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    jlowin

    testing-python

    jlowin/testing-python
    Coding
    22,710
    4 installs

    About

    SKILL.md

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    About

    Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage...

    SKILL.md

    Writing Effective Python Tests

    Core Principles

    Every test should be atomic, self-contained, and test single functionality. A test that tests multiple things is harder to debug and maintain.

    Test Structure

    Atomic unit tests

    Each test should verify a single behavior. The test name should tell you what's broken when it fails. Multiple assertions are fine when they all verify the same behavior.

    # Good: Name tells you what's broken
    def test_user_creation_sets_defaults():
        user = User(name="Alice")
        assert user.role == "member"
        assert user.id is not None
        assert user.created_at is not None
    
    # Bad: If this fails, what behavior is broken?
    def test_user():
        user = User(name="Alice")
        assert user.role == "member"
        user.promote()
        assert user.role == "admin"
        assert user.can_delete_others()
    

    Use parameterization for variations of the same concept

    import pytest
    
    @pytest.mark.parametrize("input,expected", [
        ("hello", "HELLO"),
        ("World", "WORLD"),
        ("", ""),
        ("123", "123"),
    ])
    def test_uppercase_conversion(input, expected):
        assert input.upper() == expected
    

    Use separate tests for different functionality

    Don't parameterize unrelated behaviors. If the test logic differs, write separate tests.

    Project-Specific Rules

    No async markers needed

    This project uses asyncio_mode = "auto" globally. Write async tests without decorators:

    # Correct
    async def test_async_operation():
        result = await some_async_function()
        assert result == expected
    
    # Wrong - don't add this
    @pytest.mark.asyncio
    async def test_async_operation():
        ...
    

    Imports at module level

    Put ALL imports at the top of the file:

    # Correct
    import pytest
    from fastmcp import FastMCP
    from fastmcp.client import Client
    
    async def test_something():
        mcp = FastMCP("test")
        ...
    
    # Wrong - no local imports
    async def test_something():
        from fastmcp import FastMCP  # Don't do this
        ...
    

    Use in-memory transport for testing

    Pass FastMCP servers directly to clients:

    from fastmcp import FastMCP
    from fastmcp.client import Client
    
    mcp = FastMCP("TestServer")
    
    @mcp.tool
    def greet(name: str) -> str:
        return f"Hello, {name}!"
    
    async def test_greet_tool():
        async with Client(mcp) as client:
            result = await client.call_tool("greet", {"name": "World"})
            assert result[0].text == "Hello, World!"
    

    Only use HTTP transport when explicitly testing network features.

    Inline snapshots for complex data

    Use inline-snapshot for testing JSON schemas and complex structures:

    from inline_snapshot import snapshot
    
    def test_schema_generation():
        schema = generate_schema(MyModel)
        assert schema == snapshot()  # Will auto-populate on first run
    

    Commands:

    • pytest --inline-snapshot=create - populate empty snapshots
    • pytest --inline-snapshot=fix - update after intentional changes

    Fixtures

    Prefer function-scoped fixtures

    @pytest.fixture
    def client():
        return Client()
    
    async def test_with_client(client):
        result = await client.ping()
        assert result is not None
    

    Use tmp_path for file operations

    def test_file_writing(tmp_path):
        file = tmp_path / "test.txt"
        file.write_text("content")
        assert file.read_text() == "content"
    

    Mocking

    Mock at the boundary

    from unittest.mock import patch, AsyncMock
    
    async def test_external_api_call():
        with patch("mymodule.external_client.fetch", new_callable=AsyncMock) as mock:
            mock.return_value = {"data": "test"}
            result = await my_function()
            assert result == {"data": "test"}
    

    Don't mock what you own

    Test your code with real implementations when possible. Mock external services, not internal classes.

    Test Naming

    Use descriptive names that explain the scenario:

    # Good
    def test_login_fails_with_invalid_password():
    def test_user_can_update_own_profile():
    def test_admin_can_delete_any_user():
    
    # Bad
    def test_login():
    def test_update():
    def test_delete():
    

    Error Testing

    import pytest
    
    def test_raises_on_invalid_input():
        with pytest.raises(ValueError, match="must be positive"):
            calculate(-1)
    
    async def test_async_raises():
        with pytest.raises(ConnectionError):
            await connect_to_invalid_host()
    

    Running Tests

    uv run pytest -n auto              # Run all tests in parallel
    uv run pytest -n auto -x           # Stop on first failure
    uv run pytest path/to/test.py      # Run specific file
    uv run pytest -k "test_name"       # Run tests matching pattern
    uv run pytest -m "not integration" # Exclude integration tests
    

    Checklist

    Before submitting tests:

    • Each test tests one thing
    • No @pytest.mark.asyncio decorators
    • Imports at module level
    • Descriptive test names
    • Using in-memory transport (not HTTP) unless testing networking
    • Parameterization for variations of same behavior
    • Separate tests for different behaviors
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    Repository
    jlowin/fastmcp
    Files