Coding Standards MCP Server

# Python Best Practices ## Project Structure ``` project-root/ ├── src/ # Source code │ └── mypackage/ │ ├── __init__.py # Package initialization │ ├── cli.py # Command-line interface │ ├── config.py # Configuration handling │ ├── constants.py # Constants and enums │ ├── models/ # Data models │ │ ├── __init__.py │ │ └── base.py │ ├── services/ # Business logic │ └── utils/ # Helper functions ├── tests/ # Test suite │ ├── __init__.py │ ├── conftest.py # pytest fixtures │ ├── test_models.py │ └── test_services.py ├── docs/ # Documentation ├── scripts/ # Maintenance scripts ├── .env.example # Environment template ├── .gitignore ├── README.md ├── pyproject.toml # Project metadata (PEP 621) └── requirements.txt # or poetry.lock ## Python-Specific Practices - Use virtual environments (venv, poetry) - Follow PEP guidelines - Use type hints for larger projects - Leverage duck typing and EAFP - Use iterators and generators - Implement context managers ## Dependency Management - Use modern tools (poetry, pip-tools) - Lock dependencies (poetry.lock, requirements.txt) - Specify version ranges appropriately - Use setup.py for libraries - Use pyproject.toml for applications ## Pythonic Code - Use list/dict comprehensions - Leverage generator expressions - Use context managers (with statement) - Follow EAFP (Easier to Ask for Forgiveness) - Use magic methods when appropriate - Leverage standard library ## Error Handling ```python # Yes try: value = data['key'] except KeyError: value = default # No if 'key' in data: value = data['key'] else: value = default ``` ## Configuration - Use environment variables - Use .env files for development - Use configuration objects - Validate configuration at startup - Keep secrets out of code ## Testing - Use pytest - Write fixtures - Use parametrized tests - Mock external services - Test edge cases ```python @pytest.mark.parametrize("input,expected", [ (1, 1), (2, 4), (3, 9), ]) def test_square(input, expected): assert square(input) == expected ``` ## Logging - Use structured logging - Configure proper log levels - Include context in logs - Use logging handlers - Implement log rotation ## Performance - Profile before optimizing - Use appropriate data structures - Consider asyncio for I/O - Implement caching - Use PyPy for CPU-bound tasks ## Documentation - Write clear docstrings - Generate API docs (sphinx) - Maintain README.md - Include examples - Document gotchas ## Development Tools - Use black for formatting - Use flake8 for linting - Use mypy for type checking - Use pre-commit hooks - Use tox for testing