Skip to main content

MCP server for retrieving context from a Qdrant vector database

Project description

mcp-server-qdrant: A Qdrant MCP server

smithery badge

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

mcp-server-qdrant MCP server

Overview

A basic Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database.

Components

Tools

  1. qdrant-store
    • Store some information in the Qdrant database
    • Input:
      • information (string): Information to store
      • metadata (JSON): Optional metadata to store
    • Returns: Confirmation message
  2. qdrant-find
    • Retrieve relevant information from the Qdrant database
    • Input:
      • query (string): Query to use for searching
    • Returns: Information stored in the Qdrant database as separate messages

Installation in Claude Desktop

Using mcp (recommended)

When using mcp no specific installation is needed to directly run mcp-server-qdrant.

mcp install src/mcp_server_qdrant/server.py \
  -v QDRANT_URL="http://localhost:6333" \
  -v QDRANT_API_KEY="your_api_key" \
  -v COLLECTION_NAME="my_collection" \
  -v EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"

Installing via Smithery

To install Qdrant MCP Server for Claude Desktop automatically via Smithery:

npx @smithery/cli install mcp-server-qdrant --client claude

Manual configuration

To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_URL": "http://localhost:6333",
      "QDRANT_API_KEY": "your_api_key",
      "COLLECTION_NAME": "your_collection_name",
      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
    }
  }
}

For local Qdrant mode:

{
  "qdrant": {
    "command": "uvx",
    "args": ["mcp-server-qdrant"],
    "env": {
      "QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
      "COLLECTION_NAME": "your_collection_name",
      "EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
    }
  }
}

This MCP server will automatically create a collection with the specified name if it doesn't exist.

By default, the server will use the sentence-transformers/all-MiniLM-L6-v2 embedding model to encode memories. For the time being, only FastEmbed models are supported.

Support for other tools

This MCP server can be used with any MCP-compatible client. For example, you can use it with Cursor, which provides built-in support for the Model Context Protocol.

Environment Variables

The configuration of the server is done using environment variables:

  • QDRANT_URL: URL of the Qdrant server, e.g. http://localhost:6333
  • QDRANT_API_KEY: API key for the Qdrant server (optional, depends on Qdrant server configuration)
  • COLLECTION_NAME: Name of the collection to use (required)
  • EMBEDDING_MODEL: Name of the embedding model to use (default: sentence-transformers/all-MiniLM-L6-v2)
  • EMBEDDING_PROVIDER: Embedding provider to use (currently only "fastembed" is supported)
  • QDRANT_LOCAL_PATH: Path to the local Qdrant database (alternative to QDRANT_URL)

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

[!IMPORTANT] Command-line arguments are not supported anymore! Please use environment variables for all configuration.

Contributing

If you have suggestions for how mcp-server-qdrant could be improved, or want to report a bug, open an issue! We'd love all and any contributions.

Testing mcp-server-qdrant locally

The MCP inspector is a developer tool for testing and debugging MCP servers. It runs both a client UI (default port 5173) and an MCP proxy server (default port 3000). Open the client UI in your browser to use the inspector.

QDRANT_URL=":memory:" COLLECTION_NAME="test" \
mcp dev src/mcp_server_qdrant/server.py

Once started, open your browser to http://localhost:5173 to access the inspector interface.

License

This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the Apache License 2.0. For more details, please see the LICENSE file in the project repository.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mcp_server_qdrant-0.7.0.tar.gz (81.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mcp_server_qdrant-0.7.0-py3-none-any.whl (14.0 kB view details)

Uploaded Python 3

File details

Details for the file mcp_server_qdrant-0.7.0.tar.gz.

File metadata

  • Download URL: mcp_server_qdrant-0.7.0.tar.gz
  • Upload date:
  • Size: 81.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.5

File hashes

Hashes for mcp_server_qdrant-0.7.0.tar.gz
Algorithm Hash digest
SHA256 a40be1c7ec21238430d566a82784ac04db9512d0cccb9441aee876fddc75c36a
MD5 9f21bb873046a62f51d61bbf2cc31c24
BLAKE2b-256 97956e6f17a2d7f1dc361fb3d1cfb30c90eb416689ec4463c0e0c42f7295953a

See more details on using hashes here.

File details

Details for the file mcp_server_qdrant-0.7.0-py3-none-any.whl.

File metadata

File hashes

Hashes for mcp_server_qdrant-0.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 12f419c9e7ee577e689c35568ec077efe1ee767818f243f44fbb2d09fcecd309
MD5 241f6ba12e096ceb6bfdebae2447bb4c
BLAKE2b-256 7c317e61d297ba2ce00e51aa161f13b3b569fe9e728161315b8f03335bf70409

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page