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
Installing via Smithery
To install Qdrant Vector Search Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @amansingh0311/mcp-qdrant-openai --client claude
Manual 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 keyQDRANT_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 searchquery_text: The search query in natural languagelimit: 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.