PyGithub MCP Server

# Project Progress ## What Works ### Core Functionality - Complete Python implementation of GitHub MCP Server - Successful MCP server connection and tool operations - FastMCP integration with GitHub API - Error handling and validation - Docker containerization - PyGithub integration with object-oriented GitHub API interactions - Centralized GitHub client management - Robust pagination handling - Proper parameter handling for PyGithub methods ### Architecture Implementation - Modular Tool Architecture (ADR-006): - Configuration system in `config/` package - Decorator-based tool registration in `tools/` package - Tools organized by domain (issues, repositories, etc.) - Selective enabling/disabling of tool groups - Factory pattern in server.py with `create_server()` - Schema Reorganization (ADR-003): - Dedicated `schemas` directory with domain-specific files - Schemas organized by domain (base, repositories, issues, etc.) - Improved maintainability and discoverability - Schema Validation (ADR-004): - Field validators to prevent empty strings in critical fields - Validation for owner, repo, path, title, body, and label fields - Enhanced error messages for validation failures - Aligned with PyGithub expectations - Common Module Reorganization (ADR-005): - Domain-specific directories for converters - Dedicated modules for error handling, client management, and utilities - Backward compatibility through re-exports - Standardized on PyGithub for API interactions - Pydantic-First Architecture (ADR-007): - Operations layer accepts Pydantic models directly - No parameter unpacking between layers - Validation logic lives in Pydantic models - Consistent error handling across layers ### GitHub Tools Implementation - Complete set of GitHub issue tools: - create_issue: Create new issues - get_issue: Get issue details - update_issue: Modify existing issues - list_issues: List repository issues - add_issue_comment: Add comments - list_issue_comments: List comments - update_issue_comment: Edit comments - delete_issue_comment: Remove comments - add_issue_labels: Add labels - remove_issue_label: Remove labels - Complete set of GitHub repository tools: - get_repository: Get repository details - create_repository: Create new repositories - fork_repository: Fork existing repositories - search_repositories: Search for repositories - get_file_contents: Get file or directory contents - create_or_update_file: Create or update files - push_files: Push multiple files in one commit - create_branch: Create new branches - list_commits: List repository commits ### Testing Improvements - Advanced test infrastructure: - Developed modular coverage analysis package in `scripts/coverage/`: - Dedicated modules for test running, parsing, reporting, and data modeling - Interactive HTML reports with test failure display - JSON reporting for CI/CD integration - Priority-based module categorization - Module-specific test execution support - Package structure with proper imports via `__main__.py` - Created `scripts/generate_tool_tests.py` for generating standardized tests - Added test templates that follow ADR-002 principles with dataclasses instead of mocks - Documented clear workflow for viewing and using coverage reports - Added comprehensive coverage analysis guide to test_improvement_plan.md - Testing infrastructure following ADR-002: - Integration test directory structure with application-layer organization - Tests organized by module layer (operations, tools, etc.) - Test fixtures with retry mechanism for rate limits - Environment configuration for real API testing - Test documentation with patterns and best practices - Standardized integration test infrastructure: - Created robust test_cleanup fixture for resource tracking - Implemented standardized with_retry mechanism for API rate limits - Established consistent fixture naming (test_owner, test_repo_name) - Fixed skipped repository tests by standardizing environment handling - Created comprehensive README.md for integration tests - Test coverage enhancements: - Improved converter tests using realistic data structures - Added integration tests for client module and error handlers - Enhanced error handling tests - Fixed datetime handling and timezone-aware operations - Standardized error handling across operations ### Documentation - Created comprehensive documentation guides: - Updated scripts/README.md with clear guidance on viewing reports - Enhanced test_improvement_plan.md with coverage analysis workflow ## What's Left to Build ### Test Coverage Improvements - [x] Fixed TestGitHubClient warning in unit tests by using underscore prefix - [x] Improved converters/common/datetime.py from 54% to 95%+ coverage - [x] Created comprehensive test infrastructure for systematic coverage improvements - [ ] Fix coverage analysis tool accuracy issue (currently reporting 28% instead of ~90%) - [ ] Continue improving coverage for remaining modules: - [ ] client/client.py (currently 87%) - [ ] tools/repositories/tools.py (currently 63%) - [ ] operations/repositories.py (currently 77%) - [ ] operations/issues.py (currently 91%) - [ ] utils/environment.py (currently 83%) - [ ] tools/__init__.py (currently 80%) ### Schema Validation Expansion - [ ] Review all schema models for validation opportunities - [ ] Add field validators for remaining critical string fields - [ ] Implement enum validation for state, sort, and direction fields - [ ] Add range validation for numeric fields - [ ] Ensure consistent validation patterns across all schemas ### Real API Testing Implementation - [ ] Infrastructure refinement for real API testing - [ ] Replace all mock-based tests with real API tests - [ ] Implement thorough cleanup mechanisms - [ ] Develop helper functions for common test operations - [ ] Identify edge cases that still require mocking - [ ] Implement caching strategies for improved test performance ### PyPI Publication - [ ] Verify package name availability - [ ] Prepare package for PyPI - [ ] Document installation process - [ ] Add badges to README.md - [ ] Create release workflow ### Documentation - [ ] API reference docs - [ ] Advanced usage examples - [ ] Best practices guide - [ ] Troubleshooting guide ### Performance Optimizations - [ ] Response caching - [ ] Rate limit tracking - [ ] Cache invalidation - [ ] Memory management - [ ] Request batching - [ ] Concurrent operations - [ ] Connection pooling ### Feature Enhancements - [x] Repository tools group (implemented in v0.5.15) - [ ] Additional tool groups (pull_requests, discussions, users, etc.) - [ ] GraphQL support - [ ] Webhook support - [ ] Real-time updates ## Current Status Core implementation is operational with the new modular architecture. All GitHub issue and repository operations have been implemented as MCP tools with proper parameter handling, error management, and logging. We've successfully implemented: - ADR-002: Real API Testing Strategy - ADR-003: Schema Reorganization - ADR-004: Enhanced Schema Validation - ADR-005: Common Module Reorganization - ADR-006: Modular Tool Architecture - ADR-007: Pydantic-First Architecture All unit and integration tests now pass with no warnings. The TestGitHubClient warning has been resolved and test coverage for the datetime conversion module has been significantly improved. We've refactored the analyze_coverage.py script into a proper Python package with modular components for better maintainability and extensibility. The coverage analysis tool now has a cleaner architecture with separate modules for running tests, parsing coverage data, generating reports, and modeling coverage information. However, there appears to be an issue with the coverage reporting accuracy, showing around 28% when the actual coverage is expected to be closer to 90%. ### Priorities 1. Fix the inaccurate coverage reporting in the coverage analysis tool 2. Implement additional tool groups (repositories, pull_requests, etc.) 3. Enhance testing coverage for the new architecture 4. Create documentation for adding new tool groups 5. Optimize performance for tool loading 6. Continue improving test coverage for low-coverage modules 7. Prepare for PyPI publication ## Known Issues 1. Coverage analysis tool reports inaccurate coverage percentage (~28% vs expected ~90%) 2. Some modules still have low test coverage 3. Documentation could be more comprehensive 4. Performance could be optimized 5. Need to document synchronous operation benefits 6. Need to update API examples for synchronous usage ## Next Actions 1. Debug and fix coverage analysis tool accuracy: - Investigate how coverage data is being calculated - Check configuration in pyproject.toml - Add debug output to see coverage command execution - Ensure proper parsing of coverage output - Fix module filtering in coverage calculation 2. Continue implementing test improvement plan (see docs/test_improvement_plan.md): - ✅ Fixed TestGitHubClient warning in unit tests - ✅ Improved coverage for converters/common/datetime.py (from 54% to 95%+) - ✅ Created test infrastructure for systematic coverage improvements - ✅ Enhanced test coverage workflow with clear documentation - ✅ Refactored analyze_coverage.py into a proper modular package - Improve coverage for tools/repositories/tools.py (currently 63%) - Enhance repositories.py coverage (currently 77%) 3. Use coverage analyzer to prioritize test development 4. Generate tests for highest-priority modules using the test generator 5. Implement more integration tests with real API testing 6. Add performance optimizations 7. Enhance documentation 8. Prepare for PyPI publication ## Dependencies - Git repository at github.com/AstroMined/pygithub-mcp-server - Python 3.10+ - MCP Python SDK - Pydantic - PyGithub - pytest - UV package manager