Qdrant Retrieve MCP Server

Name: Qdrant Retrieve MCP Server
Author: gergelyszerovay

@gergelyszerovay

代码仓库

关于 Qdrant Retrieve MCP Server

MCP server for semantic search with Qdrant vector database

#qdrant #semantic-search #mcp-server

基本信息

分类

数据库

许可证

MIT license

运行时

node

传输方式

stdio

发布者

gergelyszerovay

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "qdrant": {
      "command": "npx",
      "args": [
        "-y",
        "@gergelyszerovay/mcp-server-qdrant-retrive"
      ],
      "env": {
        "QDRANT_API_KEY": "your_api_key_here"
      }
    }
  }
}

工具

Retrieves semantically similar documents from multiple Qdrant vector store collections based on multiple queries

概览

What is Qdrant Retrieve MCP Server?

An MCP server that enables semantic search across multiple Qdrant vector database collections. It supports multi-query input, configurable result counts, and collection source tracking, connecting to any Qdrant instance specified by URL.

How to use Qdrant Retrieve MCP Server?

Configure it in Claude Desktop by adding an MCP server entry with the command npx -y @gergelyszerovay/mcp-server-qdrant-retrive and optionally set the QDRANT_API_KEY environment variable. Alternatively, run it from the command line using flags like --qdrantUrl, --enableHttpTransport, or --embeddingModelType.

Key features of Qdrant Retrieve MCP Server

Semantic search across multiple Qdrant collections
Multi-query support (array of query texts)
Configurable result count (topK)
Collection source tracking in results
Stdio and HTTP transport options
Optional REST API server

Use cases of Qdrant Retrieve MCP Server

Retrieve semantically similar documents from multiple Qdrant collections in one request
Integrate semantic search into AI assistants via the MCP protocol
Power applications that need multi-query document retrieval with similarity scores
Combine vector search with LLM tools for knowledge retrieval

FAQ from Qdrant Retrieve MCP Server

What embedding model does the server use?

The default embedding model is Xenova/all-MiniLM-L6-v2. It can be customized via the --embeddingModelType command-line option.

How do I connect to my Qdrant instance?

Set the Qdrant URL using the --qdrantUrl option (default: http://localhost:6333). If authentication is required, provide an API key via the QDRANT_API_KEY environment variable.

Why is the first retrieval slow?

The first retrieve may be slower because the server downloads the required embedding model on initial use.

Can the server run over HTTP instead of stdio?

Yes, enable HTTP transport with the --enableHttpTransport flag. The MCP HTTP server port defaults to 3001.

What transport methods are available?

The server supports stdio transport (enabled by default), HTTP transport (optional), and a REST API server (optional, default port 3002).

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