広告掲載
MCP.so
ログイン

Qdrant MCP Server

@hadv

リポジトリ

基本情報

カテゴリ

データベース

ライセンス

MIT license

トランスポート

stdio

作者

hadv

Qdrant MCP Server について

A Model Context Protocol (MCP) server implementation for RAG

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

Qdrant MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Qdrant MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

What vector databases does Qdrant MCP Server support?

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

2

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.

3

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.

4

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).

5

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.

コメント

「データベース」の他のコンテンツ

Xata MCP Server

The Xata MCP server lets AI assistants and agents interact with your Xata organizations, projects, and Postgres database branches.

PostgreSQL

modelcontextprotocol

Model Context Protocol Servers

88.1K

Postgres Mcp

crystaldba

Postgres MCP Pro provides configurable read/write access and performance analysis for you and your AI agents.

3.0K

Schemity

tbson

Local database MCP server: your agent sees a migration's impact before it runs, for PostgreSQL, MySQL, SQL Server and SQLite, with no database credentials.

Database Gateway

centralmind

Universal MCP-Server for your Databases optimized for LLMs and AI-Agents.

531
広告掲載