taiga-cli 1.0.2

Taiga CLI

A powerful command-line interface for Taiga project management, built with .NET 10 and Cocona.

Features

• 🔐 Authentication: Login with Taiga.io or self-hosted instances
• 📁 Projects: List, view details, stats, memberships, and roles
• 📝 User Stories: Manage user stories, view history, comments, and attachments
• ✅ Tasks: Track tasks, update status, and view history
• 🐛 Issues: Manage issues with severity, priority, and type tracking
• 🚀 Epics: Handle high-level features and related user stories
• 📅 Milestones: Track sprint progress and burndown charts
• 📚 Wiki: Access project documentation and history
• 🔍 Search: Full-text search across projects or within specific projects
• 🔔 Notifications: Check unread notifications and manage them
• 📊 Timeline: View project and user activity timelines

Installation

Prerequisites

• .NET 10.0 SDK or later

Global install - Preferred method

Use the command:

dotnet tool install --global taiga-cli

Run from DotNet DNX

Use the command:

dnx taiga-cli

Usage

Authentication

Login to Taiga.io (default):

taiga auth login -u <username> -p <password>

Login to a self-hosted instance:

taiga auth login -u <username> -p <password> -a https://taiga.mycompany.com

Logout:

taiga auth logout

Projects

List all projects:

taiga project list

Get project details:

taiga project get <project-id>

User Stories

List user stories for a specific project:

taiga userstory list -p <project-id>

Get user story details:

taiga userstory get <story-id>

Tasks & Issues

List tasks:

taiga task list -p <project-id>

List issues:

taiga issue list -p <project-id>

Help

View all available commands:

taiga --help

View help for a specific command:

taiga userstory --help

Configuration

The CLI stores configuration (including the authentication token) in ~/.taiga-cli/config.json.

AI Integration: MCP Server Replacement

The Taiga CLI can be used as a replacement for an MCP (Model Context Protocol) server when integrating with AI assistants and LLMs. Instead of setting up a dedicated MCP server, AI systems can execute CLI commands directly and parse their output.

For AI Developers

If you're building an AI assistant that needs to interact with Taiga, you can use this CLI tool by:

1. Executing Commands: Have your AI system execute taiga commands programmatically
2. Parsing Output: Parse the human-readable output from commands to extract structured data
3. Chaining Operations: Chain multiple commands together to perform complex workflows

Instructions for AI LLMs

We provide detailed instructions for AI LLMs in instructions-to-ai.md. This file contains:

• Complete command reference
• Usage patterns and workflows
• Best practices for AI integration
• Example workflows for common tasks
• Error handling guidelines

You can include this file in your AI system's context or use it as a reference when configuring your AI assistant to work with Taiga.

Benefits

• No MCP Server Required: Skip the complexity of setting up and maintaining an MCP server
• Direct Integration: Execute commands directly from your AI system
• Full Feature Access: Access all CLI features without additional API layers
• Simple Authentication: Use standard CLI authentication flow

Example AI Workflow

# AI system executes commands like:
taiga project list                    # Find projects
taiga userstory list -p 123          # List user stories
taiga userstory get 456              # Get details
taiga userstory create -p 123 -s "New Story"  # Create new story

The AI system then parses the output and presents it to the user in a conversational format.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.