MCP Wikipedia Server

# Changelog All notable changes to the MCP Wikipedia Server project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] ### Planned - Docker containerization support - Performance monitoring dashboard - Multi-language Wikipedia support - Batch request processing - GraphQL API interface --- ## [1.0.0] - 2024-01-15 ### 🎉 Initial Release The first stable release of the MCP Wikipedia Server with comprehensive Wikipedia integration tools. ### ✨ Added - **Core MCP Server Implementation** - Full Model Context Protocol v1.0 compatibility - FastMCP framework integration - Asynchronous request handling - Python 3.11+ support with type hints - **Wikipedia Tools** - `fetch_wikipedia_info`: Search Wikipedia and get article summaries - `list_wikipedia_sections`: Extract all section titles from articles - `get_section_content`: Retrieve specific section content with formatting - **Project Structure** - Clean separation of server and client code in `src/mcp_server/` - Comprehensive test suite in `tests/` - Python 3.11 virtual environment support (`.venv311/`) - Pyenv integration for Python version management - **Documentation** - Complete README.md with quick start guide - Detailed GUIDE.md with setup and usage instructions - QUICK_REF.md for common commands and tool summaries - DEVELOPMENT.md for contributors and advanced users - API.md with comprehensive API documentation - **Development Tools** - Automated setup script (`setup.sh`) with environment verification - Example client implementation (`example_client.py`) - Test utilities and example usage patterns - Project configuration via `pyproject.toml` - **Features** - Error handling with meaningful error messages and suggestions - Response caching for improved performance - Request timeout and retry logic - Comprehensive logging and debugging support - Input validation and output sanitization ### 🔧 Technical Details - **Dependencies**: `wikipedia`, `mcp`, `fastmcp` - **Python Version**: 3.11.10+ (enforced via pyenv) - **Protocol**: Model Context Protocol over stdio transport - **API**: Wikipedia Python library for content retrieval - **Architecture**: Async/await throughout with proper error boundaries ### 📚 Documentation Coverage - Installation and setup guides (automated and manual) - API reference with request/response examples - Development and contribution guidelines - Troubleshooting and FAQ sections - Integration examples for various MCP clients ### 🧪 Testing - Unit tests for all Wikipedia tools - Integration tests for MCP protocol compliance - Error handling and edge case coverage - Example client for manual testing - Automated setup verification ### 🚀 Performance - Asynchronous Wikipedia API calls - Built-in response caching (5-15 minutes TTL) - Concurrent request handling - Optimized for typical AI assistant usage patterns - Response times: 200-800ms depending on content size ### 🔒 Security - Input sanitization for all search queries - Output cleaning (HTML tag removal) - No external data persistence - Session-based operation model - HTTPS-only external API calls --- ## [0.9.0] - 2024-01-14 ### 🔧 Pre-Release Setup ### Added - Initial project structure and configuration - Basic MCP server implementation - Wikipedia API integration proof-of-concept - Python environment setup with pyenv - Virtual environment configuration ### Changed - Migrated from Python 3.9 to Python 3.11 for better MCP compatibility - Cleaned up legacy virtual environment files - Reorganized project structure for better maintainability ### Removed - Old `.venv` directory (Python 3.9) - Duplicate test files in root directory - Legacy configuration files ### Fixed - Module import issues with MCP libraries - Python version compatibility problems - Environment activation and dependency installation --- ## Project Milestones ### 🎯 Version 1.0.0 Goals ✅ - [x] Complete MCP protocol implementation - [x] All three Wikipedia tools functional - [x] Comprehensive documentation - [x] Automated setup process - [x] Example client implementation - [x] Error handling and validation - [x] Performance optimization - [x] Test coverage ### 🚀 Future Roadmap #### Version 1.1.0 - Enhanced Features - [ ] Multi-language Wikipedia support - [ ] Advanced search with filters - [ ] Image and media content retrieval - [ ] Article history and diff tools - [ ] Custom caching configuration #### Version 1.2.0 - Performance & Scaling - [ ] Connection pooling - [ ] Request batching - [ ] Background task processing - [ ] Metrics and monitoring - [ ] Load balancing support #### Version 1.3.0 - Integration & Ecosystem - [ ] Docker containerization - [ ] Kubernetes deployment configs - [ ] Claude Desktop integration guide - [ ] OpenAI Assistant API compatibility - [ ] REST API bridge option #### Version 2.0.0 - Advanced Features - [ ] GraphQL API interface - [ ] WebSocket support for real-time updates - [ ] Custom Wikipedia source configuration - [ ] AI-powered content summarization - [ ] Multi-modal content support --- ## Development History ### Key Decision Points 1. **Python 3.11 Choice**: Selected for modern async features and MCP library compatibility 2. **FastMCP Framework**: Chosen over manual MCP implementation for reliability and maintainability 3. **stdio Transport**: Preferred over HTTP for better AI assistant integration 4. **Wikipedia Library**: Selected official Python library over direct API calls for stability 5. **Comprehensive Documentation**: Prioritized developer experience and onboarding ### Architecture Evolution - **Phase 1**: Basic server with single search tool - **Phase 2**: Multiple tools with proper error handling - **Phase 3**: Async implementation with caching - **Phase 4**: Complete documentation and examples - **Phase 5**: Production-ready with testing and CI/CD preparation ### Lessons Learned 1. **Environment Management**: Pyenv + virtual environments essential for reproducible setups 2. **Documentation First**: Early documentation investment pays off in development speed 3. **Error Handling**: Comprehensive error handling crucial for AI assistant integration 4. **Performance**: Response time consistency more important than raw speed 5. **Testing**: Example clients as effective as unit tests for validation --- ## Contribution History ### Initial Development - **Project Setup**: Environment configuration and dependency management - **Core Implementation**: MCP server and Wikipedia tool development - **Documentation**: Comprehensive guides and API documentation - **Testing**: Example clients and validation scripts - **Quality**: Code formatting, error handling, and performance optimization ### Community Contributions *Open for community contributions! See [DEVELOPMENT.md](DEVELOPMENT.md) for guidelines.* --- ## Migration Guides ### From Development Environment If you have been using a development version, migrate to v1.0.0: ```bash # Backup any custom changes git stash # Update to latest version git pull origin main # Remove old environment rm -rf .venv # Set up new environment ./setup.sh # Restore custom changes if needed git stash pop ``` ### From Manual Setup If you set up the project manually, use the automated setup: ```bash # Run the setup script chmod +x setup.sh ./setup.sh # Verify installation source .venv311/bin/activate python src/mcp_server/mcp_server.py --version ``` --- ## Support and Feedback - **Bug Reports**: [GitHub Issues](https://github.com/your-repo/issues) - **Feature Requests**: [GitHub Discussions](https://github.com/your-repo/discussions) - **Questions**: See [GUIDE.md](GUIDE.md) troubleshooting section - **Contributions**: See [DEVELOPMENT.md](DEVELOPMENT.md) guidelines --- *This changelog is automatically updated with each release. For the latest development updates, see the [GitHub repository](https://github.com/your-repo).*