testing-python
Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
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name: testing-python description: Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
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 snapshotspytest --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.asynciodecorators - 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