mad-mcp-qdrant 1.8.0

MCP server for retrieving context from a Qdrant vector database

MADPANDA3D QDRANT MCP

Manage your Vector Database how you see fit

MADPANDA3D QDRANT MCP is a production-ready Model Context Protocol server for Qdrant. It turns your vector store into a managed memory layer with structured controls for ingest, retrieval, validation, and ongoing cleanup.

Use it to keep memories clean, deduped, and relevance-tuned over time. The toolkit includes safe dry-run previews, bulk maintenance jobs with progress reporting, and operational guardrails so agents can manage your database at scale without chaos.

Overview

This server is designed for production workloads and supports hosted header-auth access for clients like n8n, Claude Desktop, and other MCP-capable agents.

Hosted MCP (Header Auth)

Use the MADPANDA3D hosted endpoint:

https://qdrant-mcp.madpanda3d.com/mcp

Note: https://qdrant-mcp.madpanda3d.com/mcp/ is deprecated and returns 410 Gone with a migration message.

n8n setup:

1. Add MCP tool node to your agent.
2. Add the MCP endpoint URL.
3. Set Server transport to HTTP streamable.
4. Set Auth to Multiple Headers Auth.
5. Add headers:
   • X-MADPANDA-PORTAL-GRANT
   • X-Qdrant-Url
   • X-Qdrant-Api-Key
   • X-Collection-Name (optional)
   • X-Embedding-Provider (optional; required for semantic search + write tools)
   • X-Embedding-Model (optional; required with X-Embedding-Provider)
   • X-OpenAI-Api-Key (required when X-Embedding-Provider=openai)
6. Save the auth credentials.
7. Set Tools to include → All.

n8n Setup

Screenshots below show the MCP node configuration in n8n.

Deploy

• 

Quickstart

Install (pip)

pip install mad-mcp-qdrant

Run with Docker

docker build -t mcp-server-qdrant .
docker run -d --name mcp-qdrant \
  --restart unless-stopped \
  --network mcp-network \
  --env-file .env \
  mcp-server-qdrant

Or use the hosted-standard compose file:

cp .env.example .env
docker compose up -d --build

Auto-update with Watchtower (GHCR)

# Run from GHCR (make the package public or login to GHCR first).
docker run -d --name mcp-qdrant \
  --network mcp-network \
  --env-file .env \
  --label com.centurylinklabs.watchtower.enable=true \
  ghcr.io/<owner>/mad-mcp-qdrant:latest \
  mcp-server-qdrant --transport streamable-http

# Watchtower will pull new release images and restart the container.
docker run -d --name watchtower \
  --restart unless-stopped \
  -v /var/run/docker.sock:/var/run/docker.sock \
  containrrr/watchtower \
  --cleanup --interval 1800 --label-enable

Run locally (uvx)

QDRANT_URL=... COLLECTION_NAME=... uvx mad-mcp-qdrant

Prefer mad-mcp-qdrant; mcp-server-qdrant remains as a compatible alias.

Tools

Most mutating tools support dry_run + confirm and return a dry_run_diff preview for safer approvals.

Search tools are token-safe by default. qdrant-find, qdrant-find-short-term, and qdrant-recommend-memories return compact results unless response_mode="payload" is requested. Start with top_k=3-5, add filters when possible, and increase detail only after selecting a specific result. For large content, prefer qdrant-ingest-document or qdrant-ingest-textbook instead of sending long text directly to store tools. For school work, start with qdrant-study-search; it defaults to the MCP_STUDY_COLLECTION collection and exposes course, subject, material type, module, week, status, title, author, document, and chapter filters directly.

Core Memory Tools

• qdrant-store: store a single memory point with metadata.
• qdrant-cache-memory: store short-term memory with a TTL in a cache collection.
• qdrant-promote-short-term: promote short-term memories into the long-term collection.
• qdrant-ingest-with-validation: validate inputs, optionally quarantine, then store.
• qdrant-ingest-document: chunk a document and store as multiple points.
  • PDF ingestion runs native text extraction plus OCR by default (ocr=true).
  • OCR is local (Tesseract via pytesseract + pdf2image), not a third-party API.
• qdrant-ingest-textbook: submit an async textbook PDF ingestion job (source_url-only).
  • Returns immediately with job_id for large-file workflows.
  • Requires metadata: class, material_type, title, author, edition, isbn.
  • Uses streaming download + page-wise extraction to reduce memory pressure.
  • With ocr=true, applies OCR coverage-gating (blank/low-text pages first) and fails fast if target coverage cannot be met within budget.
  • Job status is persisted on disk so restart scenarios return structured failure instead of Job not found.
• qdrant-study-search: search school/study materials with compact output and direct course/week/module/material filters.
• qdrant-find: query vectors with filters and return matches.
• qdrant-find-short-term: query the short-term memory cache collection.
• qdrant-recommend-memories: recommend memories using positive/negative examples.
• qdrant-update-point: update payload fields for a point id.
• qdrant-patch-payload: patch specific payload keys for a point id.
• qdrant-tag-memories: append or replace labels for a set of points.
• qdrant-link-memories: link memories via related ids and optional associations.
• qdrant-list-points: scroll point ids in a collection with filters.
• qdrant-get-points: fetch points by id list with payload/vectors.
• qdrant-count-points: count points that match optional filters.

Agent Navigation

• qdrant-check-configuration: check hosted readiness, accepted headers, cache settings, and missing configuration without returning secrets.
• qdrant-list-capabilities: list compact capability groups and common workflows.
• qdrant-get-endpoint-coverage: summarize Qdrant endpoint coverage and documented exclusions from docs/endpoint-coverage.md.
• qdrant-get-tool-usage: inspect a tool schema, annotations, and token-safe usage guidance.

Housekeeping + Quality

• qdrant-audit-memories: scan for missing fields, bad payloads, and duplicates.
• qdrant-backfill-memory-contract: populate missing metadata fields at scale.
• qdrant-bulk-patch: patch payloads in bulk by filter or ids (dry-run supported).
• qdrant-dedupe-memories: dedupe exact matches by hash.
• qdrant-find-near-duplicates: cluster semantic near-duplicates.
• qdrant-merge-duplicates: merge duplicate groups into a canonical point.
• qdrant-reembed-points: recompute embeddings for selected points.
• qdrant-expire-memories: delete/archive memories past expires_at_ts.
• qdrant-expire-short-term: delete expired memories from the short-term cache.
• qdrant-delete-points: delete points by id list.
• qdrant-delete-by-filter: delete points that match a filter.
• qdrant-delete-document: delete all chunks for a document id.

Jobs + Progress

• qdrant-submit-job: start a background maintenance job.
• qdrant-get-ingest-status: ingest-focused status/metrics for textbook jobs.
• qdrant-cancel-ingest: cancel a running textbook ingest job.
• qdrant-job-status: get job status and summary.
• qdrant-job-progress: read progress counters and phase.
• qdrant-job-logs: tail recent logs for a job.
• qdrant-job-result: fetch the final job result.
• qdrant-cancel-job: cancel a running job.

Collection + Admin

• qdrant-health-check: validate collection health and expected indexes.
• qdrant-metrics-snapshot: capture collection stats and index coverage.
• qdrant-ensure-payload-indexes: create missing payload indexes.
• qdrant-optimizer-status: report optimizer and segment status.
• qdrant-update-optimizer-config: update optimizer settings (admin).
• qdrant-list-collections: list all collections.
• qdrant-create-collection: create a collection with embedding-compatible vector settings and default payload indexes.
• qdrant-collection-exists: check if a collection exists.
• qdrant-collection-info: fetch collection config and metadata.
• qdrant-collection-stats: read collection stats (points, segments).
• qdrant-collection-vectors: show vector config for the collection.
• qdrant-collection-payload-schema: list payload schema + indexed fields.
• qdrant-get-vector-name: resolve the active vector name.
• qdrant-list-aliases: list all aliases.
• qdrant-collection-aliases: list aliases for a collection.
• qdrant-collection-cluster-info: cluster and shard info.
• qdrant-list-snapshots: list collection snapshots.
• qdrant-list-full-snapshots: list full snapshots on the server.
• qdrant-list-shard-snapshots: list shard snapshots for a collection.
• qdrant-create-snapshot: create a new collection snapshot.
• qdrant-restore-snapshot: restore a snapshot into a collection.

Configuration

Environment Variables

Name	Description	Default Value
MCP_PORTAL_GRANT_TOKEN	Required grant token for /mcp HTTP traffic from the MAD MCP Portal	unset
QDRANT_URL	URL of the Qdrant server	None
QDRANT_API_KEY	API key for the Qdrant server	None
COLLECTION_NAME	Name of the default collection to use.	None
QDRANT_VECTOR_NAME	Override vector name used by the MCP server	None
QDRANT_LOCAL_PATH	Path to the local Qdrant database (alternative to QDRANT_URL)	None
QDRANT_SEARCH_LIMIT	Default result limit when top_k is omitted	10
QDRANT_READ_ONLY	Hide mutating tools when enabled	false
QDRANT_ALLOW_ARBITRARY_FILTER	Allow raw Qdrant filter input	false
EMBEDDING_PROVIDER	Embedding provider to use (fastembed or openai)	fastembed
EMBEDDING_MODEL	Name of the embedding model to use	sentence-transformers/all-MiniLM-L6-v2
EMBEDDING_VECTOR_SIZE	Vector size override (required for unknown OpenAI models)	unset
EMBEDDING_VERSION	Embedding version label stored with each memory	unset
OPENAI_API_KEY	OpenAI API key (required for openai provider)	unset
OPENAI_BASE_URL	OpenAI-compatible base URL (optional)	unset
OPENAI_ORG	OpenAI organization ID (optional)	unset
OPENAI_PROJECT	OpenAI project ID (optional)	unset
TOOL_STORE_DESCRIPTION	Custom description for the store tool	See default in src/mcp_server_qdrant/settings.py
TOOL_FIND_DESCRIPTION	Custom description for the find tool	See default in src/mcp_server_qdrant/settings.py
MCP_ADMIN_TOOLS_ENABLED	Enable admin-only tools (optimizer updates)	false
MCP_MUTATIONS_REQUIRE_ADMIN	Require admin access for mutating tools	false
MCP_MAX_BATCH_SIZE	Max batch size for bulk operations	500
MCP_MAX_POINT_IDS	Max point id list size	500
MCP_STRICT_PARAMS	Reject unknown keys/filters and oversized text	false
MCP_MAX_TEXT_LENGTH	Max text length before chunking	8000
MCP_TEXTBOOK_MAX_FILE_BYTES	Max textbook file size (bytes) for async ingest	104857600
MCP_TEXTBOOK_MAX_PAGES	Max textbook page count for async ingest	1000
MCP_TEXTBOOK_MAX_EXTRACTED_CHARS	Max extracted characters for async textbook ingest	3000000
MCP_TEXTBOOK_MAX_CHUNKS	Max chunk count for async textbook ingest	20000
MCP_TEXTBOOK_OCR_LOW_TEXT_THRESHOLD_CHARS	Chars/page threshold considered low-text during OCR gating	120
MCP_TEXTBOOK_OCR_MIN_COVERAGE_RATIO	Minimum acceptable text coverage ratio after OCR budget pass	0.85
MCP_TEXTBOOK_OCR_MAX_PAGES	Max number of pages eligible for OCR in textbook ingest	120
MCP_TEXTBOOK_OCR_MAX_PAGE_RATIO	Max OCR page budget as ratio of total pages	0.30
MCP_TEXTBOOK_JOB_TIMEOUT_SECONDS	Timeout for async textbook ingest jobs	2700
MCP_TEXTBOOK_EMBED_BATCH_SIZE	Embed batch size for textbook jobs	32
MCP_TEXTBOOK_UPSERT_BATCH_SIZE	Upsert batch size for textbook jobs	64
MCP_TEXTBOOK_MAX_CONCURRENCY	Max concurrent textbook embed/upsert workers	2
MCP_TEXTBOOK_JOB_STATE_DIR	Local directory used to persist async textbook job state	/tmp/mcp-server-qdrant/jobs
MCP_TEXTBOOK_JOB_STATE_RETENTION_HOURS	Retention window for terminal persisted job state	168
MCP_QUERY_EMBEDDING_CACHE_SIZE	Max cached query embeddings; set 0 to disable	256
MCP_QUERY_EMBEDDING_CACHE_TTL_SECONDS	Query embedding cache TTL seconds	3600
MCP_DEDUPE_ACTION	Dedupe behavior (update or skip)	update
MCP_INGEST_VALIDATION_MODE	Validation mode (allow, reject, quarantine)	allow
MCP_QUARANTINE_COLLECTION	Collection name for quarantined memories	jarvis-quarantine
MCP_HEALTH_CHECK_COLLECTION	Default collection for health check	unset
MCP_SHORT_TERM_COLLECTION	Collection name for short-term memory cache	jarvis-short-term
MCP_STUDY_COLLECTION	Default collection for qdrant-study-search	school
MCP_SHORT_TERM_TTL_DAYS	Default TTL (days) for short-term memory cache	7
MCP_SERVER_VERSION	Optional git SHA for telemetry	unset
MCP_ALLOW_REQUEST_OVERRIDES	Allow per-request Qdrant headers	false
MCP_REQUIRE_REQUEST_QDRANT_URL	Require X-Qdrant-Url when overrides enabled	true
MCP_REQUIRE_REQUEST_QDRANT_API_KEY	Require X-Qdrant-Api-Key when overrides enabled	false
MCP_REQUIRE_REQUEST_COLLECTION	Require X-Collection-Name when overrides enabled	true
MCP_DISABLE_DEFAULT_QDRANT_FALLBACK	Disable server-side Qdrant fallback defaults	false
MCP_DISABLE_DEFAULT_EMBEDDING_FALLBACK	Require embedding headers for all tools	false
MCP_PORTAL_GRANT_HEADER	Header name for the MAD MCP Portal grant token	x-madpanda-portal-grant
MCP_QDRANT_URL_HEADER	Header name for Qdrant URL	x-qdrant-url
MCP_QDRANT_API_KEY_HEADER	Header name for Qdrant API key	x-qdrant-api-key
MCP_COLLECTION_NAME_HEADER	Header name for collection name	x-collection-name
MCP_QDRANT_VECTOR_NAME_HEADER	Header name for vector name	x-qdrant-vector-name
MCP_EMBEDDING_PROVIDER_HEADER	Header name for embedding provider	x-embedding-provider
MCP_EMBEDDING_MODEL_HEADER	Header name for embedding model	x-embedding-model
MCP_EMBEDDING_VECTOR_SIZE_HEADER	Header name for embedding vector size	x-embedding-vector-size
MCP_OPENAI_API_KEY_HEADER	Header name for OpenAI API key	x-openai-api-key
MCP_OPENAI_BASE_URL_HEADER	Header name for OpenAI-compatible base URL	x-openai-base-url
MCP_OPENAI_ORG_HEADER	Header name for OpenAI organization	x-openai-org
MCP_OPENAI_PROJECT_HEADER	Header name for OpenAI project	x-openai-project
MCP_QDRANT_HOST_ALLOWLIST	Comma/space-separated allowed Qdrant hostnames	unset

Note: You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.

Example .env templates

Base (Qdrant + hosted overrides):

MCP_PORTAL_GRANT_TOKEN=replace-with-shared-portal-token
QDRANT_URL=https://your-qdrant-host:6333
QDRANT_API_KEY=your-qdrant-api-key
COLLECTION_NAME=

# Hosted MCP: require client headers (recommended for public endpoints)
MCP_ALLOW_REQUEST_OVERRIDES=true
MCP_REQUIRE_REQUEST_QDRANT_URL=true
MCP_REQUIRE_REQUEST_QDRANT_API_KEY=true
MCP_REQUIRE_REQUEST_COLLECTION=false
MCP_DISABLE_DEFAULT_QDRANT_FALLBACK=true
MCP_DISABLE_DEFAULT_EMBEDDING_FALLBACK=false
MCP_QDRANT_HOST_ALLOWLIST=*.qdrant.io
MCP_QUERY_EMBEDDING_CACHE_SIZE=256
MCP_QUERY_EMBEDDING_CACHE_TTL_SECONDS=3600

FastEmbed (local embeddings, no external API):

EMBEDDING_PROVIDER=fastembed
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

OpenAI embeddings:

EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=text-embedding-3-large
OPENAI_API_KEY=your-openai-key

OpenAI-compatible embeddings (custom base URL):

EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=your-model-id
OPENAI_API_KEY=your-openai-compatible-key
OPENAI_BASE_URL=https://your-openai-compatible-host/v1
# Optional:
OPENAI_ORG=
OPENAI_PROJECT=

Hosted MCP (Bring Your Own Qdrant)

If you run a public MCP endpoint and want users to supply their own Qdrant credentials (e.g., in n8n), enable per-request overrides and send headers.

Server env:

MCP_PORTAL_GRANT_TOKEN=replace-with-shared-portal-token
MCP_ALLOW_REQUEST_OVERRIDES=true
MCP_REQUIRE_REQUEST_QDRANT_URL=true
MCP_REQUIRE_REQUEST_QDRANT_API_KEY=true
MCP_REQUIRE_REQUEST_COLLECTION=false
MCP_DISABLE_DEFAULT_QDRANT_FALLBACK=true
MCP_DISABLE_DEFAULT_EMBEDDING_FALLBACK=false
# Optional hardening
MCP_QDRANT_HOST_ALLOWLIST=*.qdrant.io

Client headers (n8n MCP node):

• X-MADPANDA-PORTAL-GRANT: shared portal grant token (required for /mcp)
• X-Qdrant-Url: user Qdrant URL (required)
• X-Qdrant-Api-Key: user Qdrant API key (required in strict hosted mode)
• X-Collection-Name: user collection (optional; omit to work across collections)
• X-Qdrant-Vector-Name: optional vector name override
• X-Embedding-Provider: optional (fastembed or openai)
• X-Embedding-Model: optional; required when provider is set
• X-Embedding-Vector-Size: optional vector size override
• X-OpenAI-Api-Key: required when provider is openai
• X-OpenAI-Base-Url: optional OpenAI-compatible endpoint
• X-OpenAI-Org: optional OpenAI org
• X-OpenAI-Project: optional OpenAI project

Behavior in hosted mode:

• /health is public and secret-free.
• /mcp fails closed before request override, Qdrant, or embedding headers are processed when MCP_PORTAL_GRANT_TOKEN is missing or X-MADPANDA-PORTAL-GRANT is absent/invalid.
• Missing embedding headers puts the session into read-only mode.
• Write/mutation tools are blocked until embedding headers are provided.
• Semantic search tools (qdrant-find, qdrant-find-short-term) require embedding headers.
• Collection-scoped tools remain visible in tools/list and include the marker [Requires collection name].
• You can create collections without X-Collection-Name by calling qdrant-create-collection with collection_name in tool args.
• Send headers on initialize, then reuse mcp-session-id for tools/list and tools/call.

Tip: If you enable request overrides for a public endpoint, do not rely on server-side QDRANT_* defaults. Require user headers and keep your own Qdrant instance network-restricted.

Memory Contract

Stored memories are normalized to include at least: text, type, entities, source, created_at, updated_at, scope, confidence, and text_hash.

Optional fields include expires_at / ttl_days, labels, related_ids, associations, validation metadata (validation_status, validation_errors), merge markers (merged_into, merged_from), plus embedding metadata (embedding_model, embedding_dim, embedding_provider, embedding_version).

Document ingestion stores additional fields such as doc_id, doc_title, doc_hash, source_url, file_name, file_type, page_start, page_end, and section_heading.

When a duplicate text_hash is found in the same scope, the server updates last_seen_at and reinforcement_count instead of inserting a duplicate.

Maintenance Playbooks

See docs/MAINTENANCE_PLAYBOOKS.md for recommended maintenance flows.

Release & Versioning

This repo uses conventional commits and semantic-release. Every push to main runs the release workflow, and a release is created only when commit messages warrant a version bump.

License

Apache-2.0.

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