Qdrant Semantic Search
STDIOMCP server for semantic search with Qdrant vector database.
MCP server for semantic search with Qdrant vector database.
MCP server for semantic search with Qdrant vector database.
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.
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 forresults
: 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 1Add 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" } } } }
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