Overview

The Task Tracker MCP server bridges two popular productivity tools—Linear for task management and TrackingTime for time tracking—into a single, AI‑friendly interface. By exposing these services through the Model Context Protocol, Claude and other LLM assistants can manipulate tasks, update statuses, and control time entries using natural language commands. This removes the friction of switching between separate web apps or command‑line utilities, allowing developers to keep their focus on high‑level problem solving while the assistant handles routine workflow steps.

At its core, the server implements a set of intuitive resources that mirror Linear’s and TrackingTime’s capabilities. Developers can create new tasks, assign them to projects or teams, and specify initial states such as backlog or started. The server also supports querying tasks by status, searching by title, and updating a task’s state—all through straightforward MCP endpoints. For time tracking, the server offers commands to start and stop timers on specific tasks, view the currently active session, and append notes directly to a TrackingTime entry. These actions are all accessible via natural language prompts, letting users say things like “Start tracking time on the bug fix task” or “Move the design review to done.”

The value proposition for AI‑centric development workflows is significant. By integrating task and time management into the same conversational context, developers can keep Claude as a single source of truth for project status and effort estimation. This reduces context switching, improves traceability of work items, and enables automated reporting or reminders that are directly tied to the assistant’s output. For example, a developer could ask Claude to “Create a new task for refactoring the authentication module and start tracking time,” and the assistant would perform both operations in one interaction.

Key use cases include continuous integration pipelines that need to log time spent on debugging, agile teams that want to automatically update task states from chat discussions, and solo developers who rely on voice or typed commands to maintain their task board. In educational settings, instructors could use the server to log student progress and time spent on assignments without leaving the chat interface. The ability to set a current working team further streamlines multi‑team environments, ensuring that new tasks land in the correct context without manual selection.

What sets Task Tracker apart is its tight coupling of Linear’s task lifecycle with TrackingTime’s granular time entries, all exposed through a single MCP server. This unified API eliminates the need for custom integrations or third‑party connectors, giving developers a ready‑made, AI‑friendly toolset that can be expanded with additional MCP servers for other services. The result is a cohesive, conversational workspace where task creation, status updates, and time tracking coexist seamlessly within the same AI assistant.