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

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

This server cannot be installed

security - not tested

license - not found

quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.

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