Qdrant

GitHub
Integrates with
Qdrant, OpenAI

MCP Qdrant Server with OpenAI Embeddings

This MCP server provides vector search capabilities using Qdrant vector database and OpenAI embeddings.

Features

  • Semantic search in Qdrant collections using OpenAI embeddings
  • List available collections
  • View collection information

Prerequisites

  • Python 3.10+ installed
  • Qdrant instance (local or remote)
  • OpenAI API key

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/mcp-qdrant-openai.git
cd mcp-qdrant-openai

  2. Install dependencies:

    pip install -r requirements.txt

Configuration

Set the following environment variables:

  • OPENAI_API_KEY: Your OpenAI API key
  • QDRANT_URL: URL to your Qdrant instance (default: "http://localhost:6333")
  • QDRANT_API_KEY: Your Qdrant API key (if applicable)

Usage

Run the server directly

python mcp_qdrant_server.py

Run with MCP CLI

mcp dev mcp_qdrant_server.py

Installing in Claude Desktop

mcp install mcp_qdrant_server.py --name "Qdrant-OpenAI"

Available Tools

query_collection

Search a Qdrant collection using semantic search with OpenAI embeddings.

  • collection_name: Name of the Qdrant collection to search
  • query_text: The search query in natural language
  • limit: Maximum number of results to return (default: 5)
  • model: OpenAI embedding model to use (default: text-embedding-3-small)

list_collections

List all available collections in the Qdrant database.

collection_info

Get information about a specific collection.

  • collection_name: Name of the collection to get information about

Example Usage in Claude Desktop

Once installed in Claude Desktop, you can use the tools like this:

What collections are available in my Qdrant database?

Search for documents about climate change in my "documents" collection.

Show me information about the "articles" collection.