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
Máy chủ liên quan
MCP Knowledge Base
A knowledge base server that processes local documents (PDF, DOCX, TXT, HTML) and answers questions based on their content using similarity search.
Web Search
A server that provides web search capabilities using OpenAI models.
mcp-seo-audit
SEO audit and Google Search Console MCP server with 23 tools. Search analytics, URL inspection, Indexing API, Core Web Vitals (CrUX), striking distance keywords, keyword cannibalization detection, branded query analysis, and automated site audits.
Rakuten Travel
Search for hotels and check their availability using the Rakuten Travel API.
EzBiz SEO & Marketing Analysis
AI-powered keyword research, SERP analysis, backlink checking, and content optimization for SEO.
mxHERO Multi-Account Email Search
Search across multiple email accounts using mxHERO's vector search service.
Perplexity AI
Intelligent search, reasoning, and research capabilities powered by Perplexity's specialized AI models.
NPMLens MCP
NPMLens MCP lets your coding agent (such as Claude, Cursor, Copilot, Gemini or Codex) search the npm registry and fetch package context (README, downloads, GitHub info, usage snippets). It acts as a Model‑Context‑Protocol (MCP) server, giving your AI assistant a structured way to discover libraries and integrate them quickly.
Library Docs MCP Server
Search and fetch documentation for popular libraries like Langchain, Llama-Index, and OpenAI using the Serper API, providing updated information for LLMs.
MCP Naver News
Search for news articles using the Naver News API. Requires Naver News API credentials.