iflow-mcp_amansingh0311-mcp-qdrant-openai

[](https://mseep.ai/app/amansingh0311-mcp-qdrant-openai) # MCP Qdrant Server with OpenAI Embeddings [](https://smithery.ai/server/@amansingh0311/mcp-qdrant-openai) 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](https://smithery.ai/server/@amansingh0311/mcp-qdrant-openai): ```bash npx -y @smithery/cli install @amansingh0311/mcp-qdrant-openai --client claude ``` ### Manual Installation 1. Clone this repository: ```bash git clone https://github.com/yourusername/mcp-qdrant-openai.git cd mcp-qdrant-openai ``` 2. Install dependencies: ```bash 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 ```bash python mcp_qdrant_server.py ``` ### Run with MCP CLI ```bash mcp dev mcp_qdrant_server.py ``` ### Installing in Claude Desktop ```bash 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`: Open…