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Toolbox - MCP Server Manager

@AlexanderOllman

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AlexanderOllman

Toolbox - MCP Server Manager について

Agnostic MCP server addition for FastAgent

Toolbox - MCP Server Manager 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": {
    "Toolbox": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Toolbox - MCP Server Manager 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 Toolbox - MCP Server Manager?

A web application for managing MCP servers and generating configuration files, featuring a FastAPI backend and React frontend. It automatically extracts repository information using GPT, supports YAML and JSON config generation, and provides vector-based search via Qdrant.

How to use Toolbox - MCP Server Manager?

Clone the repository, set up the backend (Python 3.8+, install dependencies, run python run.py on port 8020) and frontend (Node.js 18+, install dependencies, run npm run dev on port 5173). Use CLI tools like add_server.py to add Git repositories or cli.py to generate YAML configs.

Key features of Toolbox - MCP Server Manager

  • Repository management (add, view, delete)
  • Automatic README information extraction via GPT
  • YAML and JSON configuration generation
  • Modern UI with React and Tailwind CSS
  • Command-line tools for repository and config management
  • Vector-based search for repositories

Use cases of Toolbox - MCP Server Manager

  • Centralizing and managing multiple MCP server repositories
  • Automatically generating MCP configuration files from repository metadata
  • Searching through MCP server repositories using semantic similarity
  • Maintaining a local catalog of MCP servers with automatic documentation extraction

よくある質問

1

What are the main dependencies to run Toolbox - MCP Server Manager?

Python 3.8+, Node.js 18+, Git, an OpenAI API key, and a Qdrant vector database instance.

2

Where is repository data stored and how is it managed?

Repository data is stored in a Qdrant vector database. The connection parameters can be configured in the Vector Settings section of the application.

3

What CLI tools are available and how do I use them?

Two tools: `add_server.py` (adds Git repositories to the database) and `cli.py` (generates YAML configs, lists repositories). Run them from the backend directory with appropriate arguments.

4

Is the OpenAI API key handled securely?

The README notes that the API key is currently hardcoded in `backend/app/services/openai_service.py` and recommends using an environment variable in production.

5

What transports and authentication are used?

The API is served via HTTP on port 8020, with Swagger UI and ReDoc documentation. No authentication mechanism is mentioned in the README.

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