Overview

The Mcp Taskwarrior server bridges the gap between AI assistants and the popular command‑line task manager Taskwarrior. By exposing a lightweight MCP interface, it allows AI agents such as Claude, Cursor, or Goose to create, modify, and query tasks directly from their natural‑language conversations. This eliminates the need for developers to write custom adapters or manually sync data between Taskwarrior and their AI workflows.

At its core, the server implements four intuitive tools: add_task, update_task, delete_task, and list_tasks. These tools mirror the most common operations a user performs in Taskwarrior, yet they are exposed as declarative MCP calls that can be invoked with simple prompts. For example, an AI assistant can respond to “What are my current tasks?” by calling , while a developer can programmatically add a new item with . The server handles all the underlying Taskwarrior commands, parses their output, and returns structured JSON that the AI can seamlessly interpret.

Key capabilities include:

• Seamless task CRUD: Create, read, update, and delete tasks without leaving the chat interface.
• Priority & filtering: The tool can be customized with filters such as priority, project, or due date, enabling the assistant to surface only relevant items.
• Contextual awareness: Because MCP passes context automatically, the AI can remember task details across turns and suggest follow‑up actions or reminders.
• Extensibility: Developers can add custom tags or integrate with Taskwarrior’s annotations, allowing richer interactions such as linking tasks to external resources.

Typical use cases span personal productivity and team collaboration. A developer working on a home renovation project can ask the assistant to “Add ‘Install photo room vent cover’ with high priority,” and later query, “Which tasks are due before Feb 24?” The assistant can also delete completed items or update a task’s status, keeping the Taskwarrior database in sync with spoken commands. In corporate settings, teams can use the MCP server to let AI assistants act as a lightweight project manager, automatically generating task lists from meeting notes or sprint backlogs.

The server’s integration is straightforward: an MCP client selects the “Command” style MCP and points to the repository’s file. From there, any AI workflow can invoke the exposed tools as part of a larger chain of reasoning or data retrieval. This tight coupling means developers can build richer, context‑aware AI experiences without reinventing the wheel for task management.