Integrations
Provides semantic search capabilities using OpenAI embeddings to convert text into vector representations for search queries
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:Copy
- Install dependencies:Copy
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
Copy
Run with MCP CLI
Copy
Installing in Claude Desktop
Copy
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:
Copy
This server cannot be installed
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.
Related MCP Servers
- AsecurityAlicenseAqualityThis repository is an example of how to create a MCP server for Qdrant, a vector search engine.Last updated -2448PythonApache 2.0
- -securityAlicense-qualityProvides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.Last updated -54TypeScriptApache 2.0
Chroma MCP Serverofficial
-securityAlicense-qualityA server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.Last updated -71PythonApache 2.0- -securityAlicense-qualityA Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.Last updated -89TypeScriptMIT License