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Qdrant Retrieve MCP Server: High-Performance, Scalable Semantic Search - MCP Implementation

Qdrant Retrieve MCP Server: High-Performance, Scalable Semantic Search

Qdrant Retrieve MCP Server: High-performance semantic search powered by Qdrant's vector DB, delivering scalable, accurate insights for enterprise applications.

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About Qdrant Retrieve MCP Server

What is Qdrant Retrieve MCP Server: High-Performance, Scalable Semantic Search?

Qdrant Retrieve MCP Server is a specialized tool for performing efficient semantic search across multiple collections stored in a Qdrant vector database. It enables users to query text data using natural language, returning the most relevant documents based on semantic similarity. The server is designed for high performance and scalability, making it suitable for applications requiring real-time or near-real-time search capabilities.

How to Use Qdrant Retrieve MCP Server: High-Performance, Scalable Semantic Search?

To deploy the server, configure your claude_desktop_config.json with the provided template, specifying the server's entry point and parameters. Command-line options allow customization of behaviors like connection settings and output formatting. For API usage, call the qdrant_retrieve tool with inputs such as search queries and filtering criteria. Ensure the Qdrant database instance is accessible via the specified URL and credentials.

Qdrant Retrieve MCP Server Features

Key Features of Qdrant Retrieve MCP Server

Core features include support for multi-collection searches, handling multiple queries in a single request, customizable result limits, and automatic tracking of document sources. The server also manages initial latency caused by loading machine learning models, optimizing subsequent query performance. Users can adjust settings like API endpoints and authentication methods through environment variables for security and flexibility.

Common Use Cases

Qdrant Retrieve MCP Server FAQ

Frequently Asked Questions

Why is the first query slower? The server loads necessary models on the first request, which adds initial overhead but improves subsequent speed. How do I change the Qdrant instance? Update the QDRANT_URL environment variable or command-line parameter. Can I use custom models? The server uses default embeddings, but advanced users can extend functionality via plugin systems. What's the maximum query length? Limited by the underlying Qdrant database's configuration and available memory resources.

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

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