mcp-server

dfernand90/mcp-server

3.2

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A Model Context Protocol (MCP) server implementation in Python, designed for deployment to Azure Web App with GitHub Actions integration.

Tools

  1. echo

    Echo back the input message.

  2. get_time

    Get current server time.

  3. placeholder_tool

    A placeholder for your custom implementation.

MCP Server - Placeholder Implementation

A Model Context Protocol (MCP) server implementation in Python, ready for deployment to Azure Web App via GitHub Actions.

Features

  • MCP Protocol Support: Implements the Model Context Protocol specification
  • Placeholder Tools: Includes sample tools that you can replace with your own implementations
  • Azure Web App Ready: Configured for deployment to Azure Web App
  • GitHub Actions CI/CD: Automated testing and deployment
  • Multiple Interfaces:
    • STDIO for local development
    • HTTP REST API for web access
    • WebSocket for real-time communication
  • Docker Support: Containerized deployment option
  • Health Checks: Built-in health monitoring for Azure

Project Structure

mcp-server/
ā”œā”€ā”€ main.py                 # Core MCP server implementation
ā”œā”€ā”€ app.py                  # FastAPI wrapper for web deployment
ā”œā”€ā”€ requirements.txt        # Python dependencies
ā”œā”€ā”€ startup.sh             # Azure startup script
ā”œā”€ā”€ web.config             # Azure Web App configuration
ā”œā”€ā”€ Dockerfile             # Container configuration
ā”œā”€ā”€ test_mcp_server.py     # Unit tests
ā”œā”€ā”€ .github/workflows/     # GitHub Actions CI/CD
ā””ā”€ā”€ README.md              # This file

Quick Start

Local Development

  1. Clone the repository

    git clone <your-repo-url>
cd mcp-server

  2. Install dependencies

    pip install -r requirements.txt

  3. Run the server locally

    # STDIO mode (for MCP clients)
python main.py

# Web mode (for HTTP access)
python app.py

  4. Test the server

    python -m pytest test_mcp_server.py -v

Azure Deployment

  1. Create Azure Web App

    • Go to Azure Portal
    • Create a new Web App with Python runtime
    • Note the app name and get the publish profile

  2. Configure GitHub Secrets

    • AZURE_WEBAPP_NAME: Your Azure Web App name
    • AZURE_WEBAPP_PUBLISH_PROFILE: Download from Azure Portal

  3. Deploy

    • Push to main branch
    • GitHub Actions will automatically build and deploy

Available Tools (Placeholders)

The server includes three placeholder tools that you can replace with your own implementations:

1. Echo Tool

  • Name: echo
  • Description: Echo back the input message
  • Parameters: message (string)

2. Get Time Tool

  • Name: get_time
  • Description: Get current server time
  • Parameters: None

3. Placeholder Tool

  • Name: placeholder_tool
  • Description: A placeholder for your custom implementation
  • Parameters: input (string)

API Endpoints

When deployed as a web app, the server provides these endpoints:

  • GET / - Server information
  • GET /health - Health check
  • GET /tools - List available tools
  • POST /mcp - MCP protocol requests
  • POST /tools/call - Direct tool execution
  • WebSocket /ws - Real-time MCP communication

Customization

Adding Your Own Tools

  1. Update _setup_default_tools() in main.py:

    def _setup_default_tools(self):
    self.tools = {
        "your_tool": {
            "name": "your_tool",
            "description": "Description of your tool",
            "inputSchema": {
                "type": "object",
                "properties": {
                    "param1": {
                        "type": "string",
                        "description": "Parameter description"
                    }
                },
                "required": ["param1"]
            }
        }
    }

  2. Implement tool logic in _execute_tool() method:

    async def _execute_tool(self, tool_name: str, arguments: Dict[str, Any]) -> str:
    if tool_name == "your_tool":
        # Your custom implementation here
        param1 = arguments.get('param1')
        result = your_custom_logic(param1)
        return f"Result: {result}"

Adding Resources

Resources are static or dynamic content that tools can access:

def __init__(self):
    self.resources = {
        "your_resource": {
            "uri": "resource://your_resource",
            "name": "Your Resource",
            "description": "Description of your resource",
            "mimeType": "text/plain"
        }
    }

Adding Prompts

Prompts are reusable templates for AI interactions:

def __init__(self):
    self.prompts = {
        "your_prompt": {
            "name": "your_prompt",
            "description": "Description of your prompt",
            "arguments": [
                {
                    "name": "context",
                    "description": "Context for the prompt",
                    "required": True
                }
            ]
        }
    }

Testing

Run the test suite:

# Run all tests
python -m pytest test_mcp_server.py -v

# Run specific test
python -m pytest test_mcp_server.py::TestMCPServer::test_echo_tool -v

# Run with coverage
pip install pytest-cov
python -m pytest test_mcp_server.py --cov=main --cov-report=html

Environment Variables

Configure these environment variables for deployment:

  • PORT: Server port (default: 8000)
  • PYTHONUNBUFFERED: Set to 1 for Azure
  • PYTHONDONTWRITEBYTECODE: Set to 1 for Azure

Docker Deployment

Build and run with Docker:

# Build image
docker build -t mcp-server .

# Run container
docker run -p 8000:8000 mcp-server

# Run with environment variables
docker run -p 8000:8000 -e PORT=8000 mcp-server

Monitoring and Logging

The server includes comprehensive logging:

  • Application logs: All requests and responses
  • Error logs: Detailed error information
  • Health checks: Available at /health endpoint
  • Azure logs: Check Azure portal for deployment logs

Security Considerations

  • CORS: Configured for web deployment
  • Input validation: Validate all tool parameters
  • Error handling: Graceful error responses
  • Rate limiting: Consider implementing for production
  • Authentication: Add authentication for sensitive operations

Performance Optimization

For production deployment:

  1. Use Gunicorn: Pre-configured in startup script
  2. Configure workers: Adjust based on your needs
  3. Enable caching: Implement caching for expensive operations
  4. Monitor resources: Use Azure Application Insights
  5. Scale horizontally: Use Azure App Service scaling

Troubleshooting

Common Issues

  1. Deployment fails:

    • Check Azure publish profile
    • Verify GitHub secrets
    • Review deployment logs

  2. Tools not responding:

    • Check tool implementation
    • Verify parameter schemas
    • Review server logs

  3. Performance issues:

    • Check worker configuration
    • Monitor memory usage
    • Review database connections

Debug Mode

Enable debug mode for development:

# In app.py
app = FastAPI(debug=True)

# In main.py
logging.basicConfig(level=logging.DEBUG)

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Add your implementation
  4. Write tests
  5. Submit a pull request

License

This project is open source and available under the MIT License.

Support

For issues and questions:

  • Check the troubleshooting section
  • Review Azure Web App documentation
  • Open an issue on GitHub

Next Steps

  1. Replace placeholder tools with your actual implementations
  2. Add authentication if needed
  3. Implement caching for better performance
  4. Add monitoring and alerting
  5. Scale based on usage patterns

Note: This is a placeholder implementation. Replace the example tools with your actual business logic and add proper error handling, validation, and security measures for production use.