Qdrant Retrieve MCP Server

Qdrant Retrieve MCP Server

By gergelyszerovay GitHub

MCP server for semantic search with Qdrant vector database

qdrant semantic-search
Overview

what is Qdrant Retrieve MCP Server?

Qdrant Retrieve MCP Server is a server designed for semantic search using the Qdrant vector database, enabling efficient retrieval of semantically similar documents across multiple collections.

how to use Qdrant Retrieve MCP Server?

To use the server, configure it with the Qdrant API key and specify the Qdrant instance URL. You can then send queries to retrieve similar documents based on your input.

key features of Qdrant Retrieve MCP Server?

  • Semantic search across multiple collections
  • Multi-query support for enhanced search capabilities
  • Configurable result count to tailor the output
  • Collection source tracking for better context

use cases of Qdrant Retrieve MCP Server?

  1. Retrieving relevant documents for research purposes
  2. Enhancing search functionalities in applications
  3. Analyzing large datasets for semantic similarities

FAQ from Qdrant Retrieve MCP Server?

  • What is the default port for the MCP HTTP server?

The default port for the MCP HTTP server is 3001.

  • Can I enable HTTP transport?

Yes, you can enable HTTP transport by using the --enableHttpTransport option.

  • Is there a REST API available?

Yes, you can enable the REST API server by using the --enableRestServer option.

Content

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
No tools information available.
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