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MCP SaaS Platform
A software-as-a-service platform for creating and managing Model Context Protocol (MCP) servers.
Overview
This platform allows users to easily create custom MCP servers by selecting templates and providing API keys and credentials. The service handles the generation, deployment, and management of MCP servers, making it easy for developers to integrate AI capabilities into their applications.
Features
- User-friendly web interface for creating MCP servers
- Documentation-driven MCP server generation
- LLM-powered code generation for custom APIs
- Secure API key management
- Multiple deployment options
- Monitoring and management tools
Architecture
The platform uses a sophisticated architecture:
-
Documentation Processing:
- Handles various documentation formats (OpenAPI, Markdown)
- Extracts API structure and requirements
- Uses Jina AI for comprehensive document understanding
-
LLM Workflow:
- Planning LLM (GPT-4) analyzes docs and creates implementation plan
- Coding LLM (GPT-3.5) generates high-quality MCP code
- Validation ensures generated code meets requirements
-
FastMCP Integration:
- Templates follow FastMCP best practices
- Proper tool definition and implementation
- Error handling and security built-in
Project Structure
mcp-saas/
βββ frontend/ # React/Next.js frontend application
βββ backend/ # FastAPI backend application
β βββ api/ # API routes and controllers
β β βββ auth/ # Authentication endpoints
β β βββ servers/ # Server management endpoints
β β βββ templates/ # Template management endpoints
β β βββ generators/ # LLM generation endpoints
β βββ core/ # Core application logic
β β βββ security/ # Authentication and security
β β βββ config/ # Configuration management
β β βββ utils/ # Utility functions
β βββ db/ # Database models and connections
β β βββ models/ # SQLAlchemy models
β β βββ migrations/ # Alembic migrations
β βββ engine/ # Server generation engine
β β βββ generator/ # Code generation logic
β β β βββ doc_processor.py # Documentation processing
β β β βββ llm_workflow.py # LLM workflow
β β β βββ mcp_generator_service.py # Generator service
β β βββ validator/ # Validation logic
β β βββ deployer/ # Deployment logic
β βββ templates/ # MCP server templates
β βββ fastmcp_base/ # Base FastMCP template
β βββ generated/ # Generated MCP server templates
βββ shared/ # Shared code/types between frontend and backend
βββ docs/ # Documentation
βββ deployment/ # Deployment configuration
Getting Started
Prerequisites
- Python 3.9+
- Node.js 16+
- API keys for OpenAI/OpenRouter and Jina AI
- PostgreSQL database (optional, SQLite for development)
Installation
-
Clone the repository:
git clone https://github.com/yourusername/mcp-saas.git cd mcp-saas -
Install backend dependencies:
cd backend pip install -r requirements.txt -
Install frontend dependencies:
cd ../frontend npm install -
Configure environment variables:
cd ../backend cp .env.example .env # Edit .env with your configuration -
Initialize the database:
alembic upgrade head
Running the Application
-
Start the backend:
cd backend uvicorn main:app --reload -
Start the frontend:
cd frontend npm run dev
API Documentation
Once the application is running, you can access the API documentation at http://localhost:8000/docs.
License
MIT
