mcp-nextcloud/docs/configuration.md

Configuration

The Nextcloud MCP server requires configuration to connect to your Nextcloud instance. Configuration is provided through environment variables, typically stored in a .env file.

Note: Configuration was significantly simplified in v0.58.0. If you're upgrading from v0.57.x, see the Configuration Migration Guide.

Quick Start

We provide mode-specific configuration templates for quick setup:

# Choose a template based on your deployment mode:
cp env.sample.single-user .env         # Simplest - one user, local dev
cp env.sample .env                     # Full reference with all options

# For multi-user Login Flow v2 (recommended), see the dedicated guide:
# docs/login-flow-v2.md#setup

# Edit .env with your Nextcloud details

Note: The legacy templates env.sample.oauth-multi-user and env.sample.oauth-advanced configure the deprecated direct-OAuth-to-Nextcloud modes. New deployments should use Login Flow v2 for multi-user setups.

Then choose your deployment mode:

• Single-User BasicAuth - Simplest for personal instances
• Multi-User BasicAuth - Internal deployments with credential pass-through
• Login Flow v2 - Recommended for hosted / OAuth-based MCP clients
• Deployment Mode Selection - Explicit mode declaration

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Deployment Mode Selection

The server supports three deployment modes. See Authentication for the full comparison and Login Flow v2 for the recommended multi-user setup.

Mode	When to use
single_user_basic	Personal use, dev — credentials in env vars
multi_user_basic	Internal deployments — clients send credentials via Authorization: Basic header
login_flow_v2	Hosted / OAuth-based MCP clients (claude.ai, Astrolabe Cloud) — recommended for multi-user

You can declare the mode explicitly:

MCP_DEPLOYMENT_MODE=login_flow_v2

If MCP_DEPLOYMENT_MODE is not set, the server auto-detects from the other env vars below.

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Single-User BasicAuth Mode

The simplest mode. Use for personal instances, local development, and testing.

NEXTCLOUD_HOST=https://your.nextcloud.instance.com
NEXTCLOUD_USERNAME=your_nextcloud_username
NEXTCLOUD_PASSWORD=your_app_password

Variable	Required	Description
NEXTCLOUD_HOST	✅ Yes	Full URL of your Nextcloud instance
NEXTCLOUD_USERNAME	✅ Yes	Your Nextcloud username
NEXTCLOUD_PASSWORD	✅ Yes	Use a dedicated Nextcloud app password, not your login password

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Multi-User BasicAuth Mode

Each MCP client sends its own Nextcloud credentials in an Authorization: Basic header. The server passes them through per-request and never persists them.

NEXTCLOUD_HOST=https://your.nextcloud.instance.com
ENABLE_MULTI_USER_BASIC_AUTH=true

# Optional: enable per-user app-password storage for background sync
TOKEN_ENCRYPTION_KEY=<fernet-key>
TOKEN_STORAGE_DB=/app/data/tokens.db

NEXTCLOUD_USERNAME and NEXTCLOUD_PASSWORD must NOT be set in this mode.

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Login Flow v2 Mode

The recommended multi-user mode. MCP clients authenticate to the MCP server via OAuth; the server holds per-user Nextcloud app passwords (encrypted) obtained via Login Flow v2.

NEXTCLOUD_HOST=https://your.nextcloud.instance.com
ENABLE_LOGIN_FLOW=true

# App-password storage (required)
TOKEN_ENCRYPTION_KEY=<fernet-key>
TOKEN_STORAGE_DB=/app/data/tokens.db

# Public URLs for browser redirects
NEXTCLOUD_MCP_SERVER_URL=https://mcp.example.com
NEXTCLOUD_PUBLIC_ISSUER_URL=https://your.nextcloud.instance.com

Variable	Required	Description
NEXTCLOUD_HOST	✅ Yes	Internal URL of your Nextcloud instance (server-to-server)
ENABLE_LOGIN_FLOW	✅ Yes	Set to true to enable Login Flow v2
TOKEN_ENCRYPTION_KEY	✅ Yes	Fernet key for app-password encryption — generate with python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
TOKEN_STORAGE_DB	✅ Yes	Path to SQLite DB for stored app passwords (use a persistent volume)
NEXTCLOUD_MCP_SERVER_URL	✅ Yes	Public URL of the MCP server (OAuth issuer)
NEXTCLOUD_PUBLIC_ISSUER_URL	✅ Yes	Public URL of Nextcloud (for browser redirects during Login Flow v2)

See Login Flow v2 for full setup, scope reference, and troubleshooting.

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SSL/TLS Configuration (Optional)

If your Nextcloud instance uses a self-signed certificate or a private CA (common with reverse proxies like Traefik or Caddy), the MCP server will reject the connection by default. Use these settings to configure certificate verification.

Custom CA Bundle (Recommended)

Point the server at your CA certificate file:

NEXTCLOUD_CA_BUNDLE=/etc/ssl/certs/my-ca.pem

With Docker, mount the certificate as a read-only volume:

docker run \
  -v /path/to/my-ca.pem:/etc/ssl/certs/my-ca.pem:ro \
  -e NEXTCLOUD_CA_BUNDLE=/etc/ssl/certs/my-ca.pem \
  -e NEXTCLOUD_HOST=https://nextcloud.local \
  --env-file .env \
  ghcr.io/cbcoutinho/nextcloud-mcp-server:latest

Disable Verification (Development Only)

Warning

Disabling TLS verification is insecure. Only use this for local development or testing.

NEXTCLOUD_VERIFY_SSL=false

Environment Variables Reference

Variable	Required	Default	Description
NEXTCLOUD_VERIFY_SSL	⚠️ Optional	true	Set to false to disable TLS certificate verification
NEXTCLOUD_CA_BUNDLE	⚠️ Optional	-	Path to a PEM CA bundle file for custom certificate authorities

Scope

These settings apply to all outbound connections to Nextcloud and its OIDC endpoints, including:

• Nextcloud API calls (Notes, Calendar, Contacts, WebDAV, etc.)
• OIDC discovery and token endpoints
• OAuth client registration (DCR)
• Health checks

They do not affect connections to internal services (Ollama, Qdrant, Unstructured) which have their own SSL configuration.

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Semantic Search Configuration (Optional)

New in v0.58.0: Simplified semantic search configuration with automatic dependency resolution.

The MCP server includes semantic search capabilities powered by vector embeddings. This feature requires a vector database (Qdrant) and an embedding service.

Quick Start

Single-User Mode:

NEXTCLOUD_HOST=http://localhost:8080
NEXTCLOUD_USERNAME=admin
NEXTCLOUD_PASSWORD=password

# Enable semantic search
ENABLE_SEMANTIC_SEARCH=true

# Vector database
QDRANT_LOCATION=:memory:

# Embedding provider
OLLAMA_BASE_URL=http://ollama:11434

Multi-User Login Flow v2 Mode:

NEXTCLOUD_HOST=https://nextcloud.example.com
MCP_DEPLOYMENT_MODE=login_flow_v2
ENABLE_LOGIN_FLOW=true

# Enable semantic search
# In multi-user modes, this AUTOMATICALLY enables background operations!
ENABLE_SEMANTIC_SEARCH=true

# Required for background operations (auto-enabled by semantic search)
TOKEN_ENCRYPTION_KEY=your-key-here
TOKEN_STORAGE_DB=/app/data/tokens.db

# Vector database
QDRANT_URL=http://qdrant:6333

# Embedding provider
OLLAMA_BASE_URL=http://ollama:11434

Note: In multi-user modes (Login Flow v2, Multi-User BasicAuth), enabling ENABLE_SEMANTIC_SEARCH automatically enables background operations and refresh token storage. You don't need to set ENABLE_BACKGROUND_OPERATIONS separately!

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:

# No Qdrant configuration needed - defaults to :memory:
ENABLE_SEMANTIC_SEARCH=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:

# Local persistent storage
QDRANT_LOCATION=/app/data/qdrant  # Or any writable path
ENABLE_SEMANTIC_SEARCH=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:

# Network mode configuration
QDRANT_URL=http://qdrant:6333
QDRANT_API_KEY=your-secret-api-key  # Optional
QDRANT_COLLECTION=nextcloud_content  # Optional
ENABLE_SEMANTIC_SEARCH=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:

# 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:

# 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:

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:

# 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

Background Indexing Configuration

Control background indexing behavior:

# Semantic search (ADR-007, ADR-021)
ENABLE_SEMANTIC_SEARCH=true           # Enable background indexing

# Tuning parameters (advanced - only modify if needed)
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)

Note: The VECTOR_SYNC_* tuning parameters keep their names as they're implementation details. Only the user-facing feature flag was renamed to ENABLE_SEMANTIC_SEARCH.

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:

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:

# 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
ENABLE_SEMANTIC_SEARCH	⚠️ Optional	false	Enable semantic search with background indexing (replaces VECTOR_SYNC_ENABLED)
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	Auto-generated	Qdrant collection name
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)

Deprecated variables (still functional):

• VECTOR_SYNC_ENABLED - Use ENABLE_SEMANTIC_SEARCH instead (will be removed in v1.0.0)

Docker Compose Example

Enable network mode Qdrant with docker-compose:

services:
  mcp:
    environment:
      - QDRANT_URL=http://qdrant:6333
      - ENABLE_SEMANTIC_SEARCH=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:

docker-compose --profile qdrant up

Or use default in-memory mode (no --profile needed):

docker-compose up

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Loading Environment Variables

After creating your .env file, load the environment variables:

On Linux/macOS

# Load all variables from .env
export $(grep -v '^#' .env | xargs)

On Windows (PowerShell)

# Load variables from .env
Get-Content .env | ForEach-Object {
    if ($_ -match '^\s*([^#][^=]*)\s*=\s*(.*)$') {
        [Environment]::SetEnvironmentVariable($matches[1].Trim(), $matches[2].Trim(), "Process")
    }
}

Via Docker

# Docker automatically loads .env when using --env-file
docker run -p 127.0.0.1:8000:8000 --env-file .env --rm \
  ghcr.io/cbcoutinho/nextcloud-mcp-server:latest

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CLI Configuration

Some configuration options can also be provided via CLI arguments. CLI arguments take precedence over environment variables.

OAuth-related CLI Options

uv run nextcloud-mcp-server --help

Options:
  --oauth / --no-oauth            Force OAuth mode (if enabled) or
                                  BasicAuth mode (if disabled). By default,
                                  auto-detected based on environment
                                  variables.
  --oauth-client-id TEXT          OAuth client ID (can also use
                                  NEXTCLOUD_OIDC_CLIENT_ID env var)
  --oauth-client-secret TEXT      OAuth client secret (can also use
                                  NEXTCLOUD_OIDC_CLIENT_SECRET env var)
  --mcp-server-url TEXT           MCP server URL for OAuth callbacks (can
                                  also use NEXTCLOUD_MCP_SERVER_URL env
                                  var)  [default: http://localhost:8000]

Server Options

Options:
  -h, --host TEXT                 Server host  [default: 127.0.0.1]
  -p, --port INTEGER              Server port  [default: 8000]
  -w, --workers INTEGER           Number of worker processes
  -r, --reload                    Enable auto-reload
  -l, --log-level [critical|error|warning|info|debug|trace]
                                  Logging level  [default: info]
  -t, --transport [sse|streamable-http|http]
                                  MCP transport protocol  [default: sse]

App Selection

Options:
  -e, --enable-app [notes|tables|webdav|calendar|contacts|deck]
                                  Enable specific Nextcloud app APIs. Can
                                  be specified multiple times. If not
                                  specified, all apps are enabled.

Example CLI Usage

# OAuth mode with custom client and port
uv run nextcloud-mcp-server --oauth \
  --oauth-client-id abc123 \
  --oauth-client-secret xyz789 \
  --port 8080

# BasicAuth mode with specific apps only
uv run nextcloud-mcp-server --no-oauth \
  --enable-app notes \
  --enable-app calendar

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Configuration Best Practices

For Development

• Use Single-User BasicAuth for the fastest local setup (one user, one app password)
• Store .env file in your project directory
• Add .env to .gitignore

For Production

Pick the mode that matches your deployment topology — there is no single "always" answer:

• Multi-user / hosted — use Login Flow v2. The MCP server registers with the chosen IdP (Nextcloud's built-in OIDC by default; Keycloak, AWS Cognito, etc. via OIDC_DISCOVERY_URL) using static NEXTCLOUD_OIDC_CLIENT_ID / NEXTCLOUD_OIDC_CLIENT_SECRET (generic OIDC creds, preferred) or RFC 7591 DCR (fallback). MCP clients authenticate via OAuth 2.1 + PKCE; per-user Nextcloud access is stored as encrypted app passwords.
• Internal multi-user — Multi-User BasicAuth pass-through (clients send Authorization: Basic headers) is fully supported when users manage their own Nextcloud credentials.
• Personal / self-hosted — Single-User BasicAuth with a Nextcloud app password is the simplest production setup.

In all modes:

• 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

For Docker

Mount two volumes for OAuth-mode deployments:

• /app/.oauth — DCR-registered MCP-client state (only used when DCR is the chosen registration path; harmless to mount otherwise).
• /app/data — encrypted app-password store under Login Flow v2 (TOKEN_STORAGE_DB=/app/data/tokens.db).

docker run \
  -v $(pwd)/.oauth:/app/.oauth \
  -v $(pwd)/data:/app/data \
  --env-file .env \
  ghcr.io/cbcoutinho/nextcloud-mcp-server:latest --oauth

Use Docker secrets for sensitive values in production (TOKEN_ENCRYPTION_KEY, NEXTCLOUD_OIDC_CLIENT_SECRET, NEXTCLOUD_PASSWORD, etc.)

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See Also

• Configuration Migration Guide v2 - New in v0.58.0: Migrate from old variable names
• Authentication - Authentication modes comparison
• Login Flow v2 - Recommended multi-user setup
• Running the Server - Starting the server with different configurations
• Troubleshooting - Common configuration issues
• ADR-021 - Configuration consolidation architecture decision
• ADR-022 - Deployment mode consolidation