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: env.sample.oauth-multi-user is a Login Flow v2 quick-start template for multi-user setups. See Login Flow v2.

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

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`	Hosted / OAuth-based MCP clients (claude.ai, Astrolabe Cloud) — recommended for multi-user

You can declare the mode explicitly:

MCP_DEPLOYMENT_MODE=login_flow

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

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

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.

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

# Static OIDC client for the MCP server's own IdP registration.
# Strongly recommended — with Nextcloud's built-in oidc app the DCR
# fallback expires after ~1h (see the warning below). Create the client
# under Administration settings → OpenID Connect provider.
NEXTCLOUD_OIDC_CLIENT_ID=<client-id-from-nextcloud>
NEXTCLOUD_OIDC_CLIENT_SECRET=<client-secret-from-nextcloud>

# 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 used as the OAuth issuer for JWT validation and (by default) the browser-reachable Nextcloud URL for Login Flow v2 redirects. When Nextcloud is its own IdP these coincide.
`NEXTCLOUD_PUBLIC_URL`	Optional (required for external IdPs)	Browser-reachable public URL of Nextcloud for Login Flow v2 login pages and elicitation links. Only needed when the OAuth issuer is a separate IdP (e.g. Keycloak/Cognito): there `NEXTCLOUD_PUBLIC_ISSUER_URL` points at the IdP, so set this to Nextcloud's own URL or the Login Flow v2 login page is built on the IdP origin and 404s. Falls back to `NEXTCLOUD_PUBLIC_ISSUER_URL` then `NEXTCLOUD_HOST` when unset.
`NEXTCLOUD_OIDC_CLIENT_ID`	✅ Strongly recommended	OIDC client ID for the MCP server's relying-party registration with the IdP (Nextcloud's built-in OIDC by default; Keycloak / Cognito / etc. via `OIDC_DISCOVERY_URL`). If unset and the IdP advertises a `registration_endpoint`, the server falls back to RFC 7591 Dynamic Client Registration (DCR) — but with Nextcloud's built-in `oidc` app this fallback breaks after ~1 hour (see warning below). Create a static client and set this instead.
`NEXTCLOUD_OIDC_CLIENT_SECRET`	✅ Strongly recommended	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.
`OIDC_DISCOVERY_MAX_ATTEMPTS`	Optional (default `10`)	Number of attempts for the OIDC discovery fetch performed at startup. Discovery is retried on transport errors (e.g. connect timeouts) and 5xx responses with capped exponential backoff + jitter, so a cold-start network race (e.g. Cilium egress programming) doesn't crashloop the server. `4xx` responses (real misconfiguration) fail immediately. Set to `1` to restore fail-fast-on-first-error behavior.
`OIDC_DISCOVERY_BACKOFF_BASE`	Optional (default `1.0`)	Base delay in seconds for the first discovery retry; subsequent retries grow exponentially (`base * 2**n`) with full jitter.
`OIDC_DISCOVERY_BACKOFF_MAX`	Optional (default `15.0`)	Per-retry cap in seconds for the discovery backoff. With the defaults, worst-case startup blocks on the order of ~90s of backoff (`1+2+4+8+15×5`) plus the per-attempt connect timeouts (~5s each on the LOGIN_FLOW path, 30s on the hybrid multi-user-basic path) before a persistently-down IdP finally exits — size your k8s `startupProbe`/`livenessProbe` accordingly.

⚠️ Use a static OIDC client with Nextcloud's built-in oidc app. If you don't set NEXTCLOUD_OIDC_CLIENT_ID / NEXTCLOUD_OIDC_CLIENT_SECRET, the MCP server registers its own relying-party client via DCR. Nextcloud's oidc app treats DCR clients as ephemeral and deletes them after client_expire_time (default 3600s = 1 hour), pruning on every /authorize. Once it's gone, authorization and token refresh fail and users hit an "Access forbidden" page — permanently, because the server keeps reusing the deleted client. Register a permanent client in Administration settings → OpenID Connect provider and set the two env vars. See Login Flow v2 → Troubleshooting and issue #907.

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

Centralized Token Storage (DATABASE_URL, Optional)

By default the MCP server stores tokens / sessions / app passwords in a local SQLite file (TOKEN_STORAGE_DB, falling back to a per-process tempfile). For HA Kubernetes deployments where you need multiple stateless pods to share state, point the server at a centralized database via DATABASE_URL.

# Centralized Postgres backend (HA k8s deployments)
DATABASE_URL=postgresql+asyncpg://mcp:secret@postgres.svc.cluster.local:5432/mcp
TOKEN_ENCRYPTION_KEY=<fernet-key>

Variable	Required	Description
`DATABASE_URL`	Optional	SQLAlchemy async URL for any supported backend. When set, wins over `TOKEN_STORAGE_DB`. Primary supported targets: `postgresql+asyncpg://...` (recommended for HA) and `sqlite+aiosqlite:///...` (development).
`TOKEN_STORAGE_DB`	Optional	Legacy SQLite-only path. Used when `DATABASE_URL` is unset. Falls back to a per-process ephemeral tempfile when both are unset.
`DATABASE_VERIFY_SSL`	Optional	TLS verification toggle for the Postgres backend. Unset (default) → asyncpg's `prefer` mode (TLS if offered, no verification — keeps cluster-internal Postgres working). `true` → full cert verification. `false` → silence cert errors (homelab / self-signed).
`DATABASE_CA_BUNDLE`	Optional	Path to a PEM file containing a private CA. Implies `DATABASE_VERIFY_SSL=true`. Use this for self-hosted Postgres signed by your homelab CA instead of disabling verification.
`DATABASE_POOL_SIZE`	Deprecated, no-op	Was per-pod SQLAlchemy pool size for the Postgres backend. The engine now uses `NullPool` (one fresh asyncpg connection per checkout) to avoid cross-event-loop crashes under anyio TaskGroups — see ADR-026 § Connection pool and #799. Still accepted for backward compatibility; setting it has no effect.
`DATABASE_MAX_OVERFLOW`	Deprecated, no-op	Was per-pod burst connection cap on top of `DATABASE_POOL_SIZE`. Now ignored (see above).

The asyncpg engine is NullPool-only: each engine.connect() opens and tears down a fresh asyncpg connection in the caller's current event loop. On LAN-local Postgres the per-connection overhead is a single round-trip (~5 ms), so the throughput cost is negligible for the MCP server's traffic shape (low concurrency, bursty per-user requests).

Homelab example (self-signed Postgres with a private CA):

DATABASE_URL=postgresql+asyncpg://mcp:secret@pg.lan:5432/mcp
DATABASE_CA_BUNDLE=/etc/ssl/certs/homelab-ca.pem
TOKEN_ENCRYPTION_KEY=<fernet-key>

Notes:

PyPI extra required. The asyncpg driver is an optional extra so the default pip install nextcloud-mcp-server stays lean. Install with pip install 'nextcloud-mcp-server[postgres]' when using a Postgres URL. The Docker image bundles it by default. When DATABASE_URL=postgresql+asyncpg://... is set without the extra, the server fails fast with a clear actionable error.
Bring-your-own DB. The MCP server doesn't provision the database; it just consumes the URL. Use CNPG, RDS, your existing Helm chart's Postgres sub-chart, etc.
Encryption stays in the app. TOKEN_ENCRYPTION_KEY (Fernet) is applied in Python; the database only ever sees ciphertext for sensitive columns. You don't need pgcrypto.
Schema is managed automatically. On startup the server runs Alembic migrations against the configured backend. Existing SQLite deployments are stamped at the current revision and skip re-execution.
No data migration tool. Moving from SQLite to Postgres is a clean cutover — tokens are reissued on the next login, webhooks re-register on the next sync tick.
Testing a Postgres backend locally: docker compose --profile postgres up -d postgres-test then export DATABASE_URL=postgresql+asyncpg://mcp:mcp@localhost:5433/mcp.

See ADR-026 Pluggable database backend for the architecture rationale.

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
`NEXTCLOUD_HTTP_KEEPALIVE`	⚠️ Optional	`true`	Reuse pooled keep-alive connections for the Nextcloud httpx client. Set to `false` to open a fresh connection per request. See the note below.

NEXTCLOUD_HTTP_KEEPALIVE — With the default (true) the httpx client pools and reuses connections. Setting it to false builds the transport with max_keepalive_connections=0, so every request opens (and closes) its own connection. This is recommended for deployments behind a flaky CDN/WAN path, where a truncated/interrupted response can poison a pooled connection and make later reads silently return empty bytes — see #965. It mirrors the DATABASE_POOL_SIZE→NullPool precedent at the HTTP layer. The trade-off is a TLS handshake per request, which is negligible for a background indexer.

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.

Health & Readiness Probes

The server exposes two Kubernetes probe endpoints:

GET /health/live — liveness. Returns 200 whenever the process is running. It does not check external dependencies, so it never restarts the Pod on an upstream blip.
GET /health/ready — readiness. Gates only on local configuration (NEXTCLOUD_HOST set, auth mode configured). External-dependency reachability (Nextcloud status.php, Qdrant /readyz) is reported in the response body for observability but is non-gating.

Why non-gating (Deck #302): the server typically runs as a single replica per tenant. If readiness failed whenever Nextcloud or Qdrant had a transient blip, the only Pod would be pulled from its Service, leaving the gateway with no upstream — turning a degraded dependency into a total outage and dropping every MCP client's streamable-HTTP session. Dependency health is instead refreshed by a background loop and cached, so the probe path performs no external I/O.

# Cadence (seconds) for the background dependency-health refresh loop (default: 15)
HEALTH_READY_REFRESH_INTERVAL=15

The probe reports each dependency under checks (ok / embedded / pending / error: ...); a non-ok dependency no longer flips the overall status to not_ready.

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!

Search Mode: Keyword-Only (Airgapped) — `SEARCH_MODE`

SEARCH_MODE selects how indexed content is searched (ADR-030):

Value	Behavior	Embedding endpoint
`hybrid` (default)	Dense semantic vectors + BM25 sparse vectors, fused in Qdrant	Required (Ollama/Bedrock/OpenAI/Mistral/gateway)
`keyword`	BM25 sparse (full-text/keyword) only — no dense embeddings	None

Use SEARCH_MODE=keyword for fully airgapped deployments that cannot (or do not want to) run a text-embedding endpoint. You still get the unified cross-app Qdrant index (notes, files, OCR'd PDFs, deck cards, news, mail) and verify-on-read ACLs — just lexical (keyword) matching instead of conceptual similarity. BM25 sparse vectors are computed in-process, so no embedding service is contacted at ingestion or query time.

# Airgapped, no embedding endpoint:
ENABLE_SEMANTIC_SEARCH=true   # keyword mode still uses the Qdrant pipeline
SEARCH_MODE=keyword
QDRANT_URL=http://qdrant:6333
# (no OLLAMA_BASE_URL / Bedrock / OpenAI / gateway needed)

Notes:

keyword still requires ENABLE_SEMANTIC_SEARCH=true — it uses the Qdrant index. With vector sync off the search tools don't register.
The nc_semantic_search / nc_semantic_search_answer tools stay available; results carry search_method="bm25_keyword". The RAG answer tool still works airgapped (retrieval via BM25, answer generated client-side via MCP sampling).
Score caveat: in keyword mode score is a raw BM25 value (unbounded), not a normalized [0,1] fusion score, so a non-zero score_threshold filters very differently. The fusion parameter is ignored.
Switching modes uses a different collection (keyword collections are named …-bm25-keyword). Keyword and hybrid indexes are not interchangeable — to switch, use a fresh collection and let background sync re-ingest.

Qdrant Vector Database Modes

The server supports three Qdrant deployment modes:

In-Memory Mode (Default) - Simplest for development and testing
Persistent Local Mode - For single-instance deployments with persistence
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:

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.
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=2048              # Characters per chunk (default: 2048)
DOCUMENT_CHUNK_OVERLAP=200            # Overlapping characters between chunks (default: 200)

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.

Enabling document parsing — `ENABLE_DOCUMENT_PROCESSING` (master switch)

ENABLE_DOCUMENT_PROCESSING is the master switch for the parsing subsystem and is off by default. Set it before configuring unstructured, the OCR tier, or docling below — otherwise those processors are never registered and parsing silently no-ops (a common first-run trip-up):

ENABLE_DOCUMENT_PROCESSING=true       # register optional processors + on-demand parsing (default: false)

What it controls:

Registers the optional processors — unstructured, tesseract, custom, and docling — into the shared registry at startup (initialize_document_processors()). The built-in PDF tiers (pypdfium2_fast → fast, pymupdf → structured, and the ocr tier) are always registered independently of this flag.
Gates on-demand parsing in nc_webdav_read_file. With it off the tool returns the raw file (base64) and a force_processor=… argument is rejected as an unknown processor (the parse registry is empty). With it on, the tool parses the file inline and honours force_processor.

Consequently the docling image and force-PDF touchpoints (which flow through the registry / nc_webdav_read_file) require this flag; the automatic scanned-PDF OCR touchpoint rides the always-registered ocr tier, so it needs only DOCUMENT_OCR_ENABLED + DOCUMENT_OCR_PROVIDER (see the recipe table under Docling below).

Document parsing robustness (PDF)

These guard the parse/OCR tiers against pathological PDFs. Defaults are safe; tune per tenant when a corpus has very large scans or a gateway with its own shorter OCR ceiling:

DOCUMENT_PARSE_TIMEOUT_SECONDS=120    # Wall-clock cap per isolated parse (default: 120)
DOCUMENT_OCR_TIMEOUT_SECONDS=180      # OCR backend request timeout (default: 180)
DOCUMENT_MAX_PDF_SIZE_MB=50           # Pre-parse size cap; 0 disables (default: 50)

A PDF larger than DOCUMENT_MAX_PDF_SIZE_MB fails fast with reason oversize (exported on astrolabe_document_parse_failed_total{reason="oversize"}) instead of being handed to the tiers, where a 40+ MB scan would otherwise burn the full OCR timeout for zero recovered text.

OCR tier configuration

OCR is a single configurable escalation tier. Enable it and choose the backend, model, and execution mode:

DOCUMENT_OCR_ENABLED=true              # route scanned/no-text-layer PDFs to OCR (default: false)
DOCUMENT_OCR_PROVIDER=auto            # "auto" | "gateway" | "mistral" | "docling" | "none"
DOCUMENT_OCR_MODEL=mistral/mistral-ocr-latest  # provider-namespaced model id

DOCUMENT_OCR_PROVIDER selects the backend: gateway posts to the Astrolabe Cloud model gateway's POST /v1/ocr (no provider keys in the pod; the gateway routes on the model's <provider>/ prefix, so it serves Mistral, surya, etc.); mistral calls the Mistral OCR API directly (MISTRAL_API_KEY); docling posts scanned/no-text-layer PDFs to a self-hosted docling-serve instance (DOCLING_API_URL); auto prefers the gateway (if EMBEDDING_GATEWAY_URL is set) then direct Mistral (auto never selects docling — it needs an explicit self-hosted URL); none disables OCR.
DOCUMENT_OCR_MODEL is the provider-namespaced model id — e.g. mistral/mistral-ocr-latest (Mistral) or surya/surya-ocr-2 (surya, via the gateway). The gateway routes on the prefix; the direct Mistral backend strips it. (Ignored by the docling backend, which uses the docling-serve instance's own OCR engine.)

Docling (docling-serve) — photographed / scanned / handwritten text

docling has notably stronger OCR than unstructured for photographed, scanned and handwritten documents. The MCP server talks to an external docling-serve instance over HTTP — no ML dependencies are added to the server image. Run one via the docling docker-compose profile (docker compose --profile docling up -d).

ENABLE_DOCLING=false                  # master switch for the docling touchpoints
DOCLING_API_URL=http://docling:5001   # docling-serve base URL (required)
DOCLING_TIMEOUT=120                   # image/force conversion timeout (seconds)
DOCLING_OCR_LANG=en,de                # engine-dependent codes (EasyOCR: en,de; Tesseract: eng,deu)
DOCLING_DO_OCR=true                   # run OCR (vs. text-layer extraction only)

Required configuration per use case (auto never selects docling — it needs an explicit self-hosted URL):

Use case	Minimal env
Images auto-route to docling	`ENABLE_DOCUMENT_PROCESSING=true` + `ENABLE_DOCLING=true` + `DOCLING_API_URL`
Force docling on a text-layer PDF (`force_processor="docling"`)	`ENABLE_DOCUMENT_PROCESSING=true` + `ENABLE_DOCLING=true` + `DOCLING_API_URL`
Scanned / no-text-layer PDFs auto-OCR via docling	`DOCUMENT_OCR_ENABLED=true` + `DOCUMENT_OCR_PROVIDER=docling` + `DOCLING_API_URL`

The scanned-PDF row deliberately omits ENABLE_DOCLING/ENABLE_DOCUMENT_PROCESSING: that path rides the always-registered ocr tier during indexing (so it also needs ENABLE_SEMANTIC_SEARCH=true), not the on-demand registry. The image and force-PDF rows need the ENABLE_DOCUMENT_PROCESSING master switch (see above).

Docling plugs in at three points:

Images (automatic). With ENABLE_DOCLING=true + DOCLING_API_URL set, image files (image/jpeg, image/png, image/tiff, image/bmp, image/gif, image/webp) always route to docling — it registers at a higher priority than unstructured. DOCLING_DO_OCR toggles OCR on this image path only (the scanned-PDF OCR backend always OCRs). Requires ENABLE_DOCUMENT_PROCESSING=true. If DOCLING_API_URL is unset the processor is not registered, so a bare ENABLE_DOCLING never shadows other image processors with a dead endpoint.
Scanned PDFs (automatic). Set DOCUMENT_OCR_ENABLED=true + DOCUMENT_OCR_PROVIDER=docling. PDFs whose text layer the tier-0 classifier finds missing/unusable escalate to the OCR tier and are transcribed by docling. Born-digital (text-layer) PDFs still use the cheap local fast/structured tiers — docling is only paid for genuine scans.
Text-layer PDFs (on demand). Pass force_processor="docling" to the nc_webdav_read_file MCP tool to re-parse any file with docling even when it already has a text layer — useful when that layer misses tables/figures or is incomplete. Docling returns markdown, preserving table structure. An unknown or unconfigured processor name returns a clear tool error.

Office formats (DOCX/PPTX/XLSX) deliberately stay with unstructured — docling is scoped to the image/scan/handwriting use case here. OCR language codes are engine-dependent: the docling-serve default engine (EasyOCR) uses two-letter codes (en,de); a Tesseract-backed instance wants eng,deu. The synchronous convert endpoint has an observed ~2 min practical ceiling (from our testing, not a hard server-enforced limit), so a larger DOCLING_TIMEOUT (e.g. 300s for slow CPU OCR) simply lets a slow conversion finish; very large scans are future work (async submit/poll). See docs/ADR-031-docling-document-parsing-backend.md.

OCR execution mode: synchronous vs batch (Deck #332)

The OCR tier has two execution modes, selected by DOCUMENT_OCR_MODE:

DOCUMENT_OCR_MODE=sync                 # "sync" (default) | "batch"
DOCUMENT_OCR_BATCH_POLL_SECONDS=120    # re-poll cadence for a batch job (default: 120)
DOCUMENT_OCR_BATCH_MAX_WAIT_SECONDS=86400  # give up + mark timeout after this (default: 24h)

sync (default) — transcribe the document inline via the backend's synchronous path (POST /v1/ocr for the gateway, or the direct Mistral OCR API). The document is parsed in a single call.
batch — submit the document to the gateway's async Batch OCR job (POST /v1/ocr/batch) and re-poll GET /v1/ocr/batch/{job_id} until it finishes. This trades latency (a batch job runs minutes–hours) for roughly half the OCR cost, so it suits large-corpus backfill rather than interactive ingest.

Batch mode is opt-in and gateway-routed: the embedding gateway is the batching layer, so batch OCR always routes through the gateway's batch routes (no provider keys in the pod). This is how batch works even when the chosen sync backend is direct Mistral — we leverage the gateway to batch for backends that have no native batch path from the pod. Because it needs a gateway, DOCUMENT_OCR_MODE=batch requires EMBEDDING_GATEWAY_URL: the server rejects the combination at startup rather than silently downgrading to synchronous OCR. Batch also requires the Postgres ingest queue (the per-tier procrastinate workers); the in-process (INGEST_QUEUE=memory) pipeline can't defer a poll, so use DOCUMENT_OCR_MODE=sync there.

Mechanics: the OCR tier submits the job, records its id in the batch_ocr_jobs app-DB table (keyed on the document + its etag), and raises a re-poll deferral so procrastinate re-runs the tier after DOCUMENT_OCR_BATCH_POLL_SECONDS — releasing the worker slot between polls (a long batch never pins a worker or is reclaimed as stalled). On completion the per-page markdown is indexed exactly like the sync path; a failure or a job exceeding DOCUMENT_OCR_BATCH_MAX_WAIT_SECONDS marks the document parse-failed. Each poll re-fetches + re-classifies the PDF (a known v1 inefficiency, bounded by the poll cadence); one batch job is submitted per document (coalescing many documents per job is a planned follow-up).

Upgrade notes: OCR-tier consolidation

The two OCR rungs (ocr-incluster / ocr-upstream) were merged into one ocr tier. When upgrading from a deployment that used the split tiers:

DOCUMENT_OCR_INCLUSTER_ENABLED / DOCUMENT_OCR_INCLUSTER_MODEL are removed. Configure the single tier with DOCUMENT_OCR_PROVIDER + DOCUMENT_OCR_MODEL (the gateway routes on the model's <provider>/ prefix, so surya is reached by setting e.g. DOCUMENT_OCR_PROVIDER=gateway + DOCUMENT_OCR_MODEL=surya/surya-ocr-2).
Dead-letter retry burst. The dead-letter signature dropped its ocric= component, so a deployment that had DOCUMENT_OCR_INCLUSTER_ENABLED=true will see its signature change on upgrade and automatically re-attempt documents that were dead-lettered while the in-cluster rung was on. This is intended (those docs can OCR via the unified tier) but can produce a burst of OCR jobs on scanned-doc-heavy instances right after deploy — expect it and scale the OCR worker fleet accordingly.
Batch mode now fails loud instead of downgrading to sync. DOCUMENT_OCR_MODE=batch requires the embedding gateway (rejected at startup without EMBEDDING_GATEWAY_URL) and the Postgres ingest queue. On the in-process (INGEST_QUEUE=memory) path — which can't defer a poll — batch raises at ingest time rather than silently transcribing synchronously; use DOCUMENT_OCR_MODE=sync there.
In-flight jobs are drained. Jobs still parked on the old ingest-ocr-incluster / ingest-ocr-upstream queues during a rolling upgrade are processed by the new ocr worker and routed back to the single ocr tier — nothing is stranded.

Embedding Service Configuration

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

Bedrock — if AWS_REGION or BEDROCK_EMBEDDING_MODEL is set
OpenAI — if OPENAI_API_KEY is set
Mistral — if MISTRAL_API_KEY is set
Ollama — if OLLAMA_BASE_URL is set
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 (1024-1536 characters):

More precise matching
Less context per chunk
Better for finding specific information
Higher storage requirements (more vectors)

Larger chunks (3072-4096 characters):

More context per chunk
Less precise matching
Better for understanding broader topics
Lower storage requirements (fewer vectors)

Default (2048 characters):

Balanced approach suitable for most use cases
Works well with typical note lengths
Good compromise between precision and context

For PDFs, DOCUMENT_CHUNK_PAGE_AWARE (default true) overrides this trade-off by chunking one page at a time — see the entry below.

Choosing Overlap

Overlap preserves context across chunk boundaries. Recommended settings:

10-20% of chunk size (e.g., 200-400 characters for 2048-character 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=1024
DOCUMENT_CHUNK_OVERLAP=100

# Default balanced configuration
DOCUMENT_CHUNK_SIZE=2048
DOCUMENT_CHUNK_OVERLAP=200

# More context for long documents
DOCUMENT_CHUNK_SIZE=4096
DOCUMENT_CHUNK_OVERLAP=400

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 — except file and news_item, which each batch into a single call per search regardless of how many results they contribute (see the Files and News caveats below). 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.
Files caveat: file results are gated on current vector-index tag membership, not bare access — the verifier issues a single find_files_by_tag(<tag>, mime_type_filter="application/pdf") REPORT per search that contains any file result (plus a one-shot EXCLUDED_TAGS lookup), then keeps only files in that set. This matches exactly what the scanner indexes, so a file removed from the tag (or deleted, or moved under an excluded folder) drops out of results immediately rather than waiting for the scanner sweep. The REPORT expands tagged folders via a Depth: infinity SEARCH, so deployments that tag whole directory trees pay that walk once per search; configure VECTOR_SYNC_PDF_TAG to change the tag name. The file verifier's latency therefore scales with both the Depth: infinity folder expansion and the EXCLUDED_TAGS lookup: that lookup fans out ~2 WebDAV calls (1 PROPFIND + 1 REPORT) per excluded tag, concurrently, while holding a single verification slot — so a deployment with a long EXCLUDED_TAGS list and/or deeply tagged trees issues many parallel Nextcloud requests per search. Operators in that situation may want to lower VERIFICATION_CONCURRENCY so the file verifier's internal fan-out does not overwhelm the backend.
Shared files: a file an owner tagged and shared with the searcher only survives verification if the owner's userVisible tag surfaces in the searcher's tag REPORT. The MCP server's own tag-creation path (WebDAVClient.get_or_create_tag) defaults to user_visible=True, so tags it creates are fine. Migration caveat: if the vector-index tag was created some other way — manually via occ tag:add … --user-visible=false, or in a deployment predating this release — it may be user_visible=False (the Nextcloud default for system-managed tags). In that case an owner's tag will not surface in a recipient's systemtag REPORT, so every shared-file result is silently dropped for recipients after upgrading — no error, just a narrower result set. Verify the tag's visibility (Administration → Collaborative tags, or occ tag:list) and, if it is not user-visible, recreate it as user-visible so shared search keeps working.
Eviction: when verification finds a definitive miss (a 404 / 403, or — for files — absence from the tag set), 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 misses (404 / 403, or a file no longer in the tag set) drop them. If the file tag REPORT itself errors, all file results are kept (fail open).

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`)
`SEARCH_MODE`	⚠️ Optional	`hybrid`	`hybrid` (dense+sparse) or `keyword` (BM25 sparse only, no embedding endpoint — airgapped, ADR-030). `keyword` still requires `ENABLE_SEMANTIC_SEARCH=true`
`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
`QDRANT_INIT_MAX_ATTEMPTS`	⚠️ Optional	`30`	Attempts for the startup Qdrant-collection init. Transient connection failures (Qdrant briefly unreachable during a rolling deploy) are retried with capped exponential backoff + jitter instead of crashlooping with a full traceback; genuine errors (auth/config, e.g. a 4xx) fail immediately. Set to `1` to restore fail-fast.
`QDRANT_INIT_BACKOFF_BASE`	⚠️ Optional	`1.0`	Base delay (seconds) for the first Qdrant-init retry; subsequent retries grow exponentially (`base * 2**n`) with full jitter.
`QDRANT_INIT_BACKOFF_MAX`	⚠️ Optional	`10.0`	Per-retry cap (seconds) for the Qdrant-init backoff. Size your k8s `startupProbe` accordingly (worst case ≈ `max_attempts × backoff_max` of waiting on a persistently-down Qdrant before startup finally fails).
`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	`2048`	Characters per chunk for document embedding
`DOCUMENT_CHUNK_OVERLAP`	⚠️ Optional	`200`	Overlapping characters between chunks (must be < chunk size)
`DOCUMENT_CHUNK_PAGE_AWARE`	⚠️ Optional	`true`	Split PDFs on page boundaries first (one chunk per page; oversized pages split within the page). Exact page numbers, clean snippets, and a predictable ~1 chunk/page when chunk size ≥ the largest page. Set `false` for the legacy char-based path.

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

Decomposition Hook Points (Optional, Advanced)

The server can optionally offload embeddings to an external gateway and split ingest into a separate scale-to-zero worker process (Deck #183). These are opt-in; every default reproduces the in-process monolith behavior, so self-hosters can ignore this section.

# Embeddings via an OpenAI-compatible gateway (else: autodetect — see above)
EMBEDDING_PROVIDER=gateway
EMBEDDING_GATEWAY_URL=https://embedding-gateway.internal
# Gateway M2M OIDC client (its own realm; leave unset to call it unauthenticated)
EMBEDDING_GATEWAY_TOKEN_URL=...
EMBEDDING_GATEWAY_CLIENT_ID=...
EMBEDDING_GATEWAY_CLIENT_SECRET=...

# Ingest queue backend. Default (unset) is "memory" — the in-process anyio
# queue — *regardless of DATABASE_URL*. procrastinate is strictly opt-in: set
# INGEST_QUEUE=postgres to split ingest into a separate worker (requires a
# PostgreSQL DATABASE_URL). A Postgres DATABASE_URL alone never enables it.
INGEST_QUEUE=postgres         # memory | postgres
# Process role (informational; the worker is launched via the `worker` command):
MCP_ROLE=all                  # api | worker | all (default)
TENANT_ID=<uuid>              # per-tenant identity (used in collection naming)

Postgres ingest queue + worker (api/worker split)

This is opt-in. By default (INGEST_QUEUE=memory) the scanner processes changed documents in-process via anyio task groups in the API pod — no procrastinate, no separate worker, even when DATABASE_URL is Postgres.

When you explicitly set INGEST_QUEUE=postgres (against a PostgreSQL DATABASE_URL), the scanner instead defers one job per changed document into the app's Postgres via procrastinate; a separate worker process drains the queue (fetch → chunk → embed → upsert Qdrant). Run the two roles as separate Deployments from the same image:

# API pod (always-on): serves MCP/query + runs the scanner (defers jobs)
nextcloud-mcp-server run

# Ingest worker (scale-to-zero on queue depth via KEDA): drains the queue
nextcloud-mcp-server worker -c 4

Notes:

procrastinate manages its own tables (procrastinate_jobs, …) in the same database. They are created on a fresh DB by the API pod at startup and by nextcloud-mcp-server db upgrade — a migration lineage independent of the app's Alembic schema. procrastinate is Postgres-only (psycopg3); it ships in the [postgres] extra and is imported lazily.
KEDA scales the worker on SELECT count(*) FROM procrastinate_jobs WHERE queue_name='ingest' AND status='todo'.
INGEST_QUEUE=postgres with a SQLite DATABASE_URL is rejected at startup.
Teardown: because procrastinate's schema is a separate lineage, nextcloud-mcp-server db downgrade (Alembic) does not drop the procrastinate_* tables. To fully revert (e.g. back to NATS or SQLite-only), drop them manually after downgrading: DROP TABLE IF EXISTS procrastinate_jobs, procrastinate_events, procrastinate_periodic_defers, procrastinate_workers CASCADE; (plus the procrastinate_* types/functions if removing the extension entirely).

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.

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.)

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Configuration

Quick Start

Deployment Mode Selection

Single-User BasicAuth Mode

Multi-User BasicAuth Mode

Login Flow v2 Mode

Centralized Token Storage (DATABASE_URL, Optional)

SSL/TLS Configuration (Optional)

Custom CA Bundle (Recommended)

Disable Verification (Development Only)

Environment Variables Reference

Scope

Health & Readiness Probes

Semantic Search Configuration (Optional)

Quick Start

Search Mode: Keyword-Only (Airgapped) — SEARCH_MODE

Qdrant Vector Database Modes

1. In-Memory Mode (Default)

2. Persistent Local Mode

3. Network Mode

Qdrant Collection Naming

Auto-Generated Naming (Default)

Switching Embedding Models

Startup migrations on existing collections

Explicit Override

Multi-Server Deployments

Dimension Validation

Background Indexing Configuration

Enabling document parsing — ENABLE_DOCUMENT_PROCESSING (master switch)

Document parsing robustness (PDF)

OCR tier configuration

Docling (docling-serve) — photographed / scanned / handwritten text

OCR execution mode: synchronous vs batch (Deck #332)

Upgrade notes: OCR-tier consolidation

Embedding Service Configuration

Ollama (Recommended for self-hosted)

OpenAI

Mistral

Amazon Bedrock

Simple Embedding Provider (Fallback)

Document Chunking Configuration

Choosing Chunk Size

Choosing Overlap

Verify-on-Read Latency Budget

Environment Variables Reference

Docker Compose Example

Decomposition Hook Points (Optional, Advanced)

Postgres ingest queue + worker (api/worker split)

Tag-Based File Exclusion (Optional)

Setup

Behaviour

Security considerations

Performance note

Scope

Loading Environment Variables

On Linux/macOS

On Windows (PowerShell)

Via Docker

CLI Configuration

OAuth-related CLI Options

Server Options

App Selection

Example CLI Usage

Configuration Best Practices

For Development

For Production

For Docker

See Also

Search Mode: Keyword-Only (Airgapped) — `SEARCH_MODE`

Enabling document parsing — `ENABLE_DOCUMENT_PROCESSING` (master switch)