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
MCP_DEPLOYMENT_MODE=multi_user_basic

# 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
MCP_DEPLOYMENT_MODE=login_flow

# 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)
MCP_DEPLOYMENT_MODE	✅ Yes	Set to login_flow to select this mode. The Login Flow v2 browser-app-password layer is derived from the mode automatically — no separate flag needed.
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 (used as the audience claim and for browser redirects)
NEXTCLOUD_PUBLIC_ISSUER_URL	✅ Yes	Public URL of Nextcloud (for browser redirects during Login Flow v2)
NEXTCLOUD_OIDC_CLIENT_ID	⚠️ Optional (preferred)	OIDC client ID for the MCP server's relying-party registration with the IdP (Nextcloud OIDC by default; Keycloak / Cognito / etc. via OIDC_DISCOVERY_URL). If unset and the IdP advertises a registration_endpoint, RFC 7591 DCR is used as fallback.
NEXTCLOUD_OIDC_CLIENT_SECRET	⚠️ Optional (preferred)	OIDC client secret paired with NEXTCLOUD_OIDC_CLIENT_ID.
OIDC_DISCOVERY_URL	Optional	Override the IdP discovery URL. Defaults to ${NEXTCLOUD_HOST}/.well-known/openid-configuration (Nextcloud's built-in OIDC). Set to a Keycloak realm or AWS Cognito user-pool discovery URL to use an external IdP.

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

# 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

Startup migrations on existing collections

On the first call to get_qdrant_client() against an existing collection, the server runs two idempotent migrations:

1. Payload-index creation — adds KEYWORD payload indexes for doc_id, user_id, and doc_type. Required by Qdrant for any FieldCondition filter. Cheap; runs even on healthy collections.
2. doc_id backfill — scans the collection once and rewrites any legacy integer doc_id payloads to strings so they match the keyword index. Idempotent: on a clean collection (all doc_id values already str), the scroll runs but emits zero writes. On the first start after the upgrade, expect a delay proportional to total point count for the scroll itself, plus an additional delay proportional to any int-typed doc_id points found while their payloads are rewritten.

Both steps emit INFO-level log lines so operators can track progress.

Operator note: if the server logs TypeError: SemanticSearchResult.id must be int-convertible after upgrading, this indicates a doc_type with non-numeric ids has been indexed but the public response model (SemanticSearchResult.id: int) has not been widened to accept strings. Semantic search itself is not broken — the boundary cast in server/semantic.py is failing loudly on purpose so the discrepancy is caught early. Either widen the public model's id field or convert the id at the verifier layer.

Degraded-migration signals: both startup steps swallow non-fatal failures so the server still starts, but each leaves a distinct ERROR log line that operators should treat as a "restart needed" signal:

• Unexpected error creating payload index on '<field>' (status 5xx) — the index was not created. Searches filtering on that field will keep returning HTTP 400 (Index required but not found) until a subsequent restart succeeds in creating it.
• doc_id backfill scroll failed on '<collection>'; will retry on next restart — the migration sentinel was not written. Legacy integer doc_id payloads remain invisible to the keyword index in the meantime; the scroll re-runs from scratch on the next process start.

Neither prevents the server from accepting requests, but both indicate that vector search is operating in a degraded state on the affected collection until the next clean restart.

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 picks an embedding provider via auto-detection. Priority order (see nextcloud_mcp_server/providers/registry.py):

1. Bedrock — if AWS_REGION or BEDROCK_EMBEDDING_MODEL is set
2. OpenAI — if OPENAI_API_KEY is set
3. Mistral — if MISTRAL_API_KEY is set
4. Ollama — if OLLAMA_BASE_URL is set
5. Simple — fallback when nothing else is configured

Ollama (Recommended for self-hosted)

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

OpenAI

Hosted OpenAI embeddings (or any OpenAI-compatible API via OPENAI_BASE_URL):

OPENAI_API_KEY=sk-...
OPENAI_EMBEDDING_MODEL=text-embedding-3-small  # default
# OPENAI_BASE_URL=https://models.github.ai/inference  # optional

Mistral

Hosted Mistral embeddings. Requires a Mistral API key from console.mistral.ai. Currently embeddings only (no text generation).

MISTRAL_API_KEY=...
MISTRAL_EMBEDDING_MODEL=mistral-embed   # default; produces 1024-dim vectors
# MISTRAL_BASE_URL=https://api.mistral.ai  # optional override (proxies, on-prem)

Switching to or from Mistral forces a new Qdrant collection because the collection name encodes the model (see "Qdrant Collection Naming" above).

Amazon Bedrock

Bedrock provides hosted embedding models (Titan, Cohere) and uses the AWS credential chain (env vars, profiles, or IAM role):

AWS_REGION=us-east-1
BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
# AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY are optional — boto3 will use
# the standard credential chain if not set.

Simple Embedding Provider (Fallback)

If no provider env var is set, the server falls back to a simple deterministic embedding provider for testing. This is not suitable for production as its embeddings have no semantic meaning.

SIMPLE_EMBEDDING_DIMENSION=384  # optional; default 384

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.

Verify-on-Read Latency Budget

Every semantic search request runs an access-control verification pass over its results before returning them, to filter out documents the user can no longer access (deleted, unshared, permissions changed). See ADR-019 for the full design.

This adds Nextcloud round-trips to the search path that operators should be aware of:

• Per-search cost: one Nextcloud round-trip per unique (doc_id, doc_type) in the result set. Chunking means a 10-result page typically references 3-5 unique documents, so verification adds 3-5 round-trips. With the default 20-way concurrency this is one parallel batch — usually under 100 ms on a healthy connection.
• Concurrency: all verifications fan out under a shared semaphore. Tunable via the VERIFICATION_CONCURRENCY env var (settings field verification_concurrency, default 20) — lower it if your Nextcloud backend struggles with the parallel fan-out, or raise it on a healthy connection to speed up large result pages.
• News API caveat: the News app has no per-item endpoint, so the news verifier issues a single news.get_items(batch_size=-1, get_read=True) call per search that contains any news result, then intersects locally. The payload is unbounded — for users with very large feed backlogs this can dominate verification latency. As a rough guide on a healthy LAN connection: a typical purged backlog (1k–5k items) returns in ~200–500 ms; very large backlogs (>20k items) can exceed 2 s and become the dominant cost of any search that surfaces news results. Disabling News in the indexer or running with a smaller backlog mitigates this; per-item paginated verification is tracked as a future improvement.
• Eviction: when verification finds a definitive miss (404 / 403), the corresponding Qdrant points are deleted in the background on a lifespan-owned task group — fire-and-forget, does not block the search response. Eviction failures are logged but never propagated; the next query will re-verify and re-attempt (self-healing).
• Failure modes: transient errors (5xx, network) keep results visible (fail open) so a flaky link does not silently shrink result pages; only definitive 404 / 403 drops them.

If eviction ever needs to be disabled (debugging, benchmarking), the evict_on_missing=False keyword argument on verify_search_results() skips the Qdrant deletes without changing what is returned to the caller. This is a developer/test flag, not an operator knob — it has no env-var equivalent. Operators who need a runtime toggle should open an issue.

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_GENERATION_MODEL	⚠️ Optional	-	Ollama model for text generation
OLLAMA_VERIFY_SSL	⚠️ Optional	true	Verify SSL certificates
OPENAI_API_KEY	⚠️ Optional	-	OpenAI API key (selects OpenAI provider)
OPENAI_BASE_URL	⚠️ Optional	-	OpenAI base URL override (for compatible APIs)
OPENAI_EMBEDDING_MODEL	⚠️ Optional	text-embedding-3-small	OpenAI embedding model
OPENAI_GENERATION_MODEL	⚠️ Optional	-	OpenAI model for text generation
MISTRAL_API_KEY	⚠️ Optional	-	Mistral API key (selects Mistral provider)
MISTRAL_EMBEDDING_MODEL	⚠️ Optional	mistral-embed	Mistral embedding model (1024-dim)
MISTRAL_BASE_URL	⚠️ Optional	-	Mistral base URL override (proxies, on-prem)
AWS_REGION	⚠️ Optional	-	AWS region (selects Bedrock provider)
AWS_ACCESS_KEY_ID	⚠️ Optional	-	AWS access key (boto3 credential chain fallback)
AWS_SECRET_ACCESS_KEY	⚠️ Optional	-	AWS secret key (boto3 credential chain fallback)
BEDROCK_EMBEDDING_MODEL	⚠️ Optional	-	Bedrock embedding model ID
BEDROCK_GENERATION_MODEL	⚠️ Optional	-	Bedrock generation model ID
SIMPLE_EMBEDDING_DIMENSION	⚠️ Optional	384	Dimension for the fallback Simple provider
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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Tag-Based File Exclusion (Optional)

Some files (contracts, medical records, credentials, private notes) should never be exposed to an LLM, even when the assistant has valid credentials for the account. The MCP server can hide such files from all WebDAV tools based on Nextcloud system tags (the same collaborative tags users manage from the Nextcloud UI).

Setup

Set EXCLUDED_TAGS to a comma-separated list of system tag names:

EXCLUDED_TAGS=confidential,no-ai,private

Then create the tags in Nextcloud (one-time, as admin):

docker compose exec app php occ tag:add 'no-ai' --user-visible=true --user-assignable=false

--user-assignable=false is strongly recommended for the threat model this feature is designed to address — see Security considerations below. Tag any file or folder with one of these tags from the Nextcloud UI to hide it from the MCP tools.

Empty (EXCLUDED_TAGS="", the default) disables the feature entirely.

Behaviour

When EXCLUDED_TAGS is set, every WebDAV MCP tool resolves the configured tag names to file paths and applies the following:

Tool	Effect on tagged paths
nc_webdav_list_directory	Excluded files/folders are omitted from listings
nc_webdav_read_file	Raises ToolError (access denied)
nc_webdav_write_file	Raises ToolError (access denied)
nc_webdav_create_directory	Blocked inside excluded paths
nc_webdav_delete_resource	Raises ToolError (access denied)
nc_webdav_move_resource	Blocked when source or destination is excluded
nc_webdav_copy_resource	Blocked when source or destination is excluded
nc_webdav_search_files	Excluded files are filtered from results
nc_webdav_find_by_name	Excluded files are filtered from results
nc_webdav_find_by_type	Excluded files are filtered from results
nc_webdav_list_favorites	Excluded files are filtered from results

Tagging a folder hides the folder itself and every descendant recursively, via path-prefix match.

Security considerations

The threat model is preventing accidental data exfiltration via the LLM tool surface, not hiding files from a determined operator. Specifically:

• Create exclusion tags with user_assignable=false so the credentials the MCP server uses cannot remove the tag from a file (and thereby bypass the exclusion). With user_assignable=true, any user — including the one whose credentials the MCP server uses — can untag a file.
• Optionally set user_visible=false if the exclusion tag itself is sensitive metadata.
• The exclusion is enforced at the MCP tool layer only. Direct WebDAV / Nextcloud client access still sees the files; this feature does not alter Nextcloud's underlying access control.

Performance note

The excluded path set is resolved per WebDAV tool call (1 PROPFIND for each tag name + 1 REPORT per tag). For typical setups (a handful of tagged files under one or two tag names) the overhead is negligible. Caching may be added in a future release.

Scope

This feature only covers WebDAV file operations. Notes, Calendar, Contacts, Deck, etc. are not filtered, because they use ID-based APIs rather than file paths.

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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

---

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

---

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.)

---

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