Kingdamienjl/athena-mcp-server

Athena MCP Server

A comprehensive Model Context Protocol (MCP) server that provides AI-powered tools and system utilities. This server integrates with OpenAI GPT models to deliver intelligent responses and analysis capabilities.

Features

Core AI Tools (OpenAI GPT-powered)

• ask_athena: Intelligent AI assistant for general queries and problem-solving
• analyze_code: Advanced code analysis with optimization suggestions
• generate_code: Intelligent code generation based on requirements
• text_summarize: AI-powered text summarization with customizable length and style
• translate_text: Multi-language translation using OpenAI models
• image_generate: DALL-E powered image generation

System & Development Tools

• get_system_stats: Real-time system monitoring (CPU, memory, disk usage)
• file_operations: Comprehensive file and directory management
• process_monitor: System process monitoring and management
• docker_manage: Docker container and image management
• network_tools: Network diagnostics (ping, port scan, DNS lookup, traceroute)

Web & API Tools

• web_request: HTTP client for API testing and web scraping
• weather_info: Real-time weather information using OpenWeatherMap API
• github_operations: GitHub repository management and code search

📁 Project Structure

Athena MCP/
├── app.js                 # Backend entry point
├── mcp-server.js          # MCP server for Trae integration
├── mcp-config.json        # MCP configuration file
├── package.json           # Backend dependencies
├── .env                   # Environment variables
├── tools/                 # Custom tools directory
│   └── get_cpu_stats.js   # CPU statistics tool
├── frontend/              # React frontend
│   ├── package.json       # Frontend dependencies
│   ├── public/
│   └── src/
│       ├── App.js         # Main React component
│       ├── App.css        # Component styles
│       ├── index.js       # React entry point
│       └── index.css      # Global styles
├── docker-compose.yml     # Docker orchestration
├── Dockerfile.backend     # Backend Docker image
└── README.md             # This file

🔌 MCP Integration with Trae

Quick Setup for Trae

1. Install dependencies:

   npm install
   

2. Start MCP server:

   npm run mcp
   

3. Add to Trae configuration: Add this to your Trae MCP configuration:

   {
     "mcpServers": {
       "athena": {
         "command": "node",
         "args": ["mcp-server.js"],
         "cwd": "d:\\Projects\\Athena MCP"
       }
     }
   }
   

Available MCP Tools

MCP Tool Examples

Ask Athena:

{
  "name": "ask_athena",
  "arguments": {
    "prompt": "How do I optimize React performance?",
    "context": "Working on a large React application with performance issues"
  }
}

Get System Stats:

{
  "name": "get_system_stats",
  "arguments": {
    "detailed": true
  }
}

Analyze Code:

{
  "name": "analyze_code",
  "arguments": {
    "code": "function fibonacci(n) { return n <= 1 ? n : fibonacci(n-1) + fibonacci(n-2); }",
    "language": "javascript",
    "analysis_type": "optimize"
  }
}

🛠️ Setup & Installation

Prerequisites

• Node.js 18+ and npm
• (Optional) Docker and Docker Compose

Method 1: Local Development

1. Clone and setup backend:

   cd "d:\Projects\Athena MCP"
   npm install
   

2. Setup frontend:

   cd frontend
   npm install
   

3. Configure environment:

   • Edit .env file and add your OpenAI API key:

   PORT=4000
   OPENAI_API_KEY=your_actual_api_key_here
   

4. Run the applications:

   Terminal 1 (Backend):

   npm start
   # Backend runs on http://localhost:4000
   

   Terminal 2 (Frontend):

   cd frontend
   npm start
   # Frontend runs on http://localhost:3000
   

Method 2: Docker Compose

1. Set environment variables:

   # Create .env file with your API key
   echo "OPENAI_API_KEY=your_actual_api_key_here" > .env
   

2. Run with Docker:

   docker-compose up --build
   

   This will start:

   • Backend on http://localhost:4000
   • Frontend on http://localhost:3000

🔌 API Endpoints

Backend API (Port 4000)

Example API Usage

Ask Athena a question:

curl -X POST http://localhost:4000/ask \
  -H "Content-Type: application/json" \
  -d '{"prompt": "What is artificial intelligence?"}'

Get CPU statistics:

curl http://localhost:4000/cpu

🎨 Frontend Features

• Modern UI: Clean, responsive design with gradient backgrounds
• Real-time Interaction: Instant feedback and loading states
• Error Handling: User-friendly error messages
• Mobile Responsive: Works on all device sizes
• System Monitoring: Visual display of CPU and memory stats

🔧 Development

Adding New Tools

1. Create a new file in the tools/ directory:

   // tools/my_new_tool.js
   function myNewTool() {
     // Your tool logic here
     return { result: "Tool output" };
   }
   
   module.exports = { myNewTool };
   

2. Import and use in app.js:

   const { myNewTool } = require('./tools/my_new_tool');
   
   app.get('/my-endpoint', (req, res) => {
     const result = myNewTool();
     res.json(result);
   });
   

Environment Variables

🐳 Docker Commands

# Build and run
docker-compose up --build

# Run in background
docker-compose up -d

# Stop services
docker-compose down

# View logs
docker-compose logs -f

# Rebuild specific service
docker-compose build backend
docker-compose build frontend

🚀 Production Deployment

1. Set production environment variables
2. Build optimized frontend:

   cd frontend
   npm run build
   

3. Use process manager like PM2:

   npm install -g pm2
   pm2 start app.js --name athena-backend
   

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📝 License

MIT License - feel free to use this project for your own purposes.

🆘 Troubleshooting

Backend won't start:

• Check if port 4000 is available
• Verify Node.js version (18+)
• Check .env file configuration

Frontend can't connect to backend:

• Ensure backend is running on port 4000
• Check CORS configuration
• Verify API_BASE_URL in frontend

Docker issues:

• Ensure Docker is running
• Check port conflicts
• Verify environment variables in docker-compose.yml

Happy coding! 🎉