adx-mcp-server

# Azure Data Explorer MCP Server Testing Guide This document provides comprehensive information about testing the ADX MCP Server. ## Testing Philosophy The ADX MCP Server follows these testing principles: 1. **Comprehensive Coverage**: Tests should cover both success paths and error paths 2. **Isolation**: Tests should be isolated from external dependencies (like Azure services) 3. **Realistic Data**: Mock data should resemble real Azure Data Explorer responses 4. **Maintainability**: Tests should be easy to understand and maintain ## Test Structure The test suite is organized into the following categories: ### Unit Tests - `test_config.py`: Tests for configuration handling and validation - `test_server.py`: Tests for the core server functionality and tools - `test_main.py`: Tests for the application entry point and setup process - `test_error_handling.py`: Tests for various error conditions - `test_result_formatting.py`: Tests for ADX result formatting functions - `test_mock_data.py`: Tests with realistic mock data structures ### Integration Tests - `integration/test_mcp_integration.py`: Tests that verify the complete flow of MCP tool execution ## Setting Up for Testing ### Installing Development Dependencies ```bash # Install all dependencies including test dependencies uv pip install -e ".[dev]" ``` ### Running Tests ```bash # Run all tests pytest # Run with coverage report pytest --cov=src --cov-report=term-missing # Run a specific test file pytest tests/test_server.py # Run a specific test pytest tests/test_server.py::TestServerTools::test_execute_query ``` ## Mock Fixtures The test suite includes several useful fixtures: - `mock_env_variables`: Sets up a complete environment with mock Azure credentials - `mock_missing_env_variables`: Clears environment variables to test error handling - `mock_kusto_client`: Provides a simple mock of the KustoClient - `mock_kusto_integration`: Provides a comprehensive mock for integration tests - `mock_kusto_real_data`: Provides realistic mock data structures ## Adding New Tests When adding new features to the server, follow these guidelines: 1. **Add Unit Tests First**: Write tests for the new functionality before implementing it 2. **Test Edge Cases**: Include tests for error conditions and edge cases 3. **Use Existing Fixtures**: Leverage the existing fixtures for consistency 4. **Update Integration Tests**: Make sure the integration tests cover the new functionality 5. **Check Coverage**: Ensure that your tests provide adequate coverage ## Continuous Integration The project includes GitHub Actions workflow configuration to automatically run tests on push and pull requests. The workflow: 1. Runs on multiple Python versions (3.10, 3.11, 3.12) 2. Installs dependencies 3. Runs the test suite 4. Uploads coverage reports to Codecov ## Best Practices - Keep tests focused on a single functionality - Use descriptive test names that indicate what's being tested - Avoid test dependencies (tests should be able to run in any order) - Keep mock data realistic but minimal - Comment complex test logic for clarity