Qdrant MCP Server

Name: Qdrant MCP Server
Author: hadv

@hadv

代码仓库

关于 Qdrant MCP Server

A Model Context Protocol (MCP) server implementation for RAG

#mcp #rag #qdrant #qdrant-vector-database #mcp-server #modelcontextprotocol

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

hadv

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "vito-mcp": {
      "command": "node",
      "args": [
        "dist/index.js"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Qdrant MCP Server?

A server implementation that supports both Qdrant and Chroma vector databases for storing and retrieving domain knowledge. It uses Qdrant's built-in FastEmbed for efficient embedding generation without external API keys, and is designed for integration with AI IDEs like Cursor and Claude Desktop.

How to use Qdrant MCP Server?

Clone the repository, install dependencies, create a .env file with your database type, URL, API key, and collection name, then build and start the server with npm start. Use the API endpoints /api/store and /api/query for domain knowledge, or run npm run store-doc <file-path> to store documentation files.

Key features of Qdrant MCP Server

Supports both Qdrant and Chroma vector databases
Uses Qdrant's built-in FastEmbed for embedding generation
Domain knowledge storage and retrieval via API
Documentation file storage with metadata (PDF and TXT)
Configurable database selection via environment variables
Cursor and Claude Desktop integration support

Use cases of Qdrant MCP Server

Store domain-specific knowledge in a vector database for AI IDE contexts
Retrieve relevant knowledge context from past conversations or documentation
Ingest PDF and TXT documentation files with file metadata into the database
Integrate knowledge retrieval directly into Cursor or Claude Desktop workflows

FAQ from Qdrant MCP Server

What vector databases does Qdrant MCP Server support?

It supports both Qdrant and Chroma vector databases, selectable via the DATABASE_TYPE environment variable.

What are the prerequisites for using Qdrant MCP Server?

Node.js 20.x or later, npm 10.x or later, and a running Qdrant or Chroma vector database instance.

How does embedding generation work in Qdrant MCP Server?

It uses Qdrant's built-in FastEmbed, which employs quantized model weights and ONNX Runtime for inference, eliminating the need for external embedding API keys.

What file formats are supported for documentation storage?

PDF and TXT files are supported, with automatic content extraction and metadata storage (source, file name, extension, file size, last modified date, creation date, and content type).

How do I configure Qdrant MCP Server for a remote Qdrant instance?

Set the QDRANT_URL with your instance's full URL including port (e.g., https://your-instance-id.region.gcp.cloud.qdrant.io:6333) and provide your QDRANT_API_KEY in the .env file.

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