qdrant-mcp-server

# Qdrant MCP Server An MCP server for interacting with [Qdrant](https://qdrant.tech/) vector database. This server provides tools for managing vectors, performing similarity searches, and automatic text-to-vector embedding using the MCP (Master Control Program) framework. ## Features - Automatic text-to-vector embedding using FastEmbed - Store and retrieve text content with vector search - Use default collection configuration through environment variables - Text similarity search by content - Efficient embedding with optimized models ## Configuration Create a `.env` file based on the `.env.example` template: ``` # Qdrant connection settings QDRANT_HOST=localhost QDRANT_PORT=6333 QDRANT_API_KEY= QDRANT_VERIFY_SSL=True # Set to False if using self-signed certificates # Default settings DEFAULT_COLLECTION_NAME=default_collection EMBEDDING_MODEL=BAAI/bge-small-en-v1.5 ``` You can change the embedding model to any model supported by [FastEmbed](https://github.com/qdrant/fastembed). ## Usage ### Running locally 1. Install the package: ``` pip install -e . ``` 2. Run the server: ``` qdrant-mcp-server ``` ### Running with Docker 1. Build the Docker image: ``` docker build -t qdrant-mcp-server . ``` 2. Run the container: ``` docker run -p 8000:8000 --env QDRANT_HOST=<your-qdrant-host> --env QDRANT_PORT=<your-qdrant-port> --env QDRANT_VERIFY_SSL=<True|False> qdrant-mcp-server ``` ## Testing This package includes a test suite to validate the functionality. To run the tests: 1. Install development dependencies: ``` pip install -e ".[dev]" ``` 2. Run the tests: ``` cd tests ./run_tests.py ``` Alternatively, you can use pytest directly: ``` pytest -xvs tests/ ``` ### Using Self-Signed Certificates If your Qdrant server uses a self-signed certificate, set `QDRANT_VERIFY_SSL=False` in your `.env` file or when running the Docker container. This disables SSL certificate verification. ## Tools The server provides the following tools: ### Text Tools - `store_text`: Convert text to an embedding vector and store it in the database - `search_similar_text`: Convert query text to an embedding and find similar vectors - `store_texts`: Convert multiple texts to embeddings and store them in batch ### Vector Tools - `search_vectors`: Search for similar vectors in a collection - `upsert_vectors`: Upload vectors to a collection - `filter_search`: Search collection with metadata filters ### Point Tools - `get_points`: Get points by their IDs from a collection - `delete_points`: Delete points by their IDs from a collection - `count_points`: Count the number of points in a collection ## Examples ### Storing text ``` await store_text( text="What is the capital of France?", metadata={"category": "geography", "type": "question"} ) ``` ### Searching for similar text ``` await search_similar_text( query="What is Paris the capital of?", limit=5 ) ``` ### Storing multiple texts ``` await store_texts( texts=["Paris is in France", "London is in England", "Berlin is in Germany"], metadatas=[ {"category": "geography", "country": "France"}, {"category": "geography", "country": "England"}, {"category": "geography", "country": "Germany"} ] )