mcp-server-qdrant

An official Model Context Protocol (MCP) server implementation for Qdrant, enabling MCP protocol support for Qdrant vector databases. It acts as a semantic memory layer on top of the Qdrant database, facilitating integration between LLM applications and external data sources.

Source: https://github.com/qdrant/mcp-server-qdrant

Features

• Semantic Memory Layer: Provides a standardized way to store and retrieve information in Qdrant as semantic memories, enabling LLM applications to connect with contextual data.
• Tools:
  • qdrant-store: Store information and optional metadata into a specified Qdrant collection.
  • qdrant-find: Retrieve relevant information from the Qdrant database using semantic queries.
• Flexible Configuration:
  • Environment variables for configuration (QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME, QDRANT_LOCAL_PATH, EMBEDDING_PROVIDER, EMBEDDING_MODEL, etc.).
  • Supports both remote Qdrant servers and local Qdrant database paths.
  • Automatic collection creation if it does not exist.
• Embedding Models:
  • Uses FastEmbed models (default: sentence-transformers/all-MiniLM-L6-v2) for encoding memories.
• Transport Protocols:
  • Supports stdio (default, for local clients) and sse (Server-Sent Events, recommended for remote clients and team sharing).
• Containerization:
  • Includes a Dockerfile for easy containerized deployment.
• Integration:
  • Compatible with MCP clients such as Claude Desktop, Cursor, Windsurf, and VS Code.
  • Provides example configurations for integration with these tools.
• Development & Testing:
  • Includes a development mode with MCP inspector for local testing and debugging.
• Customizable Tool Descriptions:
  • Tool descriptions can be customized for specialized use cases (e.g., semantic code search).
• Open Source:
  • Licensed under Apache 2.0.

Installation

• Directly with uvx:
  • Run the server using environment variables (no special install required).
• Docker:
  • Build and run with provided Dockerfile.
• Smithery:
  • Install via Smithery CLI for Claude Desktop integration.
• Manual Configuration:
  • Example JSON configs provided for Claude Desktop and VS Code.

Use Cases

• Semantic memory for LLM-powered applications.
• Code snippet storage and retrieval for development environments.
• Integration with IDEs (VS Code, Cursor, Windsurf) for enhanced AI-assisted workflows.

License

• Apache License 2.0

Pricing

• No pricing information provided; the project is open source under Apache 2.0 license.