# Changelog

All notable changes to the Task Manager MCP Servers 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).

## [1.0.0] - 2024-02-10

### Added

#### Task Management MCP Server
- **Task Analysis**: Convert natural language into structured tasks with priorities, time estimates, and risk assessments
- **Smart Scheduling**: Generate optimal task ordering and completion timelines based on dependencies and priorities
- **Risk Prediction**: Identify potential risks and provide actionable mitigation strategies
- **Automated Reminders**: Set up deadline and progress check notifications
- **Progress Tracking**: Dynamic progress updates with automatic estimate adjustments
- **Task Review**: Comprehensive retrospective analysis for continuous improvement
- **Resources**: Access detailed task and project information through MCP resources
- **Prompts**: Intelligent prompts for task review and risk analysis

#### Task Assignment MCP Server
- **Task Decomposition**: Break complex tasks into manageable subtasks using AI
- **Capability Matching**: Match tasks to resources based on skills and availability
- **Smart Assignment**: Intelligent resource allocation considering workload and expertise
- **Team Collaboration**: Coordinate multi-person tasks with detailed collaboration plans
- **Daily Report Review**: Automated scoring and feedback for team performance
- **Workload Management**: Track and balance team capacity across projects
- **Resources**: Access resource details and team workload summaries
- **Prompts**: Analysis prompts for assignment optimization and team performance

#### Shared Components
- **Data Models**: Comprehensive Pydantic models for tasks, resources, and assessments
- **AI Agents**: Reusable agents for requirements analysis, task generation, and decomposition
- **Utilities**: Common utilities and helper functions

#### Testing & Quality
- **Unit Tests**: Comprehensive test coverage for both MCP servers
- **Integration Tests**: End-to-end workflow testing
- **E2E Tests**: MCP protocol compliance and real-world scenario testing
- **Code Quality**: Black, isort, flake8, and mypy integration
- **Coverage Reports**: Detailed test coverage reporting

#### Documentation
- **API Reference**: Complete API documentation for all tools, resources, and prompts
- **User Guide**: Comprehensive guide for getting started and advanced usage
- **Examples**: Demo scripts and configuration examples
- **Development Guide**: Setup instructions for contributors

#### Development Tools
- **Build Scripts**: Automated build and publish workflow
- **Test Runner**: Comprehensive test execution with quality checks
- **Demo Script**: Interactive demonstration of key features
- **Configuration Examples**: Claude Desktop integration examples

### Technical Details

#### Dependencies
- **Core**: mcp>=1.0.0, pydantic>=2.0.0, requests>=2.28.0, python-dateutil>=2.8.0
- **Development**: pytest, black, isort, flake8, mypy, coverage tools
- **Documentation**: mkdocs, mkdocs-material

#### Compatibility
- **Python**: 3.9, 3.10, 3.11, 3.12
- **Platforms**: Windows, macOS, Linux
- **MCP Protocol**: Compatible with Claude Desktop and other MCP clients

#### Performance
- **Fallback Support**: Graceful degradation when AI services are unavailable
- **Efficient Processing**: Optimized algorithms for task analysis and assignment
- **Resource Management**: Smart caching and resource utilization

### Security
- **API Key Management**: Secure handling of OpenAI API credentials
- **Input Validation**: Comprehensive validation of all inputs
- **Error Handling**: Robust error handling with informative messages

### Known Limitations
- AI features require OpenAI API access
- Some advanced features may have rate limits based on API usage
- Initial release focuses on core functionality; advanced integrations planned for future versions

## [Unreleased]

### Planned Features
- **GitHub Integration**: Sync with GitHub Issues and Projects
- **Calendar Integration**: Schedule task work in calendar applications
- **Time Tracking**: Detailed time tracking and reporting
- **Advanced Analytics**: Project performance analytics and insights
- **Custom Workflows**: User-defined workflow templates
- **Notification System**: Multi-channel notification support
- **Mobile Support**: Mobile-friendly interfaces and notifications
- **Enterprise Features**: Advanced security, audit logs, and compliance features

### Improvements Under Consideration
- **Performance Optimization**: Faster task analysis and assignment algorithms
- **Enhanced AI Models**: Support for additional AI providers and models
- **Better Error Recovery**: More robust error handling and recovery mechanisms
- **Internationalization**: Multi-language support
- **Accessibility**: Enhanced accessibility features
- **Plugin System**: Extensible plugin architecture

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## Release Notes

### Version 1.0.0 - Initial Release

This is the first stable release of Task Manager MCP Servers, providing a complete solution for intelligent task management and assignment through the Model Context Protocol.

**Key Highlights:**
- Two powerful MCP servers with complementary functionality
- AI-powered task analysis and intelligent assignment recommendations
- Comprehensive testing and documentation
- Ready for production use with Claude Desktop
- Extensible architecture for future enhancements

**Getting Started:**
1. Install: `pip install task-manager-mcp`
2. Configure Claude Desktop with the provided examples
3. Start managing tasks with AI assistance

**Community:**
- GitHub: https://github.com/task-manager-mcp/task-manager-mcp
- Documentation: https://task-manager-mcp.readthedocs.io/
- Issues: https://github.com/task-manager-mcp/task-manager-mcp/issues

We welcome feedback, contributions, and feature requests from the community!