What does MyMCP Prompt generate?
It generates a complete Python MCP server script and a corresponding JSON configuration file based on a user-written description.
@AlexJ-StL
基本情報
MyMCP Prompt is a tool for generating Model Context Protocol (MCP) servers from natural language descriptions. This MVP uses the Google Gemini API to convert user descriptions into functional Python MCP servers with corresponding JSON configurations.
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": {
"mymcp-alexj-stl": {
"command": "uv",
"args": [
"venv"
]
}
}
}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。
MyMCP Prompt is a tool for generating Model Context Protocol (MCP) servers from natural language descriptions. It uses the Google Gemini API to convert user descriptions into functional Python MCP servers with corresponding JSON configurations. The application consists of a Flask backend and a React frontend built with Vite.
Clone the repository, set up the Python virtual environment and install dependencies, then install Node.js dependencies for the frontend. Set the GEMINI_API_KEY environment variable with a valid Google Gemini API key. Start the Flask backend with flask run and the frontend dev server with npm run dev. Open http://localhost:5173, enter a server description, and click "Place Your Order" to generate the server code and configuration.
/api/generate-mcp endpoint.It generates a complete Python MCP server script and a corresponding JSON configuration file based on a user-written description.
Python packages listed in `requirements.txt` (Flask, Google Generative AI client, etc.) and Node.js dependencies for the frontend (Vite, React). A Google Gemini API key is also required.
Set the environment variable `GEMINI_API_KEY` to your key before starting the backend. On Windows, use `set GEMINI_API_KEY=your_key`; on macOS/Linux, add `export GEMINI_API_KEY="your_key"` to your shell config.
The files are saved to a directory chosen by the backend LLM (the current version does not let the user specify an output directory). The frontend displays the saved file paths.
Currently, only the Google Gemini API is supported. Future versions plan to integrate additional LLMs (OpenRouter, OpenAI, Anthropic, etc.).

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