Daniel LightRAG MCP Server

# Test Suite Implementation Summary ## Task Completed: Create comprehensive test suite for all tools ### Overview Successfully implemented a comprehensive test suite for the Daniel LightRAG MCP Server covering all 22 MCP tools with 131 tests across multiple categories. ### Test Suite Structure ``` tests/ ├── __init__.py # Test package initialization ├── conftest.py # Pytest configuration and fixtures (17 fixtures) ├── test_server.py # MCP server unit tests (34 tests) ├── test_client.py # LightRAG client unit tests (43 tests) ├── test_models.py # Pydantic model validation tests (44 tests) ├── test_integration.py # Integration tests with mock server (10 tests) ├── test_runner.py # Test runner script └── README.md # Comprehensive test documentation ``` ### Test Coverage Achieved #### ✅ All 22 MCP Tools Tested: **Document Management Tools (8 tools):** - `insert_text` - Insert single text document - `insert_texts` - Insert multiple text documents - `upload_document` - Upload document file - `scan_documents` - Scan for new documents - `get_documents` - Retrieve all documents - `get_documents_paginated` - Retrieve documents with pagination - `delete_document` - Delete specific document - `clear_documents` - Clear all documents **Query Tools (2 tools):** - `query_text` - Query with text - `query_text_stream` - Stream query results **Knowledge Graph Tools (7 tools):** - `get_knowledge_graph` - Retrieve knowledge graph - `get_graph_labels` - Get graph labels - `check_entity_exists` - Check entity existence - `update_entity` - Update entity - `update_relation` - Update relation - `delete_entity` - Delete entity - `delete_relation` - Delete relation **System Management Tools (5 tools):** - `get_pipeline_status` - Get pipeline status - `get_track_status` - Get track status - `get_document_status_counts` - Get document status counts - `clear_cache` - Clear cache - `get_health` - Health check #### ✅ Test Categories Implemented: 1. **Unit Tests (test_server.py - 34 tests)** - Tool listing verification (all 22 tools registered) - Argument validation for all tools - Success response creation and formatting - Error response creation and formatting - Individual tool functionality testing - Error handling scenarios 2. **Client Tests (test_client.py - 43 tests)** - Client initialization and configuration - HTTP error mapping to custom exceptions - All 22 API method implementations - Request/response handling - Streaming functionality - Connection and timeout error handling - Context manager functionality 3. **Model Tests (test_models.py - 44 tests)** - Enum validation (DocStatus, QueryMode, PipelineStatus) - Common model validation (TextDocument, PaginationInfo) - Request model validation (all 22 request types) - Response model validation (all 22 response types) - Serialization/deserialization testing - Edge case and validation error testing 4. **Integration Tests (test_integration.py - 10 tests)** - Full workflow testing (document management, query, system) - Error scenario testing - Streaming integration testing - Concurrent operation testing - Tool listing completeness verification #### ✅ Test Quality Features: - **Mock HTTP Responses**: All API endpoints tested with realistic mock responses - **Input Validation**: All tools tested with valid and invalid inputs - **Error Handling**: All error types tested (connection, validation, API, timeout, server) - **Streaming Functionality**: Query streaming tested with async generators - **Pydantic Model Validation**: All request/response models thoroughly tested - **Integration Workflows**: Complete end-to-end workflows tested - **Concurrent Operations**: Multi-tool concurrent execution tested - **Edge Cases**: Boundary conditions and edge cases covered ### Key Implementation Details #### Test Fixtures (conftest.py): - `lightrag_client`: Mock LightRAG client for testing - `mock_response`: Factory for creating mock HTTP responses - `mock_streaming_response`: Factory for creating mock streaming responses - Sample data fixtures for all response types (insert, query, documents, graph, health, etc.) #### Mock Strategies: - **HTTP Client Mocking**: Uses `unittest.mock.AsyncMock` to mock httpx client - **Server Response Mocking**: Mock LightRAG server with realistic responses - **Streaming Mocking**: Mock async generators for streaming responses - **Error Injection**: Mock various error conditions for comprehensive error testing #### Test Runner Features: - Support for running specific test categories (unit, integration, models, server, client) - Verbose and quiet modes - Coverage reporting integration - Dependency installation automation - Cross-platform compatibility (macOS/Linux) ### Test Execution Results ```bash # All tests passing python3 tests/test_runner.py all Running command: python3 -m pytest -q tests/ --asyncio-mode=auto ................................................................................................................................... 131 passed in 1.14s ``` #### Performance Metrics: - **Total Tests**: 131 tests - **Execution Time**: ~1.14 seconds - **Test Categories**: 4 main categories - **Tools Covered**: 22/22 (100%) - **Error Scenarios**: Comprehensive coverage ### Requirements Validation ✅ **Write unit tests for all 22 MCP tools with mock HTTP responses** - All 22 tools have dedicated unit tests - Mock HTTP responses implemented for all API endpoints - Success and error scenarios covered ✅ **Test input validation using the Pydantic models** - All Pydantic models tested for validation - Valid and invalid input scenarios covered - Edge cases and boundary conditions tested ✅ **Test error handling scenarios for each tool** - Connection errors, validation errors, API errors tested - HTTP status code mapping verified - Custom exception hierarchy tested ✅ **Test streaming functionality for query tools** - Streaming query tools tested with async generators - Chunk collection and response formatting verified - Streaming error scenarios covered ✅ **Create integration tests with mock LightRAG server responses** - Full workflow integration tests implemented - Mock LightRAG server with realistic behavior - End-to-end scenarios tested ### Dependencies Added Updated `pyproject.toml` with test dependencies: ```toml [project.optional-dependencies] dev = [ "pytest>=7.0.0", "pytest-asyncio>=0.21.0", "pytest-cov>=4.0.0", "pytest-mock>=3.10.0", "black>=23.0.0", "isort>=5.12.0", "mypy>=1.0.0", ] ``` ### Configuration Files - `pytest.ini`: Pytest configuration with async support - `tests/README.md`: Comprehensive test documentation - `tests/test_runner.py`: Executable test runner script ### Code Quality Improvements During test implementation, also fixed: - Deprecated Pydantic `.dict()` method calls → `.model_dump()` - Improved error handling in client code - Enhanced mock response handling - Fixed async fixture configuration ### Continuous Integration Ready The test suite is designed for CI/CD environments: - No external dependencies (all mocked) - Parallel execution support - Clear exit codes - Comprehensive reporting - Fast execution (<2 seconds) ### Summary Successfully implemented a comprehensive test suite that: - ✅ Tests all 22 MCP tools with mock HTTP responses - ✅ Validates all Pydantic models with edge cases - ✅ Tests error handling scenarios comprehensively - ✅ Tests streaming functionality with async generators - ✅ Provides integration tests with mock server responses - ✅ Achieves 131 tests with 100% pass rate - ✅ Executes in ~1.14 seconds - ✅ Provides excellent documentation and tooling The test suite ensures the reliability, maintainability, and correctness of the Daniel LightRAG MCP Server implementation.