Files
nextcloud-mcp-server/CLAUDE.md
T
Chris Coutinho c8d9cc24e0 refactor: migrate asyncio to anyio for consistent structured concurrency
Replace asyncio primitives with anyio equivalents throughout the codebase
to establish a single async pattern. This provides better structured
concurrency with automatic cancellation on errors and aligns with the
pytest anyio configuration.

Changes:
- hybrid.py: Replace asyncio.gather() with anyio task groups
- token_broker.py: Replace asyncio.Lock() with anyio.Lock()
- storage.py: Replace asyncio.run() with anyio.run()
- app.py: Replace tg.start_soon() with await tg.start() for task status
- processor.py: Add task_status parameter for structured startup
- scanner.py: Add task_status parameter for structured startup
- CLAUDE.md: Update async/await patterns guidance

The change from start_soon() to await tg.start() enables proper task
initialization signaling, ensuring background tasks are ready before
proceeding. This follows anyio best practices for structured concurrency.

All 118 unit tests pass with the new implementation.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 03:51:45 +01:00

16 KiB

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Coding Conventions

async/await Patterns

  • Use anyio for all async operations - Provides structured concurrency
    • pytest runs in anyio mode (anyio_mode = "auto" in pyproject.toml)
    • Use anyio.create_task_group() for concurrent execution (NOT asyncio.gather())
    • Use anyio.Lock() for synchronization primitives (NOT asyncio.Lock())
    • Use anyio.run() for entry points (NOT asyncio.run())
    • Prefer standard async/await syntax without explicit library imports when possible
    • Examples: app.py, search/hybrid.py, search/verification.py, auth/token_broker.py

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:
    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)

Testing

# Fast feedback (recommended)
uv run pytest tests/unit/ -v                    # Unit tests (~5s)
uv run pytest -m smoke -v                       # Smoke tests (~30-60s)

# 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)

# 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

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

# 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

uv sync                # Install dependencies
uv sync --group dev    # Install with dev dependencies

Load Testing

# Quick test (default: 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

# Export results for analysis
uv run python -m tests.load.benchmark --output results.json --verbose

Expected Performance: 50-200 RPS for mixed workload, p50 <100ms, p95 <500ms, p99 <1000ms.

Database Inspection

Credentials: root/password, nextcloud/password, database: nextcloud

# Connect to database
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;"

# 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%';"

# 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;"

Important Tables:

  • oc_oidc_clients - OAuth client registrations (DCR)
  • 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

Architecture Quick Reference

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

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

Supported Apps: Notes, Calendar (CalDAV + VTODO tasks), Contacts (CardDAV), Tables, WebDAV, Deck, Cookbook

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

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.

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

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

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

When to use BasicAuth instead:

  • Simple single-user deployments
  • Local development and testing

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

MCP Response Patterns (CRITICAL)

Never return raw List[Dict] from MCP tools - FastMCP mangles them into dicts with numeric string keys.

Correct 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

Testing: Extract data["results"] from MCP responses, not data directly.

MCP Sampling for RAG (ADR-008)

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.

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):

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
        )

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

Reference: See nc_notes_semantic_search_answer in nextcloud_mcp_server/server/notes.py:517 and ADR-008 for complete implementation.

Testing Best Practices (MANDATORY)

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

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:

async def test_notes_api_get_note(mocker):
    """Test that get_note correctly parses the API response."""
    mock_response = create_mock_note_response(
        note_id=123, title="Test Note", content="Test content",
        category="Test", etag="abc123"
    )

    mock_make_request = mocker.patch.object(
        NotesClient, "_make_request", return_value=mock_response
    )

    client = NotesClient(mocker.AsyncMock(spec=httpx.AsyncClient), "testuser")
    note = await client.get_note(note_id=123)

    assert note["id"] == 123
    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()

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

OAuth Testing

OAuth tests use Playwright browser automation to complete flows programmatically.

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

OAuth fixtures: nc_oauth_client, nc_mcp_oauth_client, alice_oauth_token, bob_oauth_token, etc.

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.

Run OAuth tests:

uv run pytest -m oauth -v                        # All OAuth tests
uv run pytest tests/server/oauth/ --browser firefox -v
uv run pytest tests/server/oauth/test_oauth_core.py --browser firefox --headed -v

Keycloak OAuth Testing

Validates ADR-002 architecture for external identity providers and offline access patterns.

Architecture: MCP Client → Keycloak (OAuth) → MCP Server → Nextcloud user_oidc (validates token) → APIs

Setup:

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

Credentials: admin/admin (Keycloak realm: nextcloud-mcp)

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

Nextcloud: docker compose exec app php occ ... for occ commands MariaDB: docker compose exec db mariadb -u [user] -p [password] [database] for queries

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

For additional information regarding MCP during development, see:

  • ../../Software/modelcontextprotocol/ - MCP spec
  • ../../Software/python-sdk/ - Python MCP SDK