Qdrant检索
STDIOQdrant向量数据库语义搜索服务
Qdrant向量数据库语义搜索服务
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