The Taiga MCP Bridge server enables AI tools to manage Taiga project management resources programmatically through the Model Context Protocol (MCP). It provides:
Authentication: Login, logout, and session status management
Project Management: Create, list, update, and delete projects and settings
User Stories/Tasks/Issues/Epics: Create, get, update, delete, assign, and track status
Milestone Management: Handle sprints with statistics tracking
User Administration: List members, manage roles, and send invitations
Wiki Access: List and retrieve wiki pages
Status Management: Access available statuses for stories, tasks, issues, and epics
Resource Details: Query detailed information by ID or reference
Allows configuration through environment variables or a .env file for settings like API URLs, session expiry, and logging options
Used for configuration settings and validation of data structures within the bridge
Supports comprehensive testing capabilities including unit tests, integration tests, and test coverage reporting
Provides a Python-based integration layer for connecting to Taiga's resources and APIs
Taiga MCP Bridge
Overview
The Taiga MCP Bridge is a powerful integration layer that connects Taiga project management platform with the Model Context Protocol (MCP), enabling AI tools and workflows to interact seamlessly with Taiga's resources.
This bridge provides a comprehensive set of tools and resources for AI agents to:
Create and manage projects, epics, user stories, tasks, and issues in Taiga
Track sprints and milestones
Assign and update work items
Query detailed information about project artifacts
Manage project members and permissions
By using the MCP standard, this bridge allows AI systems to maintain contextual awareness about project state and perform complex project management tasks programmatically.
Features
Comprehensive Resource Support
The bridge supports the following Taiga resources with complete CRUD operations:
Projects: Create, update, and manage project settings and metadata
Epics: Manage large features that span multiple sprints
User Stories: Handle detailed requirements and acceptance criteria
Tasks: Track smaller units of work within user stories
Issues: Manage bugs, questions, and enhancement requests
Sprints (Milestones): Plan and track work in time-boxed intervals
Installation
This project uses uv for fast, reliable Python package management.
Prerequisites
Python 3.10 or higher
uv package manager
Basic Installation
Development Installation
For development (includes testing and code quality tools):
Manual Installation
If you prefer to install manually:
Configuration
The bridge can be configured through environment variables or a .env
file:
Environment Variable | Description | Default |
| Base URL for the Taiga API | |
| Session expiration time in seconds | 28800 (8 hours) |
| Transport mode (stdio or sse) | stdio |
| API request timeout in seconds | 30 |
| Maximum number of HTTP connections | 10 |
| Max keepalive connections | 5 |
| Max requests per minute | 100 |
| Logging level | INFO |
| Path to log file | taiga_mcp.log |
Create a .env
file in the project root to set these values:
Usage
With stdio mode
Paste the following json in your Claude App's or Cursor's mcp settings section:
Running the Bridge
Start the MCP server with:
Or manually:
Transport Modes
The server supports two transport modes:
stdio (Standard Input/Output) - Default mode for terminal-based clients
SSE (Server-Sent Events) - Web-based transport with server push capabilities
You can set the transport mode in several ways:
Using the
--sse
flag with run.sh or server.py (default is stdio)Setting the
TAIGA_TRANSPORT
environment variableAdding
TAIGA_TRANSPORT=sse
to your.env
file
Authentication Flow
This MCP bridge uses a session-based authentication model:
Login: Clients must first authenticate using the
login
tool:session = client.call_tool("login", { "username": "your_taiga_username", "password": "your_taiga_password", "host": "https://api.taiga.io" # Optional }) # Save the session_id from the response session_id = session["session_id"]Using Tools and Resources: Include the
session_id
in every API call:# For resources, include session_id in the URI projects = client.get_resource(f"taiga://projects?session_id={session_id}") # For project-specific resources epics = client.get_resource(f"taiga://projects/123/epics?session_id={session_id}") # For tools, include session_id as a parameter new_project = client.call_tool("create_project", { "session_id": session_id, "name": "New Project", "description": "Description" })Check Session Status: You can check if your session is still valid:
status = client.call_tool("session_status", {"session_id": session_id}) # Returns information about session validity and remaining timeLogout: When finished, you can logout to terminate the session:
client.call_tool("logout", {"session_id": session_id})
Example: Complete Project Creation Workflow
Here's a complete example of creating a project with epics and user stories:
Development
Project Structure
Testing
Run tests with pytest:
Debugging and Inspection
Use the included inspector tool for debugging:
Error Handling
All API operations return standardized error responses in the following format:
Performance Considerations
The bridge implements several performance optimizations:
Connection Pooling: Reuses HTTP connections for better performance
Rate Limiting: Prevents overloading the Taiga API
Retry Mechanism: Automatically retries failed requests with exponential backoff
Session Cleanup: Regularly cleans up expired sessions to free resources
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Fork the repository
Create your feature branch (
git checkout -b feature/amazing-feature
)Install development dependencies (
./install.sh --dev
)Make your changes
Run tests (
pytest
)Commit your changes (
git commit -m 'Add some amazing feature'
)Push to the branch (
git push origin feature/amazing-feature
)Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
Taiga for their excellent project management platform
Model Context Protocol (MCP) for the standardized AI communication framework
All contributors who have helped shape this project
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.
Tools
A protocol bridge that connects AI systems to Taiga project management platform, enabling AI tools to create and manage projects, epics, user stories, tasks, issues, and sprints.
- Overview
- Features
- Installation
- Configuration
- Usage
- Development
- Error Handling
- Performance Considerations
- Contributing
- License
- Acknowledgments
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