Qdrant Retrieve MCP Server
MCP server for semantic search with Qdrant vector database.
Features
- Semantic search across multiple collections
- Multi-query support
- Configurable result count
- Collection source tracking
Note: The server connects to a Qdrant instance specified by URL.
Note 2: The first retrieve might be slower, as the MCP server downloads the required embedding model.
API
Tools
- qdrant_retrieve
- Retrieves semantically similar documents from multiple Qdrant vector store collections based on multiple queries
- Inputs:
collectionNames(string[]): Names of the Qdrant collections to search acrosstopK(number): Number of top similar documents to retrieve (default: 3)query(string[]): Array of query texts to search for
- Returns:
results: Array of retrieved documents with:query: The query that produced this resultcollectionName: Collection name that this result came fromtext: Document text contentscore: Similarity score between 0 and 1
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"qdrant": {
"command": "npx",
"args": ["-y", "@gergelyszerovay/mcp-server-qdrant-retrive"],
"env": {
"QDRANT_API_KEY": "your_api_key_here"
}
}
}
}Command Line Options
MCP server for semantic search with Qdrant vector database.
Options
--enableHttpTransport Enable HTTP transport [default: false]
--enableStdioTransport Enable stdio transport [default: true]
--enableRestServer Enable REST API server [default: false]
--mcpHttpPort=<port> Port for MCP HTTP server [default: 3001]
--restHttpPort=<port> Port for REST HTTP server [default: 3002]
--qdrantUrl=<url> URL for Qdrant vector database [default: http://localhost:6333]
--embeddingModelType=<type> Type of embedding model to use [default: Xenova/all-MiniLM-L6-v2]
--help Show this help message
Environment Variables
QDRANT_API_KEY API key for authenticated Qdrant instances (optional)
Examples
$ mcp-qdrant --enableHttpTransport
$ mcp-qdrant --mcpHttpPort=3005 --restHttpPort=3006
$ mcp-qdrant --qdrantUrl=http://qdrant.example.com:6333
$ mcp-qdrant --embeddingModelType=Xenova/all-MiniLM-L6-v2
