Chris Farhood
935abf86d4
build-image / build (push) Failing after 18s
- Bump mcp[cli] 1.22 -> 1.28.1 (negotiates MCP protocol 2025-11-25, matching current Claude clients; the old 2025-06-18 server never got a tools/list on the connector surface). - Bake transport config into code: stateless_http + json_response for HTTP (single JSON body instead of a 34KB SSE stream, which the connector pipeline handles far more reliably). - Bake Authentik OAuth (AuthSettings + JWT TokenVerifier) into intervals_mcp_server.auth, configured from MCP_ISSUER/MCP_RESOURCE/MCP_JWKS_URI/MCP_CLIENT_ID — removes the runtime FastMCP.__init__ monkeypatch from the k8s deployment command. - Accept token audience with/without trailing slash (RFC 8707 clients use the slash-normalised resource metadata value). - Dockerfile CMD runs the module (transport via MCP_TRANSPORT); add .gitea CI to build+push the image to git.farh.net/farhoodlabs/intervalsicu-mcp. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
90 lines
2.6 KiB
Plaintext
90 lines
2.6 KiB
Plaintext
---
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description:
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globs:
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alwaysApply: true
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---
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# Testing Patterns and Practices
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## Test Organization
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Tests are organized in the [tests/](mdc:tests) directory with the following structure:
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- **[tests/test_server.py](mdc:tests/test_server.py)** - Main MCP tool testing
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- **[tests/test_formatting.py](mdc:tests/test_formatting.py)** - Utility function tests
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- **[tests/test_make_intervals_request.py](mdc:tests/test_make_intervals_request.py)** - API communication tests
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- **[tests/sample_data.py](mdc:tests/sample_data.py)** - Mock data for testing
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## Testing Framework Setup
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- **Framework**: pytest with async support (`pytest-asyncio`)
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- **Mocking**: pytest-mock for HTTP request mocking
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- **Configuration**: Test settings in [pyproject.toml](mdc:pyproject.toml) under `[tool.pytest.ini_options]`
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## Testing Patterns
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### Async Testing
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All MCP tools are async functions, so tests use:
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```python
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@pytest.mark.asyncio
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async def test_function_name():
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# Test async MCP tools
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```
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### Mock API Responses
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HTTP requests are mocked using `pytest-mock`:
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```python
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def test_api_call(mocker):
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mock_response = mocker.Mock()
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mock_response.json.return_value = {"test": "data"}
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mock_response.raise_for_status.return_value = None
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mocker.patch("httpx.AsyncClient.get", return_value=mock_response)
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```
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### Test Data Management
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- **Sample Data**: [tests/sample_data.py](mdc:tests/sample_data.py) contains realistic mock data
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- **Isolation**: Each test uses fresh mock data to avoid side effects
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- **Coverage**: Tests cover both success and error scenarios
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## Running Tests
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```bash
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# Run all tests
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pytest
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# Run with verbose output
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pytest -v
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# Run specific test file
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pytest tests/test_server.py
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# Run with coverage (if installed)
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pytest --cov=src/intervals_mcp_server
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```
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## Test Requirements
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Before committing code, ensure:
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1. **All tests pass**: `pytest` returns exit code 0
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2. **No new linting errors**: `ruff .` passes
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3. **Type checking passes**: `mypy src tests` succeeds
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## Writing New Tests
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When adding new MCP tools or utilities:
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1. **Create test cases** in appropriate test file
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2. **Mock external dependencies** (HTTP requests, file I/O)
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3. **Test both success and error paths**
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4. **Use realistic test data** from [tests/sample_data.py](mdc:tests/sample_data.py)
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5. **Follow async testing patterns** for MCP tools
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6. **Verify error message formatting** for user-facing errors
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## Mock Strategy
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The project uses comprehensive mocking to:
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- **Avoid real API calls** during testing
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- **Test error handling** by simulating various HTTP error responses
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- **Ensure deterministic results** with controlled test data
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- **Speed up test execution** by eliminating network requests
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