Chris Coutinho
137d1d6c75
This commit addresses critical performance issues with vector visualization search (reducing time from 40s to ~2s) and improves result visualization through better visual encoding. ## Performance Fixes ### 1. Fix blocking sleep in retry decorator (base.py:51) - Changed `time.sleep(5)` to `await anyio.sleep(5)` in @retry_on_429 - Prevents entire event loop from freezing during rate limit retries - Impact: Reduced search time from 22s to 16s initially ### 2. Add concurrency limiting for verification (verification.py:77-93) - Added `anyio.Semaphore(20)` to limit concurrent HTTP requests - Prevents connection pool exhaustion (RequestError) from 90+ simultaneous requests - Fixes false filtering (was filtering 77/90 results incorrectly) - Note: Semaphore still in code but verification removed from viz endpoint ### 3. Remove unnecessary verification from viz endpoint (viz_routes.py:483-486) - Visualization only needs Qdrant metadata (title, excerpt), not full content - Verification only required for sampling (LLM needs full note content) - Impact: Reduced search time from 43.7s to ~2s (final fix) ### 4. Restore streaming scanner pattern (scanner.py) - Process notes one-at-a-time using async generator - Avoids loading all notes into memory ## Visualization Improvements ### 5. Result-relative score normalization (viz_routes.py:489-504) - Normalize scores within result set: best=1.0, worst=0.0 - Removes arbitrary RRF normalization (theoretical max didn't make sense) - Makes visual encoding meaningful regardless of algorithm scores ### 6. Power scaling for marker sizes (userinfo_routes.py:743) - Changed from linear `8 + (score * 12)` to power `6 + (score² * 14)` - Creates dramatic visual contrast: 0.0→6px, 0.5→9.5px, 1.0→20px - Combined with opacity (0.2-1.0) for clear visual hierarchy ### 7. Multi-channel visual encoding (userinfo_routes.py:740-745) - Size: Exponentially scaled with score² - Opacity: Linear 0.2-1.0 (keeps all points visible) - Color: Viridis gradient (blue→yellow) - Effect: Top results are large/bright/opaque, context results small/dim/transparent ## Result - Search time: 40s → ~2s (20x faster) - Visual contrast: Subtle → dramatic (clear result hierarchy) - No arbitrary cutoffs: All results visible, best naturally highlighted 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Nextcloud MCP Server
A production-ready MCP server that connects AI assistants to your Nextcloud instance.
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.
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.
Note
Looking for AI features inside Nextcloud? Nextcloud also provides Context Agent, which powers the Assistant app and runs as an ExApp inside Nextcloud. See docs/comparison-context-agent.md for a detailed comparison of use cases.
Quick Start
Get up and running in 60 seconds using Docker:
# 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
# 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
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 for other deployment options (local, Kubernetes)
Key Features
- 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
Supported Apps
| 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) |
Want to see another Nextcloud app supported? Open an issue or contribute a pull request!
Authentication
Important
OAuth2/OIDC is experimental and requires a manual patch to the
user_oidcapp:
- Required patch: Bearer token support (issue #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 for patch status and workarounds.
Recommended: Basic Auth with app-specific passwords provides secure, production-ready authentication. See docs/authentication.md for setup details and OAuth configuration.
Authentication Modes
The server supports two authentication modes:
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
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
See docs/authentication.md for detailed setup instructions.
Semantic Search
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.
Example:
- Keyword search: Query "car" only finds notes containing "car"
- Semantic search: Query "car" also finds notes about "automobile," "vehicle," "sedan," "transportation"
This enables natural language queries and helps discover related content across your Nextcloud notes.
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 supportSee docs/semantic-search-architecture.md for architecture details and docs/configuration.md for setup instructions.
Documentation
Getting Started
- Installation - Docker, Kubernetes, local, or VM deployment
- Configuration - Environment variables and advanced options
- Authentication - Basic Auth vs OAuth2/OIDC setup
- Running the Server - Start, manage, and troubleshoot
Features
- App Documentation - Notes, Calendar, Contacts, WebDAV, Deck, Cookbook, Tables
- Document Processing - OCR and text extraction setup
- Semantic Search Architecture - Experimental vector search (Notes only, opt-in)
Advanced Topics
- OAuth Architecture - How OAuth works (experimental)
- OAuth Quick Start - 5-minute OAuth setup
- OAuth Setup Guide - Detailed OAuth configuration
- Troubleshooting - Common issues and solutions
- Comparison with Context Agent - When to use each approach
Examples
Create a Note
AI: "Create a note called 'Meeting Notes' with today's agenda"
→ Uses nc_notes_create_note tool
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
Schedule Meetings
AI: "Schedule a team meeting for next Tuesday at 2pm"
→ Uses nc_calendar_create_event tool
Manage Files
AI: "Create a folder called 'Project X' and move all PDFs there"
→ Uses nc_webdav_create_directory and nc_webdav_move tools
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)
Note: For AI-generated answers with citations, use nc_semantic_search_answer (requires MCP client with sampling support).
Contributing
Contributions are welcome!
- Report bugs or request features: GitHub Issues
- Submit improvements: Pull Requests
- Development guidelines: CLAUDE.md
Security
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
Found a security issue? Please report it privately to the maintainers.
License
This project is licensed under the AGPL-3.0 License. See LICENSE for details.