7134-easy-mcp-server by easynet-world - MCP Server

easy-mcp-server

Transform any function into a complete API ecosystem with AI integration

🎯 What You Get

Write ONE function → Get EVERYTHING:

✅ REST API - Instant HTTP endpoints
✅ MCP Tools - AI models can call your functions
✅ MCP Prompts - Template-based prompts with parameters
✅ MCP Resources - Documentation and data access
✅ OpenAPI - Complete API documentation
✅ Swagger UI - Interactive API explorer

⚡ 3 Simple Rules

Rule	Example	Result
File Path = API Path	`api/users/profile/get.js`	`GET /users/profile`
File Name = HTTP Method	`post.js`	`POST`
One Function = Everything	`process(req, res)`	REST + MCP + OpenAPI

Quick Example

const BaseAPI = require('easy-mcp-server/base-api');

class MyAPI extends BaseAPI {
  process(req, res) {
    res.json({ message: 'Hello World' });
  }
}

module.exports = MyAPI;

🚀 Quick Start

1. Install & Setup

npm install easy-mcp-server
mkdir -p api/users && touch api/users/get.js

2. Write Your API

// api/users/get.js
const BaseAPI = require('easy-mcp-server/base-api');

class GetUsers extends BaseAPI {
  process(req, res) {
    res.json({ users: [] });
  }
}

module.exports = GetUsers;

3. Start & Access

npx easy-mcp-server

Access Points:

🌐 REST API: http://localhost:3000
🤖 MCP Server: http://localhost:3001
📚 OpenAPI: http://localhost:3000/openapi.json
🔍 Swagger UI: http://localhost:3000/docs

4. Optional: Add MCP Features

# Add prompts
mkdir -p mcp/prompts/my-category
echo '{"description": "My AI prompt", "instructions": "..."}' > mcp/prompts/my-category/my-prompt.json

# Add resources  
mkdir -p mcp/resources/docs
echo '# My Guide' > mcp/resources/docs/my-guide.md

📚 Documentation

Document	Purpose	Best For
	Complete framework documentation	Deep dive, production setup
	AI agent integration guide	Building AI-powered applications
	Monitoring and observability	Production monitoring
	LLM-specific information	AI model integration

📋 Quick Reference

Getting Started: Quick Start →
AI Integration: → MCP Integration
Production: Production Ready →
Advanced: Advanced Features →

🛠 Advanced Features

Enhanced API (LLM + Logging)

const { BaseAPIEnhanced } = require('easy-mcp-server/lib/base-api-enhanced');

class MyEnhancedAPI extends BaseAPIEnhanced {
  constructor() {
    super('my-service', {
      llm: { provider: 'openai', apiKey: process.env.OPENAI_API_KEY }
    });
  }

  async process(req, res) {
    // this.llm, this.responseUtils available
    this.responseUtils.sendSuccessResponse(res, { data: 'Hello World' });
  }
}

Auto-Generated OpenAPI with JSDoc

/**
 * @description Get user information
 * @summary Retrieve user details
 * @tags users
 * @requestBody { "type": "object", "required": ["userId"], "properties": { "userId": { "type": "string" } } }
 */
class GetUser extends BaseAPI {
  process(req, res) {
    // Your code here
  }
}

🔧 Configuration

Environment Variables

PORT=3000                    # REST API port
MCP_PORT=3001               # MCP server port  
OPENAI_API_KEY=your-key-here      # OpenAI API key

CLI Options

easy-mcp-server --port 3000 --mcp-port 3001 --api-dir ./api

📦 What You Get

Feature	Description	Auto-Generated
REST API	HTTP endpoints from file structure	✅
MCP Tools	AI-callable functions	✅
OpenAPI Docs	Complete API documentation	✅
Swagger UI	Interactive API explorer	✅
MCP Prompts	Template-based AI prompts	✅
MCP Resources	Documentation & data access	✅

File Structure → API Endpoints

api/users/get.js          →  GET /users
api/users/post.js         →  POST /users  
api/users/profile/put.js  →  PUT /users/profile

MCP Prompts & Resources

Prompts - Template-based AI prompts:

mcp/prompts/category/prompt.json

Resources - Documentation & data:

mcp/resources/category/resource.md

Example Prompt:

{
  "description": "Generate API documentation",
  "arguments": { "type": "object", "properties": { "endpoint": { "type": "string" } } },
  "instructions": "You are an API expert..."
}

🚀 Production Ready

Feature	Description
Hot Reloading	Instant updates during development
MCP Protocol	Full AI model integration
LLM Integration	AI service integration
Health Monitoring	Built-in health checks
Structured Logging	Comprehensive logging
Rate Limiting	Built-in rate limiting

📄 License

MIT License - see file for details.

🤝 Contributing

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📞 Support

Issues: GitHub Issues
Documentation:
Examples: Check the api/example/ directory