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Development Guide

Building MCP Servers

This guide covers development best practices for creating MCP servers in various programming languages.

Language Support

MCP has official SDKs and community support for multiple languages:

Official SDKs

  • 🐍 Python - Full-featured SDK with FastMCP framework
  • 📜 JavaScript/TypeScript - Node.js and browser support
  • 🐹 Go - High-performance native implementation

Community SDKs

  • 🦀 Rust - Systems programming with safety
  • ☕ Java - Enterprise applications
  • 💎 Ruby - Web applications

Development Workflow

1. Setup Environment

# Create project directory
mkdir my-mcp-server
cd my-mcp-server

# Initialize version control
git init

# Setup language-specific environment
make venv  # Python
npm init   # JavaScript
go mod init # Go

2. Project Structure

my-mcp-server/
├── src/              # Source code
├── tests/            # Test files
├── docs/             # Documentation
├── Makefile          # Build automation
├── Containerfile     # Container definition
├── README.md         # Project documentation
└── pyproject.toml    # Dependencies (Python)

3. Development Cycle

  1. Write code with type hints/annotations
  2. Add comprehensive tests
  3. Document with docstrings
  4. Run linters and formatters
  5. Build and test locally
  6. Container testing
  7. Integration testing

Core Development Principles

1. Single Responsibility

Each MCP server should have a clear, focused purpose: - ✅ GitHub integration server - ✅ Database connector server - ❌ GitHub + Jira + Database server

2. Type Safety

Use strong typing for all interfaces: 

def process_data(input: str, count: int) -> dict[str, Any]:
    """Process input data with type hints"""

3. Error Handling

Implement comprehensive error handling: 

try:
    result = risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")
    raise McpError("User-friendly error message")

4. Input Validation

Always validate and sanitize inputs: 

from pydantic import BaseModel, validator

class ToolInput(BaseModel):
    text: str
    
    @validator('text')
    def validate_text(cls, v):
        if not v.strip():
            raise ValueError("Text cannot be empty")
        return v

Testing Strategy

Unit Tests

Test individual functions and methods: 

def test_tool_function():
    result = my_tool("input")
    assert result == "expected"

Integration Tests

Test server endpoints and protocols: 

curl -X POST http://localhost:8000/mcp \
  -d '{"method": "tools/call", ...}'

End-to-End Tests

Test complete workflows through gateway: 

mcp-cli cmd --server my-server \
  --tool my_tool --tool-args '{...}'

Performance Considerations

Optimization Tips

  1. Async Operations: Use async/await for I/O operations
  2. Connection Pooling: Reuse database/API connections
  3. Caching: Cache frequently accessed data
  4. Batch Processing: Group operations when possible
  5. Resource Limits: Set timeouts and memory limits

Monitoring

  • Request/response times
  • Error rates
  • Resource usage
  • Concurrent connections

Security Best Practices

  1. Never hardcode secrets
  2. Validate all inputs
  3. Use environment variables
  4. Implement rate limiting
  5. Log security events
  6. Regular dependency updates

Documentation Requirements

Every MCP server must include: - README with setup instructions - API documentation - Environment variable reference - Example usage - Troubleshooting guide