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Better Qdrant MCP Server

Better Qdrant MCP Server

A Model Context Protocol (MCP) server for enhanced Qdrant vector database functionality. This server provides tools for managing Qdrant collections, adding documents, and performing semantic searches.

Features

  • List Collections: View all available Qdrant collections
  • Add Documents: Process and add documents to a Qdrant collection with various embedding services
  • Search: Perform semantic searches across your vector database
  • Delete Collection: Remove collections from your Qdrant database

Installation

npm install -g better-qdrant-mcp-server

Or use it directly with npx:

npx better-qdrant-mcp-server

Configuration

The server uses environment variables for configuration. You can set these in a .env file in your project root:

# Qdrant Configuration QDRANT_URL=http://localhost:6333 QDRANT_API_KEY=your_api_key_if_needed # Embedding Service API Keys OPENAI_API_KEY=your_openai_api_key OPENROUTER_API_KEY=your_openrouter_api_key OLLAMA_ENDPOINT=http://localhost:11434

Supported Embedding Services

  • OpenAI: Requires an API key
  • OpenRouter: Requires an API key
  • Ollama: Local embedding models (default endpoint: http://localhost:11434)
  • FastEmbed: Local embedding models

Usage with Claude

To use this MCP server with Claude, add it to your MCP settings configuration file:

{ "mcpServers": { "better-qdrant": { "command": "npx", "args": ["better-qdrant-mcp-server"], "env": { "QDRANT_URL": "http://localhost:6333", "QDRANT_API_KEY": "your_api_key_if_needed", "DEFAULT_EMBEDDING_SERVICE": "ollama", "OPENAI_API_KEY": "your_openai_api_key", "OPENAI_ENDPOINT": "https://api.openai.com/v1", "OPENROUTER_API_KEY": "your_openrouter_api_key", "OPENROUTER_ENDPOINT": "https://api.openrouter.com/v1", "OLLAMA_ENDPOINT": "http://localhost:11434", "OLLAMA_MODEL": "nomic-embed-text" } } } }

Example Commands

List Collections
use_mcp_tool server_name: better-qdrant tool_name: list_collections arguments: {}
Add Documents
use_mcp_tool server_name: better-qdrant tool_name: add_documents arguments: { "filePath": "/path/to/your/document.pdf", "collection": "my-collection", "embeddingService": "openai", "chunkSize": 1000, "chunkOverlap": 200 }
use_mcp_tool server_name: better-qdrant tool_name: search arguments: { "query": "your search query", "collection": "my-collection", "embeddingService": "openai", "limit": 5 }
Delete Collection
use_mcp_tool server_name: better-qdrant tool_name: delete_collection arguments: { "collection": "my-collection" }

Requirements

  • Node.js >= 18.0.0
  • A running Qdrant server (local or remote)
  • API keys for the embedding services you want to use

License

MIT

Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Qdrant ベクター データベース コレクションを管理し、さまざまな埋め込みサービスを使用してドキュメントを処理および埋め込み、ベクター埋め込み全体でセマンティック検索を実行するツールを提供することで、セマンティック検索機能を有効にするモデル コンテキスト プロトコル サーバー。

  1. 特徴
    1. インストール
      1. 構成
        1. サポートされている埋め込みサービス
          1. クロードとの使用
            1. コマンド例
          2. 要件
            1. ライセンス

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