Intervals.icu MCP Server

--- description: globs: alwaysApply: true --- # Testing Patterns and Practices ## Test Organization Tests are organized in the [tests/](mdc:tests) directory with the following structure: - **[tests/test_server.py](mdc:tests/test_server.py)** - Main MCP tool testing - **[tests/test_formatting.py](mdc:tests/test_formatting.py)** - Utility function tests - **[tests/test_make_intervals_request.py](mdc:tests/test_make_intervals_request.py)** - API communication tests - **[tests/sample_data.py](mdc:tests/sample_data.py)** - Mock data for testing ## Testing Framework Setup - **Framework**: pytest with async support (`pytest-asyncio`) - **Mocking**: pytest-mock for HTTP request mocking - **Configuration**: Test settings in [pyproject.toml](mdc:pyproject.toml) under `[tool.pytest.ini_options]` ## Testing Patterns ### Async Testing All MCP tools are async functions, so tests use: ```python @pytest.mark.asyncio async def test_function_name(): # Test async MCP tools ``` ### Mock API Responses HTTP requests are mocked using `pytest-mock`: ```python def test_api_call(mocker): mock_response = mocker.Mock() mock_response.json.return_value = {"test": "data"} mock_response.raise_for_status.return_value = None mocker.patch("httpx.AsyncClient.get", return_value=mock_response) ``` ### Test Data Management - **Sample Data**: [tests/sample_data.py](mdc:tests/sample_data.py) contains realistic mock data - **Isolation**: Each test uses fresh mock data to avoid side effects - **Coverage**: Tests cover both success and error scenarios ## Running Tests ```bash # Run all tests pytest # Run with verbose output pytest -v # Run specific test file pytest tests/test_server.py # Run with coverage (if installed) pytest --cov=src/intervals_mcp_server ``` ## Test Requirements Before committing code, ensure: 1. **All tests pass**: `pytest` returns exit code 0 2. **No new linting errors**: `ruff .` passes 3. **Type checking passes**: `mypy src tests` succeeds ## Writing New Tests When adding new MCP tools or utilities: 1. **Create test cases** in appropriate test file 2. **Mock external dependencies** (HTTP requests, file I/O) 3. **Test both success and error paths** 4. **Use realistic test data** from [tests/sample_data.py](mdc:tests/sample_data.py) 5. **Follow async testing patterns** for MCP tools 6. **Verify error message formatting** for user-facing errors ## Mock Strategy The project uses comprehensive mocking to: - **Avoid real API calls** during testing - **Test error handling** by simulating various HTTP error responses - **Ensure deterministic results** with controlled test data - **Speed up test execution** by eliminating network requests