mcp

This MCP server provides a task management integration that bridges AI assistants with a robust task management API. It exposes capabilities for creating, listing, retrieving, updating, and deleting tasks, enabling AI tools to orchestrate work across systems and notify you on your iPhone. The server is designed to be consumed by MCP clients such as Claude Desktop, Cursor, or other apps that support the MCP protocol, allowing you to drive task workflows from conversational interfaces or automation scripts.

To use it, configure your MCP client to point to this server. The CLI or UI in the client will then expose the available actions (create_task, list_tasks, get_task, update_task, delete_task). You can supply parameters like title, description, priority, and notification settings when creating tasks, and filter or modify tasks as needed using the update_task and delete_task endpoints. The REST API documentation linked in the project provides the exact request formats and response schemas, along with authentication guidance if an API key is required by your setup.

Prerequisites:

• Python 3.8 or newer
• pip or pipx

Installation steps:

1. Create and activate a virtual environment (recommended): python -m venv venv source venv/bin/activate # on macOS/Linux .\venv\Scripts\activate # on Windows
2. Install the MCP server package from PyPI: pip install task-mcp
3. Verify installation: python -m task_mcp --version # or follow the package's CLI instructions
4. Run the server (example): python -m task_mcp
5. Point your MCP clients to the running server endpoint and begin using the available tasks API:
   • create_task
   • list_tasks
   • get_task
   • update_task
   • delete_task

Tips and reminders:

• Authentication: The server uses API keys via the X-API-Key header for client requests if your deployment requires it; ensure you configure keys in your MCP client when needed.
• Documentation: Refer to the REST API docs and the MCP server docs provided in the project for exact request/response formats and supported fields.
• Environment separation: Run the server in a dedicated virtual environment to avoid dependency conflicts.
• Deployment: For production, consider deploying behind HTTPS and using a reverse proxy if exposing the server publicly.
• Troubleshooting: If a client cannot connect, verify the server is running, the correct port is used, and that the client configuration matches the server URL.