Nextcloud MCP Server

# Nextcloud Instance NEXTCLOUD_HOST= # ===== AUTHENTICATION MODE ===== # Choose ONE of the following: # Option 1: OAuth2/OIDC (RECOMMENDED - More Secure) # - 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_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 # - Use only for backward compatibility or if OAuth unavailable # - If these are set, OAuth mode is disabled NEXTCLOUD_USERNAME= NEXTCLOUD_PASSWORD= # ============================================ # Document Processing Configuration # ============================================ # Enable document processing (PDF, DOCX, images, etc.) # Set to false to disable all document processing ENABLE_DOCUMENT_PROCESSING=false # Default processor to use when multiple are available # Options: unstructured, tesseract, custom DOCUMENT_PROCESSOR=unstructured # ============================================ # Unstructured.io Processor # ============================================ # Enable Unstructured processor (requires unstructured service in docker-compose) # This is a cloud-based/API processor supporting many document types ENABLE_UNSTRUCTURED=false # Unstructured API endpoint UNSTRUCTURED_API_URL=http://unstructured:8000 # Request timeout in seconds (default: 120) # OCR operations can take 30-120 seconds for large documents UNSTRUCTURED_TIMEOUT=120 # Parsing strategy: auto, fast, hi_res # - auto: Automatically choose based on document type # - fast: Fast parsing without OCR # - hi_res: High-resolution with OCR (slowest, most accurate) UNSTRUCTURED_STRATEGY=auto # OCR languages (comma-separated ISO 639-3 codes) # Common: eng=English, deu=German, fra=French, spa=Spanish UNSTRUCTURED_LANGUAGES=eng,deu # Progress reporting interval in seconds (default: 10) # During long-running OCR operations, progress notifications are sent to the MCP client # at this interval to prevent timeouts and provide status updates PROGRESS_INTERVAL=10 # ============================================ # Tesseract Processor (Local OCR) # ============================================ # Enable Tesseract processor (requires tesseract binary installed) # This is a local, lightweight OCR solution for images only ENABLE_TESSERACT=false # Path to tesseract executable (optional, auto-detected if in PATH) #TESSERACT_CMD=/usr/bin/tesseract # OCR language (e.g., eng, deu, eng+deu for multiple) TESSERACT_LANG=eng # ============================================ # Custom Processor (Your own API) # ============================================ # Enable custom document processor via HTTP API ENABLE_CUSTOM_PROCESSOR=false # Unique name for your processor #CUSTOM_PROCESSOR_NAME=my_ocr # Your custom processor API endpoint #CUSTOM_PROCESSOR_URL=http://localhost:9000/process # Optional API key for authentication #CUSTOM_PROCESSOR_API_KEY=your-api-key-here # Request timeout in seconds #CUSTOM_PROCESSOR_TIMEOUT=60 # 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