TaskWarrior MCP Server

The TaskWarrior MCP Server bridges the powerful command‑line task manager TaskWarrior with modern AI assistants that speak the Model Context Protocol. By exposing TaskWarrior’s core operations as MCP tools, developers can embed personal task management directly into conversational workflows—letting an AI read, create, and complete tasks without leaving the chat interface. This solves a common pain point for productivity‑oriented developers: keeping their task list in sync with an assistant that can reason, plan, and remember context.

At its heart the server runs the local binary, translating simple JSON commands into TaskWarrior actions. The main capabilities include listing pending tasks (optionally filtered by project or tags), adding new tasks with rich metadata such as due dates, priorities, projects, and tags, and marking tasks as done. These operations map cleanly onto everyday productivity patterns—viewing what needs attention, planning new items on the fly, and closing items after completion. Because TaskWarrior is already a mature, scriptable tool, the MCP server adds minimal overhead while unlocking AI‑driven task interactions.

Key features of this MCP implementation are:

• Task enumeration: returns a structured list of pending items, optionally scoped to projects or tags, enabling an assistant to ask “What’s my next work task?” and deliver a ready‑to‑read summary.
• Task creation: accepts natural‑language descriptions, dates in ISO format, priority flags (H/M/L), and project/tag metadata, allowing the AI to translate a user’s intent into a fully‑formed TaskWarrior entry.
• Completion handling: lets the assistant acknowledge finished work, using either the unstable numeric ID or a future UUID for robustness.
• Filter flexibility: By passing project names or tag lists, developers can tailor responses to specific contexts (e.g., “Show me all tasks tagged bug” or “What’s due for project X?”).

In practice, this server shines in scenarios where a developer or team member wants to keep their TaskWarrior backlog synchronized with an AI assistant. For example, a developer can ask Claude to “list my next work tasks” and receive an instant rundown of pending items, or instruct the assistant to “add a high‑priority call to my sister” and have it appear in TaskWarrior with the correct metadata. After completing an item, a simple “I’ve finished that task” will trigger , keeping the local database up to date.

Integrating the TaskWarrior MCP Server into an AI workflow is straightforward: configure the server in the assistant’s client (e.g., Claude Desktop) and invoke its tools via natural language. The server’s lightweight Node.js implementation means it can run locally on any machine where TaskWarrior is installed, preserving privacy and ensuring zero‑latency responses. Its design also leaves room for future enhancements—such as switching from numeric IDs to UUIDs—to make the integration even more reliable.

Overall, this MCP server provides a seamless, AI‑enabled extension to TaskWarrior that empowers developers to manage their tasks conversationally, keeping productivity high without sacrificing the control and granularity that TaskWarrior users expect.