Taiga MCP Server
A production-ready Model Context Protocol (MCP) server for Taiga Project Management
Getting Started • Features • Tools • Architecture • Contributing
📋 Overview
Taiga MCP Server enables seamless integration between Large Language Models (LLMs) and Taiga project management platform through the Model Context Protocol. Built with Python's async/await patterns and type-safe Pydantic models, it provides a robust, production-ready solution for AI-powered project management automation.
Why Taiga MCP?
- 🤖 Natural Language Interface: Interact with Taiga using conversational commands 
- 🔄 Async-First: Built on modern async/await for high performance 
- 🛡️ Type-Safe: Full Pydantic validation for reliability 
- 🎯 Production Ready: Comprehensive error handling and logging 
- 🔌 Extensible: Clean architecture for easy feature additions 
- 📦 Zero Config: Works out-of-the-box with Claude Desktop, Cursor, Windsurf 
✨ Features
Core Capabilities
| Feature | Description | 
| 🔐 Authentication | Token-based auth with automatic refresh | 
| 📊 Project Management | List, view, and search projects by ID or slug | 
| 📝 User Stories | Full CRUD operations with pagination support | 
| ✅ Task Management | Create and organize tasks within stories | 
| 👥 Team Collaboration | View members and assign work | 
| 🏷️ Rich Metadata | Tags, story points, due dates, custom fields | 
| 🔍 Flexible Queries | Support for IDs, slugs, and reference numbers (#42) | 
Technical Features
- Async Architecture: Non-blocking I/O for optimal performance 
- Smart Caching: Token management with auto-refresh 
- Intelligent Pagination: Auto-fetch all or page-by-page 
- Optimistic Locking: Version-based updates prevent conflicts 
- Role-Based Points: Automatic detection and handling 
- Flexible Identifiers: Use IDs, slugs, or #ref numbers interchangeably 
🚀 Quick Start
Prerequisites
- Python: 3.10 or higher 
- Taiga Account: taiga.io or self-hosted instance 
- MCP Client: Claude Desktop, Cursor, Windsurf, or any MCP-compatible client 
Installation
Configuration
Create .env file:
See
🛠️ Available Tools
The server exposes 10 tools through the MCP protocol:
Authentication
| Tool | Description | Parameters | 
| 
 | Authenticate with Taiga API | 
 (optional), 
 (optional) | 
Project Management
| Tool | Description | Parameters | 
| 
 | List all accessible projects | None | 
| 
 | Get project details | 
 (ID or slug) | 
| 
 | List project team members | 
 | 
User Story Management
| Tool | Description | Parameters | 
| 
 | Create a new user story | 
 , 
 , 
 , , 
 * | 
| 
 | List stories with pagination | 
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 , , 
 * | 
| 
 | Get story details | 
 , 
 * | 
| 
 | Update existing story | 
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 , , 
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 , , 
 , | 
Task Management
| Tool | Description | Parameters | 
| 
 | Create task in story | 
 , 
 , 
 , 
 , , 
 * | 
| 
 | List tasks for a story | 
 , 
 * | 
* = optional parameter
💬 Example Usage
Once configured with your LLM client, use natural language:
🏗️ Architecture
Tech Stack
| Component | Technology | Purpose | 
| Protocol | LLM-tool communication | |
| Language | Python 3.10+ | Core implementation | 
| HTTP Client | Async Taiga API calls | |
| Validation | Type-safe data models | |
| Config | Environment management | |
| Testing | + | Test framework | 
Project Structure
Design Patterns
1. Async/Await Throughout
All I/O operations use Python's async/await for non-blocking execution:
2. Service Layer Pattern
Business logic is encapsulated in service classes:
3. Pydantic Validation
All data is validated using Pydantic models:
4. Error Handling
Custom exception hierarchy for precise error handling:
🔧 Development
Setup Development Environment
Running Tests
Code Quality Tools
| Tool | Purpose | Command | 
| Black | Code formatting | 
 | 
| Ruff | Fast linting | 
 | 
| Mypy | Type checking | 
 | 
| Pytest | Testing | 
 | 
🗺️ Roadmap
Phase 1: Core Features ✅
- Authentication & token management 
- Project listing and details 
- User story CRUD operations 
- Task management 
- Team member listing 
- Smart pagination 
- Flexible identifiers (ID/slug/#ref) 
Phase 2: Enhanced Features 🚧
- Caching layer (Redis/in-memory) 
- Rate limiting 
- Bulk operations 
- Epic support 
- Sprint/Milestone management 
- Issues/Bugs tracking 
- Wiki page integration 
- File attachments 
- Comments on stories/tasks 
- Custom field support 
- Activity history tracking 
Phase 3: Advanced Features 🎯
- Standalone CLI tool 
- Analytics & reporting 
- Data export/import 
- Webhook support 
- Notification integrations (Slack, Email) 
- Project templates 
- Burndown charts 
- Time tracking 
🤝 Contributing
Contributions are welcome! Here's how to get started:
- Fork the repository 
- Create a feature branch: - git checkout -b feature/amazing-feature
- Make your changes 
- Add tests: Ensure coverage for new code 
- Run quality checks: black app/ tests/ ruff check app/ tests/ mypy app/ pytest
- Commit your changes: - git commit -m 'Add amazing feature'
- Push to branch: - git push origin feature/amazing-feature
- Open a Pull Request 
Development Guidelines
- Follow existing code style (Black formatting) 
- Add type hints to all functions 
- Write docstrings for public APIs 
- Include tests for new features 
- Update documentation as needed 
📝 License
This project is licensed under the The GNU General Public License v3.0 - see the LICENSE file for details.
🙏 Acknowledgments
- Model Context Protocol - For the excellent LLM-tool integration standard 
- Taiga - For the powerful open-source project management platform 
- Anthropic - For Claude and MCP SDK 
- Community Contributors - For feedback and improvements 
📞 Support
- Documentation: RUN.md for setup guides 
- Issues: GitHub Issues 
- Discussions: GitHub Discussions 
- Taiga API Docs: https://docs.taiga.io/api.html 
Built with ❤️ for the AI-powered project management community
⭐ Star this repo if you find it useful!
⚠️ Disclaimer
This project is not officially affiliated with Taiga. It's a community-driven MCP server implementation for integrating Taiga with LLM applications.
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Enables seamless integration between Large Language Models and Taiga project management platform, allowing users to manage projects, user stories, tasks, and team collaboration through natural language commands.