MetaMCP

AI-Powered Model Context Protocol (MCP) Code Generator

MetaMCP is a full-stack application that automatically generates MCP (Model Context Protocol) code based on natural language descriptions. Simply describe what you want your MCP to do, and MetaMCP will generate a complete, ready-to-use package with all necessary files.

✨ Features

• 🤖 AI-Powered Generation – Generate high-quality MCP code using GPT-4o-mini via OpenRouter
• 📦 Complete Package Creation – Automatically creates Python code, requirements.txt, README.md, and .env template
• ⬇️ One-Click Download – Download complete MCP packages as ZIP files
• 👁️ Live Preview – View generated code before downloading
• 🔍 Smart Dependencies – Detects and includes required Python packages
• 💾 Local Storage – Generated files saved in mcp/generated/ for easy access
• 🎨 Modern UI – Clean and responsive React frontend with real-time feedback
• • 💾 Github Integration – Automatically commits in github

🏗️ Architecture

Backend (FastAPI)

• Framework: FastAPI (Python)
• AI Integration: OpenAI client connected via OpenRouter
• File Management: Automatic file creation and ZIP packaging
• API Endpoints: RESTful API for code generation and downloads

Frontend (React/Next.js)

• Framework: React with TypeScript
• Styling: Modern inline styles
• State Management: React Hooks
• UX: Real-time feedback and download management

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v16 or higher)
• Python (v3.8 or higher)
• OpenRouter API Key (for AI-powered code generation)

📂 Project Structure

metamcp/
├── backend/
│   ├── main.py              # FastAPI backend application
│   ├── .env                 # Environment variables
│   └── mcp/generated/       # Generated files storage
├── frontend/
│   ├── src/
│   │   └── pages/
│   │       └── index.tsx    # Main React component
│   ├── package.json
│   └── next.config.js
└── README.md

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/Anusha806/MetaMCP.git
cd metamcp

2. Create a .env file and add the followinf API keys

OPENROUTER_API_KEY=sk-xxxx...
GITHUB_TOKEN=github.....
GITHUB_REPO=githubusername/repositoryname

3. Backend Setup

# Navigate to backend
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
.\venv\Scripts\activate   # Windows
# source venv/bin/activate  # macOS/Linux

# Install dependencies
pip install requirements.txt

# Start backend server
uvicorn app.main:app --reload --port 8000

# Verify if your backend is running
navigate to https://localhost:8000

4. Frontend Setup

# Open a new terminal and navigate to frontend
cd frontend

# Install dependencies
npm install

# Start development server
npm run build 
npm start

5. Access the Application

Open your browser and navigate to:

http://localhost:3000

📖 Usage

1. Enter Description – Describe the MCP functionality you want in natural language
2. Generate Code – Click the "Generate MCP" button
3. Preview Output – Review the generated code in the preview panel
4. Download Package – Click "Download ZIP" to get the complete package

Example Prompts

Build me a tool that fetches and displays the 7-day weather forecast for Vijayawada, India. 
The tool should return:
- The daily high and low temperature
- A short description of the conditions (e.g., sunny, cloudy, thunderstorms)
- Any severe weather alerts if available
- Format the output in a clean, readable table or list

Create a file manager MCP that can:
- List files in a directory
- Read file contents
- Create new files
- Delete files with confirmation

📦 Generated Package Structure

When you download a generated MCP package, it will contain:

your_mcp_package.zip
├── your_mcp_code.py      # Main MCP implementation
├── requirements.txt      # Python dependencies
├── README.md             # Documentation and usage instructions
└── .env                  # Environment variables template

🔄 How It Works

1. User Input → Describe the desired MCP functionality
2. AI Processing → Backend sends request to GPT-4o-mini (via OpenRouter)
3. Code Generation → AI generates Python MCP code with proper structure
4. Package Creation → Backend creates requirements.txt, README.md, and .env files
5. ZIP Bundling → All files are packaged into a downloadable ZIP
6. Download → User downloads the ready-to-use MCP package
7. GitHub Integration → Automatically commits the zip folder in your github repository

🆘 Support

If you encounter any issues or have questions:

1. Provide detailed information about your environment and the issue

🙏 Acknowledgments

• OpenRouter for providing AI model access
• FastAPI for the excellent Python web framework
• React for the powerful frontend library
• Model Context Protocol community for the protocol specifications

📊 Roadmap

• Support for multiple AI models
• Custom templates and boilerplates
• Built-in MCP testing tools
• Integration with popular IDEs
• Community-shared MCP templates

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Built with ❤️ by the MetaMCP Team

GitHub • Report Bug