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
Basic information
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.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mymcp-alexj-stl": {
"command": "uv",
"args": [
"venv"
]
}
}
}Tool list not available yet
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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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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