Qdrant Retrieve

Qdrant Retrieve

gergelyszerovay

Performs semantic search across document collections stored in Qdrant vector database using natural language queries.

Enables semantic search across multiple document collections using Qdrant vector database integration, allowing natural language queries with configurable result counts and collection tracking.

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What it does

  • Search documents using natural language queries
  • Retrieve results from multiple Qdrant collections
  • Configure number of search results returned
  • Track and manage different document collections

Best for

AI applications needing document retrievalBuilding semantic search featuresRAG (Retrieval Augmented Generation) systems
Natural language search queriesMulti-collection support

About Qdrant Retrieve

Qdrant Retrieve is a community-built MCP server published by gergelyszerovay that provides AI assistants with tools and capabilities via the Model Context Protocol. Perform semantic search across collections with Qdrant Retrieve, powered by vector database integration and natural lang It is categorized under databases, ai ml.

How to install

You can install Qdrant Retrieve in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

License

Qdrant Retrieve is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

Qdrant Retrieve MCP Server

MCP server for semantic search with Qdrant vector database.

Features

  • Semantic search across multiple collections
  • Multi-query support
  • Configurable result count
  • Collection source tracking

Note: The server connects to a Qdrant instance specified by URL.

Note 2: The first retrieve might be slower, as the MCP server downloads the required embedding model.

API

Tools

  • qdrant_retrieve
    • Retrieves semantically similar documents from multiple Qdrant vector store collections based on multiple queries
    • Inputs:
      • collectionNames (string[]): Names of the Qdrant collections to search across
      • topK (number): Number of top similar documents to retrieve (default: 3)
      • query (string[]): Array of query texts to search for
    • Returns:
      • results: Array of retrieved documents with:
        • query: The query that produced this result
        • collectionName: Collection name that this result came from
        • text: Document text content
        • score: Similarity score between 0 and 1

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "qdrant": {
      "command": "npx",
      "args": ["-y", "@gergelyszerovay/mcp-server-qdrant-retrive"],
      "env": {
        "QDRANT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Command Line Options

MCP server for semantic search with Qdrant vector database.

Options
  --enableHttpTransport      Enable HTTP transport [default: false]
  --enableStdioTransport     Enable stdio transport [default: true]
  --enableRestServer         Enable REST API server [default: false]
  --mcpHttpPort=<port>       Port for MCP HTTP server [default: 3001]
  --restHttpPort=<port>      Port for REST HTTP server [default: 3002]
  --qdrantUrl=<url>          URL for Qdrant vector database [default: http://localhost:6333]
  --embeddingModelType=<type> Type of embedding model to use [default: Xenova/all-MiniLM-L6-v2]
  --help                     Show this help message

Environment Variables
  QDRANT_API_KEY            API key for authenticated Qdrant instances (optional)

Examples
  $ mcp-qdrant --enableHttpTransport
  $ mcp-qdrant --mcpHttpPort=3005 --restHttpPort=3006
  $ mcp-qdrant --qdrantUrl=http://qdrant.example.com:6333
  $ mcp-qdrant --embeddingModelType=Xenova/all-MiniLM-L6-v2

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