Qdrant Retrieve
Semantic search using the Qdrant vector database.
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
Servidores relacionados
Perplexity
Web search using the Perplexity API with automatic model selection based on query intent.
VelociRAG
Lightning-fast RAG for AI agents. 4-layer fusion (vector, BM25, graph, metadata), ONNX Runtime, sub-200ms search, no PyTorch.
microCMS
A search server for the microCMS headless CMS, compatible with the Model Context Protocol (MCP).
OpenStreetMap
Enhances LLMs with location-based services and geospatial data from OpenStreetMap.
Google PSE/CSE
A Model Context Protocol (MCP) server providing access to Google Programmable Search Engine (PSE) and Custom Search Engine (CSE).
Bilibili API
Search for videos, users, and retrieve danmaku from the Bilibili API.
Gemini DeepSearch MCP
An automated research agent using Google Gemini models and Google Search to perform deep, multi-step web research.
Context7 HTTP
An MCP server for the Context7 project, providing HTTP streaming and search endpoints for library information without local installation.
Unsplash MCP Server
Search and integrate images from Unsplash using its official API.
Discourse MCP Server
Perform search operations on Discourse forums.