better-qdrant-mcp

An MCP server implemented with fastmcp, OpenAI embeddings, and qdrant-client, providing long-term memory and semantic search on top of Qdrant.

User Guide

Features

• MCP server built with fastmcp

• Hybrid search in Qdrant (dense OpenAI embeddings + sparse BM25)

• Chinese support via jieba

• Knowledge Base tools (renamed from memory tools to avoid conflict):

  • store-knowledge

  • store-knowledge-bulk

  • search-knowledge

  • get-knowledge-by-id

  • inspect-knowledge-base

  • delete-knowledge

• Multiple transports: stdio, SSE, streamable HTTP

Requirements

• Python 3.12+

• Qdrant reachable via HTTP

Quick Start (published package)

The project is published as the better-qdrant-mcp package, so you can run it directly with uvx without cloning this repo.

1. Environment variables (required for all transports)

Minimal env for typical use:

• QDRANT_URL – defaults to http://localhost:6333

• QDRANT_API_KEY – optional

• COLLECTION_NAME – optional default collection

• OPENAI_API_KEY (or OPENAPI_API_KEY) – required

• OPENAI_BASE_URL – optional

• OPENAI_EMBEDDING_MODEL – defaults to text-embedding-3-small

Advanced / transport-related env:

• MCP_TRANSPORT – stdio | sse | streamable-http (default: stdio)

• MCP_HOST – host for HTTP-based transports (default: 0.0.0.0)

• MCP_PORT – port for HTTP-based transports (default: 8000)

• MCP_PATH – path for HTTP transports (default: /mcp)

2. Available MCP tools

Once the server is running, the MCP client will see these tools:

• store-knowledge(content: str, title?: str, tags?: list[str], metadata?: dict, collection_name?: str) -> str

• store-knowledge-bulk(items: list[KnowledgeItem], collection_name?: str) -> str

• search-knowledge(query: str, limit?: int=5, collection_name?: str) -> str

• get-knowledge-by-id(ids: list[str] | str, collection_name?: str) -> str

• inspect-knowledge-base(collection_name?: str) -> str

• delete-knowledge(ids: list[str] | str, collection_name?: str) -> str

store-knowledge automatically embeds the text using OpenAI and stores it in Qdrant (Knowledge Base), returning the stored point ID. The title and tags fields help improve search context and categorization.

store-knowledge-bulk efficiently stores multiple knowledge items at once using batch embedding. Each item in the list should include content (required), and optionally title, tags, and metadata fields. This is more efficient than calling store-knowledge multiple times.

search-knowledge uses hybrid search in Qdrant (dense + sparse). If the collection is configured with named vectors dense and sparse, queries are ranked by fusing dense OpenAI embeddings and sparse BM25 scores; otherwise it falls back to dense-only search.

get-knowledge-by-id retrieves the complete payload information for one or more knowledge items by their point IDs. Use this to inspect the full details of stored items (including content, title, tags, metadata, and stored_at timestamp). You can pass a single ID or a list of IDs (typically using the id field returned by search-knowledge).

inspect-knowledge-base shows the collection configuration and sample data points, useful for debugging and verification.

delete-knowledge deletes one or more stored knowledge items from Qdrant by their point IDs. You can pass a single ID or a list of IDs (typically using the id field returned by search-knowledge).

3. Start the server

You can either specify the transport via CLI flags (recommended for quick start) or via env (MCP_TRANSPORT).

Standard IO (stdio) – default

uvx better-qdrant-mcp

In this mode, you configure your MCP client to use stdio transport and just invoke the binary; no HTTP URL is needed.

Server-Sent Events (SSE)

# Default host 0.0.0.0 and port 8000
uvx better-qdrant-mcp --transport sse

# Custom host and port
uvx better-qdrant-mcp --transport sse --host 0.0.0.0 --port 3000

Connection details for MCP clients:

• Transport: sse

• URL: http://<host>:<port>/sse (for example: http://localhost:8000/sse)

Streamable HTTP (recommended for web applications)

# Default host 0.0.0.0, port 8000 and path /mcp
uvx better-qdrant-mcp --transport streamable-http

# Custom host, port, and path
uvx better-qdrant-mcp --transport streamable-http --host 0.0.0.0 --port 3000 --path /api/mcp

Connection details for MCP clients:

• Transport: streamable-http

• URL: http://<host>:<port><path> (for example: http://localhost:8000/mcp)

Development Guide

Local installation (for development)

If you want to work on this repo locally instead of using the published package:

# using uv (recommended)
uv sync

# or with pip (editable install)
pip install -e .

Local build

For local development, you can use the provided Makefile:

make build

This command will first clean the dist directory and then run uv build to produce fresh artifacts.

Docker Deployment

Docker Compose provides Qdrant + this MCP server as a single service. Transport (stdio, sse, streamable-http) is selected via MCP_TRANSPORT.

The Docker image is built and published automatically to GitHub Container Registry as:

• ghcr.io/jtsang4/better-qdrant-mcp:latest

• Additional tags for branches, tags, and commit SHAs

The provided docker-compose.yml uses this published image directly, so you do not need to build the image locally.

# Start Qdrant + MCP using the published image
docker compose up -d

# Pull the latest published image and restart services
docker compose pull mcp && docker compose up -d

# Stop services
docker compose down

License

MIT