
import httpx
import jwt
from jwt import PyJWKClient
from mcp.server.auth.provider import AccessToken, TokenVerifier
logger = logging.getLogger(__name__)
class NextcloudTokenVerifier(TokenVerifier):
"""
Validates access tokens using JWT verification with JWKS or userinfo endpoint fallback.
This verifier supports both JWT and opaque tokens:
1. For JWT tokens: Verifies signature with JWKS and extracts scopes from payload
2. For opaque tokens: Falls back to userinfo endpoint validation
3. Caches successful responses to avoid repeated API calls/verifications
JWT validation provides:
- Faster validation (no HTTP call needed)
- Direct scope extraction from token payload
- Signature verification using JWKS
Userinfo fallback provides:
- Support for opaque tokens
- Backward compatibility
- Additional validation layer
"""
def __init__(
self,
nextcloud_host: str,
userinfo_uri: str,
jwks_uri: str | None = None,
issuer: str | None = None,
introspection_uri: str | None = None,
client_id: str | None = None,
client_secret: str | None = None,
cache_ttl: int = 3600,
):
"""
Initialize the token verifier.
Args:
nextcloud_host: Base URL of the Nextcloud instance (e.g., https://cloud.example.com)
userinfo_uri: Full URL to the userinfo endpoint
jwks_uri: Full URL to the JWKS endpoint (for JWT verification)
issuer: Expected issuer claim value (for JWT verification)
introspection_uri: Full URL to the introspection endpoint (for opaque tokens)
client_id: OAuth client ID (required for introspection)
client_secret: OAuth client secret (required for introspection)
cache_ttl: Time-to-live for cached tokens in seconds (default: 3600)
"""
self.nextcloud_host = nextcloud_host.rstrip("/")
self.userinfo_uri = userinfo_uri
self.jwks_uri = jwks_uri
self.issuer = issuer
self.introspection_uri = introspection_uri
self.client_id = client_id
self.client_secret = client_secret
self.cache_ttl = cache_ttl
# Cache: token -> (userinfo, expiry_timestamp)
self._token_cache: dict[str, tuple[dict[str, Any], float]] = {}
# HTTP client for userinfo/introspection requests
self._client = httpx.AsyncClient(timeout=10.0)
# PyJWKClient for JWT verification (lazy initialization)
self._jwks_client: PyJWKClient | None = None
if jwks_uri:
logger.info(f"JWT verification enabled with JWKS URI: {jwks_uri}")
self._jwks_client = PyJWKClient(jwks_uri, cache_keys=True)
# Introspection support
if introspection_uri and client_id and client_secret:
logger.info(f"Token introspection enabled: {introspection_uri}")
elif introspection_uri:
logger.warning(
"Introspection URI provided but missing client credentials - introspection disabled"
)
async def verify_token(self, token: str) -> AccessToken | None:
"""
Verify a bearer token using JWT verification, introspection, or userinfo endpoint.
This method:
1. Checks the cache first for recent validations
2. Attempts JWT verification if JWKS is configured and token looks like JWT
3. Falls back to introspection for opaque tokens (if configured)
4. Falls back to userinfo endpoint as last resort
5. Returns AccessToken with username and scopes
Args:
token: The bearer token to verify
Returns:
AccessToken if valid, None if invalid or expired
"""
# Check cache first
cached = self._get_cached_token(token)
if cached:
logger.debug("Token found in cache")
return cached
# Try JWT verification first if enabled and token looks like JWT
is_jwt_format = self._is_jwt_format(token)
logger.debug(
f"Token format check: is_jwt_format={is_jwt_format}, _jwks_client={self._jwks_client is not None}"
)
if self._jwks_client and is_jwt_format:
logger.debug("Attempting JWT verification...")
jwt_result = self._verify_jwt(token)
if jwt_result:
logger.info("Token validated via JWT verification")
return jwt_result
else:
logger.warning("JWT verification failed, will try other methods")
# For opaque tokens, try introspection if available
if self.introspection_uri and self.client_id and self.client_secret:
logger.debug("Attempting token introspection...")
try:
introspection_result = await self._verify_via_introspection(token)
if introspection_result:
logger.info("Token validated via introspection")
return introspection_result
except Exception as e:
logger.warning(f"Introspection failed: {e}")
# Fall back to userinfo endpoint validation (last resort)
logger.debug("Attempting userinfo endpoint validation...")
try:
return await self._verify_via_userinfo(token)
except Exception as e:
logger.warning(f"Token verification failed: {e}")
return None
def _is_jwt_format(self, token: str) -> bool:
"""
Check if token looks like a JWT (has 3 parts separated by dots).
Args:
token: The token to check
Returns:
True if token appears to be JWT format
"""
return "." in token and token.count(".") == 2
def _verify_jwt(self, token: str) -> AccessToken | None:
"""
Verify JWT token with signature validation using JWKS.
Args:
token: The JWT token to verify
Returns:
AccessToken if valid, None if invalid
"""
try:
# Get signing key from JWKS
signing_key = self._jwks_client.get_signing_key_from_jwt(token)
# Verify and decode JWT
payload = jwt.decode(
token,
signing_key.key,
algorithms=["RS256"],
issuer=self.issuer,
options={
"verify_signature": True,
"verify_exp": True,
"verify_iat": True,
"verify_iss": True if self.issuer else False,
"verify_aud": False, # Skip audience validation for Bearer tokens
},
)
logger.debug(f"JWT verified successfully for user: {payload.get('sub')}")
logger.debug(f"Full JWT payload: {payload}")
# Extract username (sub claim)
username = payload.get("sub")
if not username:
logger.error("No 'sub' claim found in JWT payload")
return None
# Extract scopes from scope claim (space-separated string)
scope_string = payload.get("scope", "")
scopes = scope_string.split() if scope_string else []
logger.debug(
f"Extracted scopes from JWT - scope claim: '{scope_string}' -> scopes list: {scopes}"
)
# Extract expiration
exp = payload.get("exp")
if not exp:
logger.warning("No 'exp' claim in JWT, using default TTL")
exp = int(time.time() + self.cache_ttl)
# Cache the result
userinfo = {
"sub": username,
"scope": scope_string,
**{k: v for k, v in payload.items() if k not in ["sub", "scope"]},
}
self._token_cache[token] = (userinfo, exp)
return AccessToken(
token=token,
client_id=payload.get("client_id", ""),
scopes=scopes,
expires_at=exp,
resource=username, # Store username in resource field (RFC 8707)
)
except jwt.ExpiredSignatureError:
logger.info("JWT token has expired")
return None
except jwt.InvalidIssuerError as e:
logger.warning(f"JWT issuer validation failed: {e}")
return None
except jwt.InvalidTokenError as e:
logger.warning(f"JWT validation failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error during JWT verification: {e}")
return None
async def _verify_via_introspection(self, token: str) -> AccessToken | None:
"""
Validate token by calling the introspection endpoint (RFC 7662).
This method validates opaque tokens and retrieves their scopes.
Args:
token: The bearer token to introspect
Returns:
AccessToken if active, None if inactive or invalid
"""
try:
# Introspection requires client authentication
response = await self._client.post(
self.introspection_uri,
data={"token": token},
auth=(self.client_id, self.client_secret),
)
if response.status_code == 200:
introspection_data = response.json()
# Check if token is active
if not introspection_data.get("active", False):
logger.info("Token introspection returned inactive=false")
return None
logger.debug(
f"Token introspected successfully for user: {introspection_data.get('sub')}"
)
# Extract username
username = introspection_data.get("sub") or introspection_data.get(
"username"
)
if not username:
logger.error("No username found in introspection response")
return None
# Extract scopes (space-separated string)
scope_string = introspection_data.get("scope", "")
scopes = scope_string.split() if scope_string else []
logger.debug(f"Extracted scopes from introspection: {scopes}")
# Extract expiration
exp = introspection_data.get("exp")
if exp:
expiry = float(exp)
else:
logger.warning(
"No 'exp' in introspection response, using default TTL"
)
expiry = time.time() + self.cache_ttl
# Cache the result
cache_data = {
"sub": username,
"scope": scope_string,
**{
k: v
for k, v in introspection_data.items()
if k not in ["sub", "scope", "active"]
},
}
self._token_cache[token] = (cache_data, expiry)
return AccessToken(
token=token,
client_id=introspection_data.get("client_id", ""),
scopes=scopes,
expires_at=int(expiry),
resource=username,
)
elif response.status_code in (400, 401, 403):
logger.warning(
f"Token introspection failed: HTTP {response.status_code}. "
f"This may indicate: (1) Client credentials mismatch - trying to introspect "
f"token issued to different OAuth client, (2) Expired client credentials, "
f"(3) Invalid token. Will fall back to userinfo endpoint. "
f"Response: {response.text[:200] if response.text else 'empty'}"
)
return None
else:
logger.warning(
f"Unexpected response from introspection: {response.status_code}. "
f"Response: {response.text[:200] if response.text else 'empty'}"
)
return None
except httpx.TimeoutException:
logger.error("Timeout while introspecting token")
return None
except httpx.RequestError as e:
logger.error(f"Network error while introspecting token: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error during token introspection: {e}")
return None
async def _verify_via_userinfo(self, token: str) -> AccessToken | None:
"""
Validate token by calling the userinfo endpoint.
Args:
token: The bearer token to verify
Returns:
AccessToken if valid, None otherwise
"""
try:
response = await self._client.get(
self.userinfo_uri, headers={"Authorization": f"Bearer {token}"}
)
if response.status_code == 200:
userinfo = response.json()
logger.debug(
f"Token validated successfully for user: {userinfo.get('sub')}"
)
# Cache the result
expiry = time.time() + self.cache_ttl
self._token_cache[token] = (userinfo, expiry)
# Create AccessToken with username in resource field (workaround for MCP SDK)
username = userinfo.get("sub") or userinfo.get("preferred_username")
if not username:
logger.error("No username found in userinfo response")
return None
return AccessToken(
token=token,
client_id="", # Not available from userinfo
scopes=self._extract_scopes(userinfo),
expires_at=int(expiry),
resource=username, # Store username in resource field (RFC 8707)
)
elif response.status_code in (400, 401, 403):
logger.info(f"Token validation failed: HTTP {response.status_code}")
return None
else:
logger.warning(
f"Unexpected response from userinfo: {response.status_code}"
)
return None
except httpx.TimeoutException:
logger.error("Timeout while validating token via userinfo endpoint")
return None
except httpx.RequestError as e:
logger.error(f"Network error while validating token: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error during token validation: {e}")
return None
def _get_cached_token(self, token: str) -> AccessToken | None:
"""
Retrieve a token from cache if not expired.
Args:
token: The bearer token to look up
Returns:
AccessToken if cached and valid, None otherwise
"""
if token not in self._token_cache:
return None
userinfo, expiry = self._token_cache[token]
# Check if expired
if time.time() >= expiry:
logger.debug("Cached token expired, removing from cache")
del self._token_cache[token]
return None
# Return cached AccessToken
username = userinfo.get("sub") or userinfo.get("preferred_username")
return AccessToken(
token=token,
client_id="",
scopes=self._extract_scopes(userinfo),
expires_at=int(expiry),
resource=username,
)
def _extract_scopes(self, userinfo: dict[str, Any]) -> list[str]:
"""
Extract scopes from userinfo response.
First attempts to read actual scopes from the 'scope' field (RFC 8693).
If not present, infers scopes from the claims present in the response.
Args:
userinfo: The userinfo response dictionary
Returns:
List of scopes (actual or inferred)
"""
# Try to get actual scopes from userinfo response (if OIDC provider includes it)
scope_string = userinfo.get("scope")
if scope_string:
scopes = scope_string.split() if isinstance(scope_string, str) else []
if scopes:
logger.debug(
f"Using actual scopes from userinfo: {scopes} (scope field present)"
)
return scopes
# Fallback: Infer scopes from claims present in response
# This maintains backward compatibility with OIDC providers that don't
# include the scope field in userinfo responses
logger.debug(
"No scope field in userinfo response, inferring scopes from claims"
)
scopes = ["openid"] # Always present
if "email" in userinfo:
scopes.append("email")
if any(
key in userinfo for key in ["name", "given_name", "family_name", "picture"]
):
scopes.append("profile")
if "roles" in userinfo:
scopes.append("roles")
if "groups" in userinfo:
scopes.append("groups")
logger.debug(f"Inferred scopes from userinfo claims: {scopes}")
return scopes
def clear_cache(self):
"""Clear the token cache."""
self._token_cache.clear()
logger.debug("Token cache cleared")
async def close(self):
"""Cleanup resources."""
await self._client.aclose()
logger.debug("Token verifier closed")
@@ -1,442 +0,0 @@
"""
Unified Token Verifier for ADR-005 Token Audience Validation.
This module replaces both NextcloudTokenVerifier and ProgressiveConsentTokenVerifier
with a single implementation that supports two compliant OAuth modes:
1. Multi-audience mode (default): Validates MCP audience per RFC 7519 (resource servers
validate only their own audience). Nextcloud independently validates its own audience.
2. Token exchange mode (opt-in): Tokens have MCP audience only, exchanged for Nextcloud tokens
Key Design Principles:
- Token verification happens HERE (validates MCP audience per OAuth spec)
- Token exchange happens in context_helper.py (when creating NextcloudClient)
- No token passthrough allowed (complies with MCP Security Specification)
- Token reuse IS allowed for multi-audience tokens (RFC 8707)
"""
import hashlib
import logging
import time
from typing import Any
import httpx
import jwt
from jwt import PyJWKClient
from mcp.server.auth.provider import AccessToken, TokenVerifier
from nextcloud_mcp_server.config import Settings
from nextcloud_mcp_server.observability.metrics import (
oauth_token_cache_hits_total,
record_oauth_token_validation,
)
logger = logging.getLogger(__name__)
class UnifiedTokenVerifier(TokenVerifier):
"""
Unified token verifier supporting both multi-audience and token exchange modes.
Compliant with MCP security specification - no token pass-through.
This verifier:
1. Validates tokens using JWT verification with JWKS or introspection fallback
2. Enforces proper audience validation based on configured mode
3. Caches successful validations to avoid repeated API calls
Mode Selection (via ENABLE_TOKEN_EXCHANGE setting):
- False/omit (default): Multi-audience mode - validates MCP audience only (per RFC 7519).
Nextcloud independently validates its own audience when receiving API calls.
- True: Exchange mode - requires MCP audience only, then exchanges for Nextcloud token
"""
def __init__(self, settings: Settings):
"""
Initialize the unified token verifier.
Args:
settings: Application settings containing OAuth configuration
"""
self.settings = settings
self.mode = "exchange" if settings.enable_token_exchange else "multi-audience"
# Common components for all modes
self.http_client = httpx.AsyncClient(timeout=10.0)
# JWT verification support
self.jwks_client: PyJWKClient | None = None
if hasattr(settings, "jwks_uri") and settings.jwks_uri:
logger.info(f"JWT verification enabled with JWKS URI: {settings.jwks_uri}")
self.jwks_client = PyJWKClient(settings.jwks_uri, cache_keys=True)
# Introspection support (for opaque tokens)
self.introspection_uri: str | None = None
if (
hasattr(settings, "introspection_uri")
and settings.introspection_uri
and settings.oidc_client_id
and settings.oidc_client_secret
):
self.introspection_uri = settings.introspection_uri
logger.info(f"Token introspection enabled: {self.introspection_uri}")
# Token cache: token_hash -> (userinfo, expiry_timestamp)
self._token_cache: dict[str, tuple[dict[str, Any], float]] = {}
self.cache_ttl = 3600 # 1 hour default
logger.info(
f"UnifiedTokenVerifier initialized in {self.mode} mode. "
f"MCP audience: {settings.oidc_client_id} or {settings.nextcloud_mcp_server_url}, "
f"Nextcloud resource URI: {settings.nextcloud_resource_uri}"
)
async def verify_token(self, token: str) -> AccessToken | None:
"""
Verify token according to MCP TokenVerifier protocol.
Per RFC 7519, we validate only MCP audience. The mode determines what
happens AFTER verification in context_helper.py:
- Multi-audience mode: Use token directly (Nextcloud validates its own audience)
- Exchange mode: Exchange for Nextcloud-audience token via RFC 8693
Args:
token: Bearer token to verify
Returns:
AccessToken if valid with MCP audience, None otherwise
"""
# Check cache first
cached = self._get_cached_token(token)
if cached:
logger.debug("Token found in cache")
oauth_token_cache_hits_total.labels(hit="true").inc()
return cached
oauth_token_cache_hits_total.labels(hit="false").inc()
# Both modes do the same validation (MCP audience only)
return await self._verify_mcp_audience(token)
async def _verify_mcp_audience(self, token: str) -> AccessToken | None:
"""
Validate token has MCP audience.
Per RFC 7519 Section 4.1.3, resource servers validate only their own
presence in the audience claim. We don't validate Nextcloud's audience -
that's Nextcloud's responsibility when it receives the token.
Args:
token: Bearer token to verify
Returns:
AccessToken if valid with MCP audience, None otherwise
"""
validation_method = "unknown"
try:
# Attempt JWT verification first
if self._is_jwt_format(token) and self.jwks_client:
validation_method = "jwt"
payload = await self._verify_jwt_signature(token)
if payload:
record_oauth_token_validation("jwt", "valid")
else:
record_oauth_token_validation("jwt", "invalid")
else:
# Fall back to introspection for opaque tokens
validation_method = "introspect"
payload = await self._introspect_token(token)
if payload:
record_oauth_token_validation("introspect", "valid")
else:
record_oauth_token_validation("introspect", "invalid")
if not payload:
return None
# Check payload is valid
if not payload:
return None
# Validate MCP audience is present
if not self._has_mcp_audience(payload):
audiences = payload.get("aud", [])
logger.error(
f"Token rejected: Missing MCP audience. "
f"Got {audiences}, need MCP ({self.settings.oidc_client_id} or "
f"{self.settings.nextcloud_mcp_server_url})"
)
# Record as invalid due to audience mismatch
record_oauth_token_validation(validation_method, "invalid")
return None
# Log based on mode for clarity
if self.mode == "multi-audience":
logger.info(
"MCP audience validated - token can be used directly "
"(Nextcloud will validate its own audience)"
)
else:
logger.info(
"MCP audience validated - token will be exchanged for Nextcloud access"
)
return self._create_access_token(token, payload)
except Exception as e:
logger.error(f"Token verification failed: {e}")
record_oauth_token_validation(validation_method, "error")
return None
def _has_mcp_audience(self, payload: dict[str, Any]) -> bool:
"""
Check if token has MCP audience.
Per RFC 7519 Section 4.1.3, resource servers should only validate their own
presence in the audience claim. We don't validate Nextcloud's audience - that's
Nextcloud's responsibility when it receives the token.
Args:
payload: Decoded token payload
Returns:
True if MCP audience present, False otherwise
"""
audiences = payload.get("aud", [])
if isinstance(audiences, str):
audiences = [audiences]
audiences_set = set(audiences)
# MCP must have at least one: client_id OR server_url OR server_url/mcp
return bool(
self.settings.oidc_client_id in audiences_set
or (
self.settings.nextcloud_mcp_server_url
and (
self.settings.nextcloud_mcp_server_url in audiences_set
or f"{self.settings.nextcloud_mcp_server_url}/mcp" in audiences_set
)
)
)
def _is_jwt_format(self, token: str) -> bool:
"""
Check if token looks like a JWT (has 3 parts separated by dots).
Args:
token: The token to check
Returns:
True if token appears to be JWT format
"""
return "." in token and token.count(".") == 2
async def _verify_jwt_signature(self, token: str) -> dict[str, Any] | None:
"""
Verify JWT token with signature validation using JWKS.
Args:
token: JWT token to verify
Returns:
Decoded payload if valid, None if invalid
"""
try:
assert self.jwks_client is not None # Caller should check before calling
# Get signing key from JWKS
signing_key = self.jwks_client.get_signing_key_from_jwt(token)
# Verify and decode JWT
# Note: We don't validate audience here - that's done separately based on mode
payload = jwt.decode(
token,
signing_key.key,
algorithms=["RS256"],
issuer=(
self.settings.oidc_issuer
if hasattr(self.settings, "oidc_issuer")
else None
),
options={
"verify_signature": True,
"verify_exp": True,
"verify_iat": True,
"verify_iss": (
True
if hasattr(self.settings, "oidc_issuer")
and self.settings.oidc_issuer
else False
),
"verify_aud": False, # We handle audience validation separately
},
)
logger.debug(f"JWT signature verified for user: {payload.get('sub')}")
return payload
except jwt.ExpiredSignatureError:
logger.info("JWT token has expired")
return None
except jwt.InvalidIssuerError as e:
logger.warning(f"JWT issuer validation failed: {e}")
return None
except jwt.InvalidTokenError as e:
logger.warning(f"JWT validation failed: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error during JWT verification: {e}")
return None
async def _introspect_token(self, token: str) -> dict[str, Any] | None:
"""
Validate token by calling the introspection endpoint (RFC 7662).
Args:
token: Bearer token to introspect
Returns:
Token payload if active, None if inactive or invalid
"""
if not self.introspection_uri:
logger.debug("No introspection endpoint configured")
return None
try:
# Introspection requires client authentication
response = await self.http_client.post(
self.introspection_uri,
data={"token": token},
auth=(self.settings.oidc_client_id, self.settings.oidc_client_secret),
)
if response.status_code == 200:
introspection_data = response.json()
# Check if token is active
if not introspection_data.get("active", False):
logger.info("Token introspection returned inactive=false")
return None
logger.debug(
f"Token introspected successfully for user: {introspection_data.get('sub')}"
)
return introspection_data
elif response.status_code in (400, 401, 403):
logger.warning(
f"Token introspection failed: HTTP {response.status_code}. "
f"Response: {response.text[:200] if response.text else 'empty'}"
)
return None
else:
logger.warning(
f"Unexpected response from introspection: {response.status_code}. "
f"Response: {response.text[:200] if response.text else 'empty'}"
)
return None
except httpx.TimeoutException:
logger.error("Timeout while introspecting token")
return None
except httpx.RequestError as e:
logger.error(f"Network error while introspecting token: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error during token introspection: {e}")
return None
def _create_access_token(
self, token: str, payload: dict[str, Any]
) -> AccessToken | None:
"""
Create AccessToken object from validated token payload.
Args:
token: The bearer token
payload: Validated token payload
Returns:
AccessToken object or None if required fields missing
"""
# Extract username (sub claim, with fallback to preferred_username)
username = payload.get("sub") or payload.get("preferred_username")
if not username:
logger.error(
"No 'sub' or 'preferred_username' claim found in token payload"
)
return None
# Extract scopes from scope claim (space-separated string)
scope_string = payload.get("scope", "")
scopes = scope_string.split() if scope_string else []
logger.debug(
f"Extracted scopes from token - scope claim: '{scope_string}' -> scopes list: {scopes}"
)
# Extract expiration
exp = payload.get("exp")
if not exp:
logger.warning("No 'exp' claim in token, using default TTL")
exp = int(time.time() + self.cache_ttl)
# Cache the result
token_hash = hashlib.sha256(token.encode()).hexdigest()
userinfo = {
"sub": username,
"scope": scope_string,
**{k: v for k, v in payload.items() if k not in ["sub", "scope"]},
}
self._token_cache[token_hash] = (userinfo, exp)
return AccessToken(
token=token,
client_id=payload.get("client_id", ""),
scopes=scopes,
expires_at=exp,
resource=username, # Store username in resource field (RFC 8707)
)
def _get_cached_token(self, token: str) -> AccessToken | None:
"""
Retrieve a token from cache if not expired.
Args:
token: The bearer token to look up
Returns:
AccessToken if cached and valid, None otherwise
"""
token_hash = hashlib.sha256(token.encode()).hexdigest()
if token_hash not in self._token_cache:
return None
userinfo, expiry = self._token_cache[token_hash]
# Check if expired
if time.time() >= expiry:
logger.debug("Cached token expired, removing from cache")
del self._token_cache[token_hash]
return None
# Return cached AccessToken
username = userinfo.get("sub") or userinfo.get("preferred_username")
scope_string = userinfo.get("scope", "")
scopes = scope_string.split() if scope_string else []
return AccessToken(
token=token,
client_id=userinfo.get("client_id", ""),
scopes=scopes,
expires_at=int(expiry),
resource=username,
)
def clear_cache(self):
"""Clear the token cache."""
self._token_cache.clear()
logger.debug("Token cache cleared")
async def close(self):
"""Cleanup resources."""
await self.http_client.aclose()
logger.debug("Unified token verifier closed")
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@@ -1,610 +0,0 @@
"""Vector visualization routes for testing search algorithms.
Provides a web UI for users to test different search algorithms on their own
indexed documents and visualize results in 2D space using PCA.
All processing happens server-side following ADR-012:
- Search execution via shared search/algorithms.py
- PCA dimensionality reduction (768-dim 2D)
- Only 2D coordinates + metadata sent to client
- Bandwidth-efficient (2 floats per doc vs 768)
"""
import logging
import numpy as np
from starlette.authentication import requires
from starlette.requests import Request
from starlette.responses import HTMLResponse, JSONResponse
from nextcloud_mcp_server.config import get_settings
from nextcloud_mcp_server.search import (
FuzzySearchAlgorithm,
HybridSearchAlgorithm,
KeywordSearchAlgorithm,
SemanticSearchAlgorithm,
)
from nextcloud_mcp_server.vector.pca import PCA
from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
logger = logging.getLogger(__name__)
@requires("authenticated", redirect="oauth_login")
async def vector_visualization_html(request: Request) -> HTMLResponse:
"""Vector visualization page with search controls and interactive plot.
Provides UI for testing search algorithms with real-time visualization.
Requires vector sync to be enabled.
Args:
request: Starlette request object
Returns:
HTML page with search interface
"""
settings = get_settings()
if not settings.vector_sync_enabled:
return HTMLResponse(
"""
<div>
<h2>Vector Visualization</h2>
<div style="padding: 20px; background: #fff3cd; border: 1px solid #ffc107; border-radius: 4px;">
Vector sync is not enabled. Set VECTOR_SYNC_ENABLED=true to use this feature.
</div>
</div>
"""
)
# Get user info from auth context
username = (
request.user.display_name
if hasattr(request.user, "display_name")
else "unknown"
)
html_content = f"""
<style>
.viz-card {{
background: white;
border-radius: 8px;
padding: 20px;
margin-bottom: 20px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}}
.viz-controls {{
margin-bottom: 20px;
}}
.viz-control-row {{
display: grid;
grid-template-columns: 2fr 1fr auto;
gap: 12px;
margin-bottom: 12px;
align-items: end;
}}
.viz-control-group {{
margin-bottom: 15px;
}}
.viz-control-group label {{
display: block;
margin-bottom: 5px;
font-weight: 500;
color: #333;
}}
.viz-control-group input[type="text"],
.viz-control-group input[type="number"],
.viz-control-group select {{
width: 100%;
padding: 8px 12px;
border: 1px solid #ddd;
border-radius: 4px;
font-size: 14px;
}}
.viz-control-group input[type="range"] {{
width: 100%;
}}
.viz-control-group select[multiple] {{
min-height: 100px;
}}
.viz-weight-display {{
display: inline-block;
min-width: 40px;
text-align: right;
color: #666;
}}
.viz-btn {{
background: #0066cc;
color: white;
border: none;
padding: 10px 20px;
border-radius: 4px;
cursor: pointer;
font-size: 14px;
font-weight: 500;
}}
.viz-btn:hover {{
background: #0052a3;
}}
.viz-btn-secondary {{
background: #6c757d;
color: white;
border: none;
padding: 6px 12px;
border-radius: 4px;
cursor: pointer;
font-size: 13px;
margin-bottom: 12px;
}}
.viz-btn-secondary:hover {{
background: #5a6268;
}}
#viz-plot-container {{
width: 100%;
height: 600px;
position: relative;
}}
#viz-plot {{
width: 100%;
height: 100%;
}}
.viz-loading {{
text-align: center;
padding: 40px;
color: #666;
}}
.viz-loading-overlay {{
position: absolute;
inset: 0;
display: flex;
align-items: center;
justify-content: center;
background: white;
color: #666;
}}
.viz-no-results {{
text-align: center;
padding: 40px;
color: #666;
font-style: italic;
}}
.viz-advanced-section {{
margin-top: 16px;
padding: 16px;
background: #f8f9fa;
border-radius: 4px;
border: 1px solid #dee2e6;
}}
.viz-advanced-grid {{
display: grid;
grid-template-columns: 1fr 1fr;
gap: 20px;
}}
.viz-info-box {{
background: #e3f2fd;
border-left: 4px solid #2196f3;
padding: 12px;
margin-bottom: 20px;
font-size: 14px;
}}
</style>
<div x-data="vizApp()">
<div class="viz-card">
<h2>Vector Visualization</h2>
<div class="viz-info-box">
Testing search algorithms on your indexed documents. User: <strong>{username}</strong>
</div>
<form @submit.prevent="executeSearch">
<div class="viz-controls">
<!-- Main Controls -->
<div class="viz-control-group">
<label>Search Query</label>
<input type="text" x-model="query" placeholder="Enter search query..." required />
</div>
<div class="viz-control-row">
<div class="viz-control-group" style="margin-bottom: 0;">
<label>Algorithm</label>
<select x-model="algorithm">
<option value="semantic">Semantic (Vector Similarity)</option>
<option value="keyword">Keyword (Token Matching)</option>
<option value="fuzzy">Fuzzy (Character Overlap)</option>
<option value="hybrid" selected>Hybrid (RRF Fusion)</option>
</select>
</div>
<div style="display: flex; align-items: flex-end;">
<button type="submit" class="viz-btn" style="width: 100%;">Search & Visualize</button>
</div>
<div style="display: flex; align-items: flex-end;">
<button type="button" class="viz-btn-secondary" @click="showAdvanced = !showAdvanced" style="white-space: nowrap;">
<span x-text="showAdvanced ? 'Hide Advanced' : 'Advanced'"></span>
</button>
</div>
</div>
<!-- Advanced Options (Collapsible) -->
<div class="viz-advanced-section" x-show="showAdvanced" x-transition.opacity.duration.200ms>
<h3 style="margin-top: 0; margin-bottom: 16px; font-size: 16px;">Advanced Options</h3>
<div class="viz-advanced-grid">
<div class="viz-control-group">
<label>Document Types</label>
<select x-model="docTypes" multiple>
<option value="">All Types (cross-app search)</option>
<option value="note">Notes</option>
<option value="file">Files</option>
<option value="calendar">Calendar Events</option>
<option value="contact">Contacts</option>
<option value="deck">Deck Cards</option>
</select>
<small style="color: #666; display: block; margin-top: 4px;">
Hold Ctrl/Cmd to select multiple
</small>
</div>
<div>
<div class="viz-control-group">
<label>Score Threshold (Semantic/Hybrid)</label>
<input type="number" x-model.number="scoreThreshold" min="0" max="1" step="0.1" />
</div>
<div class="viz-control-group">
<label>Result Limit</label>
<input type="number" x-model.number="limit" min="1" max="100" />
</div>
</div>
</div>
<!-- Hybrid Weights (only when hybrid selected) -->
<div x-show="algorithm === 'hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
<label style="margin-bottom: 12px; display: block;">Hybrid Algorithm Weights</label>
<div style="margin-bottom: 8px;">
<label style="display: inline-block; width: 100px; font-weight: normal;">Semantic:</label>
<input type="range" x-model.number="semanticWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
<span class="viz-weight-display" x-text="semanticWeight.toFixed(1)"></span>
</div>
<div style="margin-bottom: 8px;">
<label style="display: inline-block; width: 100px; font-weight: normal;">Keyword:</label>
<input type="range" x-model.number="keywordWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
<span class="viz-weight-display" x-text="keywordWeight.toFixed(1)"></span>
</div>
<div>
<label style="display: inline-block; width: 100px; font-weight: normal;">Fuzzy:</label>
<input type="range" x-model.number="fuzzyWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
<span class="viz-weight-display" x-text="fuzzyWeight.toFixed(1)"></span>
</div>
</div>
</div>
</div>
</form>
</div>
<div class="viz-card">
<div id="viz-plot-container">
<div x-show="loading" class="viz-loading-overlay" x-transition.opacity.duration.200ms>
Executing search and computing PCA projection...
</div>
<div id="viz-plot" x-show="!loading" x-transition.opacity.duration.200ms></div>
</div>
</div>
<div class="viz-card">
<h3>Search Results (<span x-text="loading ? '...' : results.length"></span>)</h3>
<div x-show="loading" class="viz-loading" x-transition.opacity.duration.200ms>
Loading results...
</div>
<div x-show="!loading && results.length === 0" class="viz-no-results" x-transition.opacity.duration.200ms>
No results found. Try a different query or adjust your search parameters.
</div>
<template x-if="!loading && results.length > 0">
<div x-transition.opacity.duration.200ms>
<template x-for="result in results" :key="result.id">
<div style="padding: 12px; border-bottom: 1px solid #eee;">
<a :href="getNextcloudUrl(result)" target="_blank" style="font-weight: 500; color: #0066cc; text-decoration: none;">
<span x-text="result.title"></span>
</a>
<div style="font-size: 14px; color: #666; margin-top: 4px;" x-text="result.excerpt"></div>
<div style="font-size: 12px; color: #999; margin-top: 4px;">
Score: <span x-text="result.score.toFixed(3)"></span> |
Type: <span x-text="result.doc_type"></span>
</div>
</div>
</template>
</div>
</template>
</div>
</div>
"""
return HTMLResponse(content=html_content)
@requires("authenticated", redirect="oauth_login")
async def vector_visualization_search(request: Request) -> JSONResponse:
"""Execute server-side search and return 2D coordinates + results.
All processing happens server-side:
1. Execute search via shared algorithm module
2. Fetch matching vectors from Qdrant
3. Apply PCA reduction (768-dim 2D)
4. Return coordinates + metadata only
Args:
request: Starlette request with query parameters
Returns:
JSON response with coordinates_2d and results
"""
settings = get_settings()
if not settings.vector_sync_enabled:
return JSONResponse(
{"success": False, "error": "Vector sync not enabled"},
status_code=400,
)
# Get user info from auth context
username = (
request.user.display_name if hasattr(request.user, "display_name") else None
)
if not username:
return JSONResponse(
{"success": False, "error": "User not authenticated"},
status_code=401,
)
# Parse query parameters
query = request.query_params.get("query", "")
algorithm = request.query_params.get("algorithm", "hybrid")
limit = int(request.query_params.get("limit", "50"))
score_threshold = float(request.query_params.get("score_threshold", "0.7"))
semantic_weight = float(request.query_params.get("semantic_weight", "0.5"))
keyword_weight = float(request.query_params.get("keyword_weight", "0.3"))
fuzzy_weight = float(request.query_params.get("fuzzy_weight", "0.2"))
# Parse doc_types (comma-separated list, None = all types)
doc_types_param = request.query_params.get("doc_types", "")
doc_types = doc_types_param.split(",") if doc_types_param else None
logger.info(
f"Viz search: user={username}, query='{query}', "
f"algorithm={algorithm}, limit={limit}, doc_types={doc_types}"
)
try:
# Get authenticated HTTP client from session
# In BasicAuth mode: uses username/password from session
# In OAuth mode: uses access token from session
from nextcloud_mcp_server.auth.userinfo_routes import (
_get_authenticated_client_for_userinfo,
)
from nextcloud_mcp_server.client.notes import NotesClient
async with await _get_authenticated_client_for_userinfo(request) as http_client:
# Create NotesClient directly with authenticated HTTP client
notes_client = NotesClient(http_client, username)
# Wrap in a minimal client object for search algorithms
# This conforms to NextcloudClientProtocol but only implements notes
class MinimalNextcloudClient:
def __init__(self, notes_client, username):
self._notes = notes_client
self.username = username
@property
def notes(self):
return self._notes
@property
def webdav(self):
return None
@property
def calendar(self):
return None
@property
def contacts(self):
return None
@property
def deck(self):
return None
@property
def cookbook(self):
return None
@property
def tables(self):
return None
nextcloud_client = MinimalNextcloudClient(notes_client, username)
# Create search algorithm
if algorithm == "semantic":
search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
elif algorithm == "keyword":
search_algo = KeywordSearchAlgorithm()
elif algorithm == "fuzzy":
search_algo = FuzzySearchAlgorithm()
elif algorithm == "hybrid":
search_algo = HybridSearchAlgorithm(
semantic_weight=semantic_weight,
keyword_weight=keyword_weight,
fuzzy_weight=fuzzy_weight,
)
else:
return JSONResponse(
{"success": False, "error": f"Unknown algorithm: {algorithm}"},
status_code=400,
)
# Execute search (supports cross-app when doc_types=None)
# Get unverified results with buffer for filtering
all_results = []
if doc_types is None or len(doc_types) == 0:
# Cross-app search - search all indexed types
unverified_results = await search_algo.search(
query=query,
user_id=username,
limit=limit * 2, # Buffer for verification filtering
doc_type=None, # Search all types
score_threshold=score_threshold,
)
all_results.extend(unverified_results)
else:
# Search each document type and combine
for doc_type in doc_types:
unverified_results = await search_algo.search(
query=query,
user_id=username,
limit=limit * 2, # Buffer for verification filtering
doc_type=doc_type,
score_threshold=score_threshold,
)
all_results.extend(unverified_results)
# Sort by score before verification
all_results.sort(key=lambda r: r.score, reverse=True)
# Verify access for all results (deduplicates and filters)
from nextcloud_mcp_server.search.verification import verify_search_results
verified_results = await verify_search_results(
all_results, nextcloud_client
)
search_results = verified_results[:limit]
if not search_results:
return JSONResponse(
{
"success": True,
"results": [],
"coordinates_2d": [],
"message": "No results found",
}
)
# Fetch vectors for matching results from Qdrant
qdrant_client = await get_qdrant_client()
doc_ids = [r.id for r in search_results]
# Retrieve vectors for the matching documents
from qdrant_client.models import FieldCondition, Filter, MatchAny
points_response = await qdrant_client.scroll(
collection_name=settings.get_collection_name(),
scroll_filter=Filter(
must=[
FieldCondition(
key="doc_id",
match=MatchAny(any=[str(doc_id) for doc_id in doc_ids]),
),
FieldCondition(
key="user_id",
match={"value": username},
),
]
),
limit=len(doc_ids) * 2, # Account for multiple chunks per doc
with_vectors=True,
with_payload=["doc_id"], # Need doc_id to map vectors to results
)
points = points_response[0]
if not points:
return JSONResponse(
{
"success": True,
"results": [],
"coordinates_2d": [],
"message": "No vectors found for results",
}
)
# Extract vectors
vectors = np.array([p.vector for p in points if p.vector is not None])
if len(vectors) < 2:
# Not enough points for PCA
return JSONResponse(
{
"success": True,
"results": [
{
"id": r.id,
"doc_type": r.doc_type,
"title": r.title,
"excerpt": r.excerpt,
"score": r.score,
}
for r in search_results
],
"coordinates_2d": [[0, 0]] * len(search_results),
"message": "Not enough vectors for PCA",
}
)
# Apply PCA dimensionality reduction (768-dim → 2D)
pca = PCA(n_components=2)
coords_2d = pca.fit_transform(vectors)
# After fit, these attributes are guaranteed to be set
assert pca.explained_variance_ratio_ is not None
logger.info(
f"PCA explained variance: PC1={pca.explained_variance_ratio_[0]:.3f}, "
f"PC2={pca.explained_variance_ratio_[1]:.3f}"
)
# Map results to coordinates (use first chunk per document)
result_coords = []
seen_doc_ids = set()
for point, coord in zip(points, coords_2d):
if point.payload:
doc_id = int(point.payload.get("doc_id", 0))
if doc_id not in seen_doc_ids and doc_id in doc_ids:
seen_doc_ids.add(doc_id)
result_coords.append(coord.tolist())
# Build response
response_results = [
{
"id": r.id,
"doc_type": r.doc_type,
"title": r.title,
"excerpt": r.excerpt,
"score": r.score,
}
for r in search_results
]
return JSONResponse(
{
"success": True,
"results": response_results,
"coordinates_2d": result_coords[: len(search_results)],
"pca_variance": {
"pc1": float(pca.explained_variance_ratio_[0]),
"pc2": float(pca.explained_variance_ratio_[1]),
},
}
)
except Exception as e:
logger.error(f"Viz search error: {e}", exc_info=True)
return JSONResponse(
{"success": False, "error": str(e)},
status_code=500,
)
-540
View File
@@ -1,540 +0,0 @@
"""Webhook management routes for admin UI.
Provides browser-based endpoints for admin users to manage webhook configurations
using preset templates. Only accessible to Nextcloud administrators.
"""
import logging
import os
import httpx
from starlette.authentication import requires
from starlette.requests import Request
from starlette.responses import HTMLResponse
from nextcloud_mcp_server.auth.permissions import is_nextcloud_admin
from nextcloud_mcp_server.client.webhooks import WebhooksClient
from nextcloud_mcp_server.server.webhook_presets import (
WEBHOOK_PRESETS,
filter_presets_by_installed_apps,
get_preset,
)
logger = logging.getLogger(__name__)
def _get_storage(request: Request):
"""Get storage instance from app state.
Args:
request: Starlette request object
Returns:
RefreshTokenStorage instance or None
"""
# Try browser_app state first (for /app routes)
storage = getattr(request.app.state, "storage", None)
# Try oauth_context if in OAuth mode
if not storage:
oauth_ctx = getattr(request.app.state, "oauth_context", None)
if oauth_ctx:
storage = oauth_ctx.get("storage")
return storage
async def _get_installed_apps(http_client: httpx.AsyncClient) -> list[str]:
"""Get list of installed and enabled apps from Nextcloud capabilities.
Args:
http_client: Authenticated HTTP client
Returns:
List of installed app names (e.g., ["notes", "calendar", "forms"])
"""
try:
response = await http_client.get(
"/ocs/v2.php/cloud/capabilities",
headers={"OCS-APIRequest": "true", "Accept": "application/json"},
)
response.raise_for_status()
data = response.json()
# Extract app names from capabilities
capabilities = data.get("ocs", {}).get("data", {}).get("capabilities", {})
# Filter out core NC capabilities (not apps)
core_keys = {"version", "core"}
app_keys = set(capabilities.keys()) - core_keys
return sorted(app_keys)
except Exception as e:
logger.warning(f"Failed to get installed apps from capabilities: {e}")
return []
def _get_webhook_uri() -> str:
"""Get the webhook endpoint URI for this MCP server.
This function determines the correct webhook URL based on the environment:
1. Uses WEBHOOK_INTERNAL_URL if explicitly set (highest priority)
2. Detects Docker environment and uses internal service name
3. Falls back to NEXTCLOUD_MCP_SERVER_URL
In Docker environments, Nextcloud needs to reach the MCP service using
the internal Docker network hostname (e.g., http://mcp:8000), not localhost.
Returns:
Full webhook endpoint URL accessible from Nextcloud
"""
# Explicit override (highest priority)
webhook_url = os.getenv("WEBHOOK_INTERNAL_URL")
if webhook_url:
return f"{webhook_url}/webhooks/nextcloud"
# Detect Docker environment
# Check for common Docker indicators
is_docker = (
os.path.exists("/.dockerenv") # Docker container marker file
or os.path.exists("/run/.containerenv") # Podman marker
or os.getenv("DOCKER_CONTAINER") == "true" # Explicit flag
)
if is_docker:
# In Docker, use internal service name from NEXTCLOUD_MCP_SERVICE_NAME
# or default to 'mcp' (docker-compose service name)
service_name = os.getenv("NEXTCLOUD_MCP_SERVICE_NAME", "mcp")
port = os.getenv("NEXTCLOUD_MCP_PORT", "8000")
logger.debug(
f"Docker environment detected, using internal URL: http://{service_name}:{port}"
)
return f"http://{service_name}:{port}/webhooks/nextcloud"
# Fallback to configured server URL (for non-Docker deployments)
server_url = os.getenv("NEXTCLOUD_MCP_SERVER_URL", "http://localhost:8000")
return f"{server_url}/webhooks/nextcloud"
async def _get_authenticated_client(request: Request) -> httpx.AsyncClient:
"""Get an authenticated HTTP client for Nextcloud API calls.
Args:
request: Starlette request object
Returns:
Authenticated httpx.AsyncClient
Raises:
RuntimeError: If unable to create authenticated client
"""
# Get OAuth context from app state
oauth_ctx = getattr(request.app.state, "oauth_context", None)
# BasicAuth mode - use credentials from environment
if not oauth_ctx:
nextcloud_host = os.getenv("NEXTCLOUD_HOST")
username = os.getenv("NEXTCLOUD_USERNAME")
password = os.getenv("NEXTCLOUD_PASSWORD")
if not all([nextcloud_host, username, password]):
raise RuntimeError("BasicAuth credentials not configured")
assert nextcloud_host is not None # Type narrowing for type checker
return httpx.AsyncClient(
base_url=nextcloud_host,
auth=(username, password),
timeout=30.0,
)
# OAuth mode - get token from session
storage = oauth_ctx.get("storage")
session_id = request.cookies.get("mcp_session")
if not storage or not session_id:
raise RuntimeError("Session not found")
token_data = await storage.get_refresh_token(session_id)
if not token_data or "access_token" not in token_data:
raise RuntimeError("No access token found in session")
access_token = token_data["access_token"]
nextcloud_host = oauth_ctx.get("config", {}).get("nextcloud_host", "")
if not nextcloud_host:
raise RuntimeError("Nextcloud host not configured")
return httpx.AsyncClient(
base_url=nextcloud_host,
headers={"Authorization": f"Bearer {access_token}"},
timeout=30.0,
)
async def _get_enabled_presets(
webhooks_client: WebhooksClient,
storage=None,
) -> dict[str, list[int]]:
"""Get currently enabled webhook presets.
Reads from database first for better performance. Falls back to API if needed.
Args:
webhooks_client: Webhooks API client
storage: Optional RefreshTokenStorage instance
Returns:
Dictionary mapping preset_id to list of webhook IDs
"""
try:
# Try database first (faster, works offline)
if storage:
all_webhooks = await storage.list_all_webhooks()
enabled_presets: dict[str, list[int]] = {}
for webhook in all_webhooks:
preset_id = webhook["preset_id"]
webhook_id = webhook["webhook_id"]
if preset_id not in enabled_presets:
enabled_presets[preset_id] = []
enabled_presets[preset_id].append(webhook_id)
return enabled_presets
# Fallback to API query
registered_webhooks = await webhooks_client.list_webhooks()
webhook_uri = _get_webhook_uri()
# Group webhooks by preset based on matching events
enabled_presets: dict[str, list[int]] = {}
for preset_id, preset in WEBHOOK_PRESETS.items():
preset_event_classes = {event["event"] for event in preset["events"]}
matching_webhooks = []
for webhook in registered_webhooks:
# Check if webhook matches this preset
if (
webhook.get("uri") == webhook_uri
and webhook.get("event") in preset_event_classes
):
matching_webhooks.append(webhook["id"])
if matching_webhooks:
enabled_presets[preset_id] = matching_webhooks
return enabled_presets
except Exception as e:
logger.error(f"Failed to list webhooks: {e}")
return {}
@requires("authenticated", redirect="oauth_login")
async def webhook_management_pane(request: Request) -> HTMLResponse:
"""Webhook management pane - returns HTML for webhook configuration.
This endpoint checks if the user is an admin and returns either:
- Admin view: Webhook management interface with preset controls
- Non-admin view: Message indicating admin-only access
Args:
request: Starlette request object
Returns:
HTML response with webhook management interface or access denied message
"""
try:
# Get authenticated HTTP client
http_client = await _get_authenticated_client(request)
username = request.user.display_name
# Check admin permissions
is_admin = await is_nextcloud_admin(request, http_client)
if not is_admin:
return HTMLResponse(
content="""
<div class="info-message">
<p><strong>Admin Access Required</strong></p>
<p>Webhook management is only available to Nextcloud administrators.</p>
<p>Your account does not have admin privileges.</p>
</div>
"""
)
# Get webhooks client
webhooks_client = WebhooksClient(http_client, username)
# Get storage for database-backed webhook tracking
storage = _get_storage(request)
# Get installed apps to filter presets
installed_apps = await _get_installed_apps(http_client)
logger.debug(f"Installed apps: {installed_apps}")
# Get currently enabled presets (from database or API)
enabled_presets = await _get_enabled_presets(webhooks_client, storage)
# Filter presets based on installed apps
available_presets = filter_presets_by_installed_apps(installed_apps)
# Build preset cards HTML
preset_cards_html = ""
for preset_id, preset in available_presets:
is_enabled = preset_id in enabled_presets
num_webhooks = len(enabled_presets.get(preset_id, []))
# Status badge
if is_enabled:
status_badge = f'<span style="color: #4caf50; font-weight: bold;">✓ Enabled ({num_webhooks} webhooks)</span>'
action_button = f"""
<button
hx-delete="/app/webhooks/disable/{preset_id}"
hx-target="#preset-{preset_id}"
hx-swap="outerHTML"
class="button"
style="background-color: #ff9800;">
Disable
</button>
"""
else:
status_badge = '<span style="color: #999;">Not Enabled</span>'
action_button = f"""
<button
hx-post="/app/webhooks/enable/{preset_id}"
hx-target="#preset-{preset_id}"
hx-swap="outerHTML"
class="button button-primary">
Enable
</button>
"""
preset_cards_html += f"""
<div id="preset-{preset_id}" style="border: 1px solid #e0e0e0; border-radius: 6px; padding: 20px; margin: 15px 0;">
<h3 style="margin-top: 0; color: #0082c9;">{preset["name"]}</h3>
<p style="color: #666; margin: 10px 0;">{preset["description"]}</p>
<p style="font-size: 13px; color: #999;">
<strong>App:</strong> {preset["app"]} |
<strong>Events:</strong> {len(preset["events"])}
</p>
<div style="margin-top: 15px; display: flex; align-items: center; gap: 15px;">
<div>{status_badge}</div>
<div>{action_button}</div>
</div>
</div>
"""
# Get webhook endpoint URL for display
webhook_uri = _get_webhook_uri()
html_content = f"""
<h2>Webhook Management</h2>
<div class="info-message">
<p><strong>About Webhooks</strong></p>
<p>Webhooks enable real-time synchronization by notifying this server when content changes in Nextcloud.</p>
<p><strong>Endpoint:</strong> <code>{webhook_uri}</code></p>
</div>
<h3 style="margin-top: 30px;">Available Presets</h3>
<p style="color: #666;">Enable webhook presets with one click for common synchronization scenarios.</p>
<p style="color: #999; font-size: 13px; margin-top: 5px;">Showing {len(available_presets)} preset(s) for your installed apps ({len(installed_apps)} detected)</p>
{preset_cards_html}
"""
return HTMLResponse(content=html_content)
except Exception as e:
logger.error(f"Error loading webhook management pane: {e}", exc_info=True)
return HTMLResponse(
content=f"""
<div class="warning">
<p><strong>Error Loading Webhooks</strong></p>
<p>{str(e)}</p>
</div>
""",
status_code=500,
)
@requires("authenticated", redirect="oauth_login")
async def enable_webhook_preset(request: Request) -> HTMLResponse:
"""Enable a webhook preset by registering all webhooks.
Args:
request: Starlette request object (preset_id in path)
Returns:
HTML response with updated preset card
"""
preset_id = request.path_params["preset_id"]
try:
# Get authenticated HTTP client
http_client = await _get_authenticated_client(request)
username = request.user.display_name
# Check admin permissions
is_admin = await is_nextcloud_admin(request, http_client)
if not is_admin:
return HTMLResponse(
content='<div class="warning">Admin access required</div>',
status_code=403,
)
# Get preset configuration
preset = get_preset(preset_id)
if not preset:
return HTMLResponse(
content=f'<div class="warning">Unknown preset: {preset_id}</div>',
status_code=404,
)
# Register webhooks
webhooks_client = WebhooksClient(http_client, username)
webhook_uri = _get_webhook_uri()
registered_ids = []
for event_config in preset["events"]:
webhook_data = await webhooks_client.create_webhook(
event=event_config["event"],
uri=webhook_uri,
event_filter=event_config["filter"] if event_config["filter"] else None,
)
webhook_id = webhook_data["id"]
registered_ids.append(webhook_id)
logger.info(f"Registered webhook {webhook_id} for {event_config['event']}")
# Persist webhook IDs to database
storage = _get_storage(request)
if storage:
for webhook_id in registered_ids:
await storage.store_webhook(webhook_id, preset_id)
logger.info(
f"Persisted {len(registered_ids)} webhook(s) for preset '{preset_id}' to database"
)
# Return updated card
num_webhooks = len(registered_ids)
return HTMLResponse(
content=f"""
<div id="preset-{preset_id}" style="border: 1px solid #e0e0e0; border-radius: 6px; padding: 20px; margin: 15px 0;">
<h3 style="margin-top: 0; color: #0082c9;">{preset["name"]}</h3>
<p style="color: #666; margin: 10px 0;">{preset["description"]}</p>
<p style="font-size: 13px; color: #999;">
<strong>App:</strong> {preset["app"]} |
<strong>Events:</strong> {len(preset["events"])}
</p>
<div style="margin-top: 15px; display: flex; align-items: center; gap: 15px;">
<div><span style="color: #4caf50; font-weight: bold;"> Enabled ({num_webhooks} webhooks)</span></div>
<div>
<button
hx-delete="/app/webhooks/disable/{preset_id}"
hx-target="#preset-{preset_id}"
hx-swap="outerHTML"
class="button"
style="background-color: #ff9800;">
Disable
</button>
</div>
</div>
</div>
"""
)
except Exception as e:
logger.error(f"Failed to enable preset {preset_id}: {e}", exc_info=True)
return HTMLResponse(
content=f'<div class="warning">Failed to enable preset: {str(e)}</div>',
status_code=500,
)
@requires("authenticated", redirect="oauth_login")
async def disable_webhook_preset(request: Request) -> HTMLResponse:
"""Disable a webhook preset by deleting all registered webhooks.
Args:
request: Starlette request object (preset_id in path)
Returns:
HTML response with updated preset card
"""
preset_id = request.path_params["preset_id"]
try:
# Get authenticated HTTP client
http_client = await _get_authenticated_client(request)
username = request.user.display_name
# Check admin permissions
is_admin = await is_nextcloud_admin(request, http_client)
if not is_admin:
return HTMLResponse(
content='<div class="warning">Admin access required</div>',
status_code=403,
)
# Get preset configuration
preset = get_preset(preset_id)
if not preset:
return HTMLResponse(
content=f'<div class="warning">Unknown preset: {preset_id}</div>',
status_code=404,
)
# Find and delete matching webhooks
webhooks_client = WebhooksClient(http_client, username)
# Get webhook IDs from database first (more reliable)
storage = _get_storage(request)
if storage:
webhook_ids = await storage.get_webhooks_by_preset(preset_id)
else:
# Fallback to API query if storage not available
enabled_presets = await _get_enabled_presets(webhooks_client)
webhook_ids = enabled_presets.get(preset_id, [])
for webhook_id in webhook_ids:
await webhooks_client.delete_webhook(webhook_id)
logger.info(f"Deleted webhook {webhook_id} from preset {preset_id}")
# Remove from database
if storage:
deleted_count = await storage.clear_preset_webhooks(preset_id)
logger.info(
f"Removed {deleted_count} webhook(s) for preset '{preset_id}' from database"
)
# Return updated card
return HTMLResponse(
content=f"""
<div id="preset-{preset_id}" style="border: 1px solid #e0e0e0; border-radius: 6px; padding: 20px; margin: 15px 0;">
<h3 style="margin-top: 0; color: #0082c9;">{preset["name"]}</h3>
<p style="color: #666; margin: 10px 0;">{preset["description"]}</p>
<p style="font-size: 13px; color: #999;">
<strong>App:</strong> {preset["app"]} |
<strong>Events:</strong> {len(preset["events"])}
</p>
<div style="margin-top: 15px; display: flex; align-items: center; gap: 15px;">
<div><span style="color: #999;">Not Enabled</span></div>
<div>
<button
hx-post="/app/webhooks/enable/{preset_id}"
hx-target="#preset-{preset_id}"
hx-swap="outerHTML"
class="button button-primary">
Enable
</button>
</div>
</div>
</div>
"""
)
except Exception as e:
logger.error(f"Failed to disable preset {preset_id}: {e}", exc_info=True)
return HTMLResponse(
content=f'<div class="warning">Failed to disable preset: {str(e)}</div>',
status_code=500,
)
-257
View File
@@ -1,257 +0,0 @@
import os
import click
import uvicorn
from nextcloud_mcp_server.config import (
get_settings,
)
from nextcloud_mcp_server.observability import get_uvicorn_logging_config
from .app import get_app
@click.command()
@click.option(
"--host", "-h", default="127.0.0.1", show_default=True, help="Server host"
)
@click.option(
"--port", "-p", type=int, default=8000, show_default=True, help="Server port"
)
@click.option(
"--log-level",
"-l",
default="info",
show_default=True,
type=click.Choice(["critical", "error", "warning", "info", "debug", "trace"]),
help="Logging level",
)
@click.option(
"--transport",
"-t",
default="sse",
show_default=True,
type=click.Choice(["sse", "streamable-http", "http"]),
help="MCP transport protocol",
)
@click.option(
"--enable-app",
"-e",
multiple=True,
type=click.Choice(
["notes", "tables", "webdav", "calendar", "contacts", "cookbook", "deck"]
),
help="Enable specific Nextcloud app APIs. Can be specified multiple times. If not specified, all apps are enabled.",
)
@click.option(
"--oauth/--no-oauth",
default=None,
help="Force OAuth mode (if enabled) or BasicAuth mode (if disabled). By default, auto-detected based on environment variables.",
)
@click.option(
"--oauth-client-id",
envvar="NEXTCLOUD_OIDC_CLIENT_ID",
help="OAuth client ID (can also use NEXTCLOUD_OIDC_CLIENT_ID env var)",
)
@click.option(
"--oauth-client-secret",
envvar="NEXTCLOUD_OIDC_CLIENT_SECRET",
help="OAuth client secret (can also use NEXTCLOUD_OIDC_CLIENT_SECRET env var)",
)
@click.option(
"--mcp-server-url",
envvar="NEXTCLOUD_MCP_SERVER_URL",
default="http://localhost:8000",
show_default=True,
help="MCP server URL for OAuth callbacks (can also use NEXTCLOUD_MCP_SERVER_URL env var)",
)
@click.option(
"--nextcloud-host",
envvar="NEXTCLOUD_HOST",
help="Nextcloud instance URL (can also use NEXTCLOUD_HOST env var)",
)
@click.option(
"--nextcloud-username",
envvar="NEXTCLOUD_USERNAME",
help="Nextcloud username for BasicAuth (can also use NEXTCLOUD_USERNAME env var)",
)
@click.option(
"--nextcloud-password",
envvar="NEXTCLOUD_PASSWORD",
help="Nextcloud password for BasicAuth (can also use NEXTCLOUD_PASSWORD env var)",
)
@click.option(
"--oauth-scopes",
envvar="NEXTCLOUD_OIDC_SCOPES",
default="openid profile email notes:read notes:write calendar:read calendar:write todo:read todo:write contacts:read contacts:write cookbook:read cookbook:write deck:read deck:write tables:read tables:write files:read files:write sharing:read sharing:write",
show_default=True,
help="OAuth scopes to request during client registration. These define the maximum allowed scopes for the client. Note: Actual supported scopes are discovered dynamically from MCP tools at runtime. (can also use NEXTCLOUD_OIDC_SCOPES env var)",
)
@click.option(
"--oauth-token-type",
envvar="NEXTCLOUD_OIDC_TOKEN_TYPE",
default="bearer",
show_default=True,
type=click.Choice(["bearer", "jwt"], case_sensitive=False),
help="OAuth token type (can also use NEXTCLOUD_OIDC_TOKEN_TYPE env var)",
)
@click.option(
"--public-issuer-url",
envvar="NEXTCLOUD_PUBLIC_ISSUER_URL",
help="Public issuer URL for OAuth (can also use NEXTCLOUD_PUBLIC_ISSUER_URL env var)",
)
def run(
host: str,
port: int,
log_level: str,
transport: str,
enable_app: tuple[str, ...],
oauth: bool | None,
oauth_client_id: str | None,
oauth_client_secret: str | None,
mcp_server_url: str,
nextcloud_host: str | None,
nextcloud_username: str | None,
nextcloud_password: str | None,
oauth_scopes: str,
oauth_token_type: str,
public_issuer_url: str | None,
):
"""
Run the Nextcloud MCP server.
\b
Authentication Modes:
- BasicAuth: Set NEXTCLOUD_USERNAME and NEXTCLOUD_PASSWORD
- OAuth: Leave USERNAME/PASSWORD unset (requires OIDC app enabled)
\b
Examples:
# BasicAuth mode with CLI options
$ nextcloud-mcp-server --nextcloud-host=https://cloud.example.com \\
--nextcloud-username=admin --nextcloud-password=secret
# BasicAuth mode with env vars (recommended for credentials)
$ export NEXTCLOUD_HOST=https://cloud.example.com
$ export NEXTCLOUD_USERNAME=admin
$ export NEXTCLOUD_PASSWORD=secret
$ nextcloud-mcp-server --host 0.0.0.0 --port 8000
# OAuth mode with auto-registration
$ nextcloud-mcp-server --nextcloud-host=https://cloud.example.com --oauth
# OAuth mode with pre-configured client
$ nextcloud-mcp-server --nextcloud-host=https://cloud.example.com --oauth \\
--oauth-client-id=xxx --oauth-client-secret=yyy
# OAuth mode with custom scopes and JWT tokens
$ nextcloud-mcp-server --nextcloud-host=https://cloud.example.com --oauth \\
--oauth-scopes="openid notes:read notes:write" --oauth-token-type=jwt
# OAuth with public issuer URL (for Docker/proxy setups)
$ nextcloud-mcp-server --nextcloud-host=http://app --oauth \\
--public-issuer-url=http://localhost:8080
"""
# Set env vars from CLI options if provided
if nextcloud_host:
os.environ["NEXTCLOUD_HOST"] = nextcloud_host
if nextcloud_username:
os.environ["NEXTCLOUD_USERNAME"] = nextcloud_username
if nextcloud_password:
os.environ["NEXTCLOUD_PASSWORD"] = nextcloud_password
if oauth_client_id:
os.environ["NEXTCLOUD_OIDC_CLIENT_ID"] = oauth_client_id
if oauth_client_secret:
os.environ["NEXTCLOUD_OIDC_CLIENT_SECRET"] = oauth_client_secret
if oauth_scopes:
os.environ["NEXTCLOUD_OIDC_SCOPES"] = oauth_scopes
if oauth_token_type:
os.environ["NEXTCLOUD_OIDC_TOKEN_TYPE"] = oauth_token_type
if mcp_server_url:
os.environ["NEXTCLOUD_MCP_SERVER_URL"] = mcp_server_url
if public_issuer_url:
os.environ["NEXTCLOUD_PUBLIC_ISSUER_URL"] = public_issuer_url
# Force OAuth mode if explicitly requested
if oauth is True:
# Clear username/password to force OAuth mode
if "NEXTCLOUD_USERNAME" in os.environ:
click.echo(
"Warning: --oauth flag set, ignoring NEXTCLOUD_USERNAME", err=True
)
del os.environ["NEXTCLOUD_USERNAME"]
if "NEXTCLOUD_PASSWORD" in os.environ:
click.echo(
"Warning: --oauth flag set, ignoring NEXTCLOUD_PASSWORD", err=True
)
del os.environ["NEXTCLOUD_PASSWORD"]
# Validate OAuth configuration
nextcloud_host = os.getenv("NEXTCLOUD_HOST")
if not nextcloud_host:
raise click.ClickException(
"OAuth mode requires NEXTCLOUD_HOST environment variable to be set"
)
# Check if we have client credentials OR if dynamic registration is possible
has_client_creds = os.getenv("NEXTCLOUD_OIDC_CLIENT_ID") and os.getenv(
"NEXTCLOUD_OIDC_CLIENT_SECRET"
)
if not has_client_creds:
# No client credentials - will attempt dynamic registration
# Show helpful message before server starts
click.echo("", err=True)
click.echo("OAuth Configuration:", err=True)
click.echo(" Mode: Dynamic Client Registration", err=True)
click.echo(" Host: " + nextcloud_host, err=True)
click.echo(" Storage: SQLite (TOKEN_STORAGE_DB)", err=True)
click.echo("", err=True)
click.echo(
"Note: Make sure 'Dynamic Client Registration' is enabled", err=True
)
click.echo(" in your Nextcloud OIDC app settings.", err=True)
click.echo("", err=True)
else:
click.echo("", err=True)
click.echo("OAuth Configuration:", err=True)
click.echo(" Mode: Pre-configured Client", err=True)
click.echo(" Host: " + nextcloud_host, err=True)
click.echo(
" Client ID: "
+ os.getenv("NEXTCLOUD_OIDC_CLIENT_ID", "")[:16]
+ "...",
err=True,
)
click.echo("", err=True)
elif oauth is False:
# Force BasicAuth mode - verify credentials exist
if not os.getenv("NEXTCLOUD_USERNAME") or not os.getenv("NEXTCLOUD_PASSWORD"):
raise click.ClickException(
"--no-oauth flag set but NEXTCLOUD_USERNAME or NEXTCLOUD_PASSWORD not set"
)
enabled_apps = list(enable_app) if enable_app else None
app = get_app(transport=transport, enabled_apps=enabled_apps)
# Get observability settings and create uvicorn logging config
settings = get_settings()
uvicorn_log_config = get_uvicorn_logging_config(
log_format=settings.log_format,
log_level=settings.log_level,
include_trace_context=settings.log_include_trace_context,
)
uvicorn.run(
app=app,
host=host,
port=port,
log_level=log_level,
log_config=uvicorn_log_config,
)
if __name__ == "__main__":
run()
-4
View File
@@ -9,7 +9,6 @@ from httpx import (
BasicAuth,
Request,
Response,
Timeout,
)
from ..controllers.notes_search import NotesSearchController
@@ -23,7 +22,6 @@ from .sharing import SharingClient
from .tables import TablesClient
from .users import UsersClient
from .webdav import WebDAVClient
from .webhooks import WebhooksClient
logger = logging.getLogger(__name__)
@@ -68,7 +66,6 @@ class NextcloudClient:
auth=auth,
transport=AsyncDisableCookieTransport(AsyncHTTPTransport()),
event_hooks={"request": [log_request], "response": [log_response]},
timeout=Timeout(timeout=30, connect=5),
)
# Initialize app clients
@@ -84,7 +81,6 @@ class NextcloudClient:
self.users = UsersClient(self._client, username)
self.groups = GroupsClient(self._client, username)
self.sharing = SharingClient(self._client, username)
self.webhooks = WebhooksClient(self._client, username)
# Initialize controllers
self._notes_search = NotesSearchController()
+4 -57
View File
@@ -7,12 +7,6 @@ from functools import wraps
from httpx import AsyncClient, HTTPStatusError, RequestError, codes
from nextcloud_mcp_server.observability.metrics import (
record_nextcloud_api_call,
record_nextcloud_api_retry,
)
from nextcloud_mcp_server.observability.tracing import trace_nextcloud_api_call
logger = logging.getLogger(__name__)
@@ -44,9 +38,6 @@ def retry_on_429(func):
logger.warning(
f"429 Client Error: Too Many Requests, Number of attempts: {retries}"
)
# Record retry metric (extract app name from args if available)
if len(args) > 0 and hasattr(args[0], "app_name"):
record_nextcloud_api_retry(app=args[0].app_name, reason="429")
time.sleep(5)
elif e.response.status_code == 404:
# 404 errors are often expected (e.g., checking if attachments exist)
@@ -81,9 +72,6 @@ def retry_on_429(func):
class BaseNextcloudClient(ABC):
"""Base class for all Nextcloud app clients."""
# Subclasses should set this to identify the app for metrics/tracing
app_name: str = "unknown"
def __init__(self, http_client: AsyncClient, username: str):
"""Initialize with shared HTTP client and username.
@@ -100,7 +88,7 @@ class BaseNextcloudClient(ABC):
@retry_on_429
async def _make_request(self, method: str, url: str, **kwargs):
"""Common request wrapper with logging, tracing, and error handling.
"""Common request wrapper with logging and error handling.
Args:
method: HTTP method
@@ -111,47 +99,6 @@ class BaseNextcloudClient(ABC):
Response object
"""
logger.debug(f"Making {method} request to {url}")
# Start timer for metrics
start_time = time.time()
status_code = 0
try:
# Wrap request in trace span
with trace_nextcloud_api_call(
app=self.app_name,
method=method,
path=url,
):
response = await self._client.request(method, url, **kwargs)
status_code = response.status_code
response.raise_for_status()
# Record successful API call metrics
duration = time.time() - start_time
record_nextcloud_api_call(
app=self.app_name,
method=method,
status_code=status_code,
duration=duration,
)
return response
except (HTTPStatusError, RequestError) as e:
# Record error metrics
if isinstance(e, HTTPStatusError):
status_code = e.response.status_code
else:
status_code = 0 # Connection error, no status code
duration = time.time() - start_time
record_nextcloud_api_call(
app=self.app_name,
method=method,
status_code=status_code,
duration=duration,
)
# Re-raise the exception
raise
response = await self._client.request(method, url, **kwargs)
response.raise_for_status()
return response
+12 -18
View File
@@ -100,7 +100,7 @@ class CalendarClient:
# Use custom PROPFIND with CalendarServer namespace (cs:) for calendar-color.
# caldav library's nsmap lacks "CS" namespace, and its CalendarColor uses
# Apple iCal namespace which Nextcloud doesn't recognize.
from lxml import etree # type: ignore[import-untyped]
from lxml import etree
propfind_body = """<?xml version="1.0" encoding="utf-8"?>
<d:propfind xmlns:d="DAV:" xmlns:cs="http://calendarserver.org/ns/" xmlns:c="urn:ietf:params:xml:ns:caldav">
@@ -261,12 +261,11 @@ class CalendarClient:
result = []
for event in events:
await event.load(only_if_unloaded=True)
if event.data:
event_dict = self._parse_ical_event(event.data)
if event_dict:
event_dict["href"] = str(event.url)
event_dict["etag"] = ""
result.append(event_dict)
event_dict = self._parse_ical_event(event.data)
if event_dict:
event_dict["href"] = str(event.url)
event_dict["etag"] = ""
result.append(event_dict)
if len(result) >= limit:
break
@@ -315,8 +314,8 @@ class CalendarClient:
await event.load(only_if_unloaded=True)
# Merge updates into existing iCal data
updated_ical = self._merge_ical_properties(event.data, event_data, event_uid) # type: ignore[arg-type]
event.data = updated_ical # type: ignore[misc]
updated_ical = self._merge_ical_properties(event.data, event_data, event_uid)
event.data = updated_ical
await event.save()
@@ -350,7 +349,7 @@ class CalendarClient:
event = await calendar.event_by_uid(event_uid)
await event.load(only_if_unloaded=True)
event_data = self._parse_ical_event(event.data) if event.data else None # type: ignore[arg-type]
event_data = self._parse_ical_event(event.data)
if not event_data:
raise ValueError(f"Failed to parse event data for {event_uid}")
@@ -417,10 +416,7 @@ class CalendarClient:
# Only load if data not already present from REPORT response
# This avoids 404 errors for virtual calendars (e.g., Deck boards)
await todo.load(only_if_unloaded=True)
if todo.data:
todo_dict = self._parse_ical_todo(todo.data) # type: ignore[arg-type]
else:
continue
todo_dict = self._parse_ical_todo(todo.data)
if todo_dict:
todo_dict["href"] = str(todo.url)
todo_dict["etag"] = ""
@@ -474,14 +470,12 @@ class CalendarClient:
await todo.load(only_if_unloaded=True)
logger.debug(
f"Loaded todo {todo_uid}, current data length: {len(todo.data)}" # type: ignore
f"Loaded todo {todo_uid}, current data length: {len(todo.data)}"
)
# Merge updates into existing iCal data
updated_ical = self._merge_ical_todo_properties(
todo.data, # type: ignore[arg-type]
todo_data,
todo_uid,
todo.data, todo_data, todo_uid
)
logger.debug(f"Merged iCal data length: {len(updated_ical)}")
logger.debug(f"Updated iCal content:\n{updated_ical}")
+2 -4
View File
@@ -13,8 +13,6 @@ logger = logging.getLogger(__name__)
class ContactsClient(BaseNextcloudClient):
"""Client for NextCloud CardDAV contact operations."""
app_name = "contacts"
def _get_carddav_base_path(self) -> str:
"""Helper to get the base CardDAV path for contacts."""
return f"/remote.php/dav/addressbooks/users/{self.username}"
@@ -126,7 +124,7 @@ class ContactsClient(BaseNextcloudClient):
carddav_path = self._get_carddav_base_path()
url = f"{carddav_path}/{addressbook}/{uid}.vcf"
contact = Contact(fn=contact_data.get("fn"), uid=uid) # type: ignore
contact = Contact(fn=contact_data.get("fn"), uid=uid)
if "email" in contact_data:
contact.email = [{"value": contact_data["email"], "type": ["HOME"]}]
if "tel" in contact_data:
@@ -176,7 +174,7 @@ class ContactsClient(BaseNextcloudClient):
)
else:
# Fallback to creating new vCard if we couldn't get existing
contact = Contact(fn=contact_data.get("fn"), uid=uid) # type: ignore
contact = Contact(fn=contact_data.get("fn"), uid=uid)
if "email" in contact_data:
contact.email = [{"value": contact_data["email"], "type": ["HOME"]}]
if "tel" in contact_data:
-2
View File
@@ -13,8 +13,6 @@ logger = logging.getLogger(__name__)
class CookbookClient(BaseNextcloudClient):
"""Client for Nextcloud Cookbook app operations."""
app_name = "cookbook"
async def get_version(self) -> Dict[str, Any]:
"""Get Cookbook app and API version."""
response = await self._make_request("GET", "/apps/cookbook/api/version")
-2
View File
@@ -17,8 +17,6 @@ from nextcloud_mcp_server.models.deck import (
class DeckClient(BaseNextcloudClient):
"""Client for Nextcloud Deck app operations."""
app_name = "deck"
def _get_deck_headers(
self, additional_headers: Optional[Dict[str, str]] = None
) -> Dict[str, str]:
-2
View File
@@ -11,8 +11,6 @@ logger = logging.getLogger(__name__)
class GroupsClient(BaseNextcloudClient):
"""Client for Nextcloud Groups API operations."""
app_name = "groups"
@retry_on_429
async def search_groups(
self,
+4 -45
View File
@@ -11,64 +11,23 @@ logger = logging.getLogger(__name__)
class NotesClient(BaseNextcloudClient):
"""Client for Nextcloud Notes app operations."""
app_name = "notes"
async def get_settings(self) -> Dict[str, Any]:
"""Get Notes app settings."""
response = await self._make_request("GET", "/apps/notes/api/v1/settings")
return response.json()
async def get_all_notes(
self, prune_before: Optional[int] = None
) -> AsyncIterator[Dict[str, Any]]:
"""Get all notes, yielding them one at a time.
The Notes API returns changed notes with full data in chunks, and ALL note IDs
(with only 'id' field) in the last chunk for deletion detection. This causes
duplicates which we handle by tracking seen IDs (first occurrence with full
data is kept, later pruned duplicates are skipped).
Args:
prune_before: Optional Unix timestamp. Notes unchanged since this time
are pruned (only 'id' field returned in last chunk).
Reduces data transfer for large note collections.
Yields:
Note dictionaries with full data (deduplicated).
"""
async def get_all_notes(self) -> AsyncIterator[Dict[str, Any]]:
"""Get all notes, yielding them one at a time."""
cursor = ""
seen_ids: set[int] = set()
while True:
params: Dict[str, Any] = {"chunkSize": 10}
if cursor:
params["chunkCursor"] = cursor
if prune_before is not None:
params["pruneBefore"] = prune_before
response = await self._make_request(
"GET",
"/apps/notes/api/v1/notes",
params=params,
params={"chunkSize": 10, "chunkCursor": cursor},
)
response_data = response.json()
for note in response_data:
note_id = note.get("id")
if note_id is None:
logger.warning(f"Skipping note without ID: {note}")
continue
# Skip duplicates (API returns all IDs in last chunk for deletion detection)
if note_id in seen_ids:
logger.debug(
f"Skipping duplicate note {note_id} (pruned version in last chunk)"
)
continue
seen_ids.add(note_id)
for note in response.json():
yield note
if "X-Notes-Chunk-Cursor" not in response.headers:
break
cursor = response.headers["X-Notes-Chunk-Cursor"]
-2
View File
@@ -11,8 +11,6 @@ logger = logging.getLogger(__name__)
class SharingClient(BaseNextcloudClient):
"""Client for Nextcloud OCS Sharing API operations."""
app_name = "sharing"
@retry_on_429
async def create_share(
self,

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