Taskboardai

🚀 Taskboard AI

A lightweight, file-based task kanban board designed specifically for AI agents. It includes a web interface for human-machine collaboration.

✨ Features

AIX Features

• JSON File Storage: Save task data in JSON format in local files.
• MCP Server Integration: Support the Model Context Protocol (MCP) and work with MCP-compatible tools such as Claude Code, Cursor, and Windsurf.

Web Features

• Kanban Board Management: Create, update, and delete kanban boards via the command line or MCP agents.
• Webhook Integration: Support Webhooks for real-time data synchronization and service integration.

📦 Installation

Method 1: Using npm

1. Open the terminal.
2. Run the following command:

   npm install taskboardai
   

3. After the installation is complete, run the following command in the terminal to start the service:

   node_modules/.bin/taskboardai start
   

Method 2: Compiling from Source Code

1. Clone the repository:

   git clone https://github.com/yourusername/taskboardai.git
   cd taskboardai
   

2. Install dependencies:

   npm install
   

3. Start the service:

   npm start
   

💻 Usage Examples

Basic Usage

• List all kanban boards:

  taskboardai list
  

• Create a new kanban board:

  taskboardai create --name "Project X"
  

• Open a specified kanban board:

  taskboardai open --name "Project X"
  

• Delete a kanban board:

  taskboardai delete --name "Project X"
  

Advanced Usage

• MCP Agent Control:
  • Start the MCP server:

    taskboard-mcp start
    

  • Start both the kanban board service and the MCP server:

    taskboard-all start
    

• Webhook Configuration:
  • Add a Webhook:

    taskboardai webhook add --url "https://example.com/webhook"
    

  • Test the Webhook connection status:

    taskboardai webhook test --id 1
    

📚 Documentation

Kanban Board Data Structure

The kanban board data is stored in JSON format and contains the following fields:

{
  "Name": "Project X",
  "Task List": [
    {
      "Title": "Complete requirements analysis",
      "Description": "Collect and organize user requirements documents",
      "Priority": "High",
      "Status": "In progress",
      "Start Time": "2023-10-01",
      "Due Time": "2023-10-15"
    }
  ],
  "Next Steps": [
    "Review the requirements document",
    "Assign development tasks"
  ],
  "Last Update Time": "2023-10-05T18:30:00Z"
}

Testing

Run all tests

npm test

Generate a coverage report

npm run test:coverage

Run tests in watch mode

npm run test:watch

Run tests by category

• Run only MCP server tests:

  npm test -- --testPathPattern 'tests/.*mcp'
  

• Run only unit tests:

  npm test -- tests/unit
  

• Run only integration tests:

  npm test -- tests/integration
  

Documentation Generation

Install dependencies (if not installed)

npm install

Generate documentation (output directory is docs/api)

npm run docs

Data Storage Location

• Linux/macOS: ~/.taskboardai/
• Windows: C:\Users\<username>\.taskboardai\

The data directory contains:

• boards/: Your kanban board JSON files
• config/: Configuration files
• webhooks/: Webhook configurations

🤝 Contribution Guidelines

Contributions are welcome! Please refer to CONTRIBUTING.md for specific guidelines.

📄 License

This project is licensed under the Apache License 2.0. See LICENSE for details.