Task Planner MCP Server

Task Planner MCP Server

The Task Planner MCP server tackles a common pain point for AI‑assisted workflows: the need to translate high‑level user intent into a concrete, actionable plan. When an assistant like Claude receives a request such as “Plan my vacation,” it can delegate the decomposition of that goal into discrete, trackable steps. The server maintains a hierarchical task list, allowing the assistant to manage dependencies, progress, and priorities without embedding complex state logic in the model itself. This separation of concerns keeps the AI lightweight while giving developers a robust, reproducible task‑management layer.

At its core, the server exposes a set of intuitive tools that mirror everyday productivity habits. Developers can create, update, and delete tasks, optionally nesting them under a parent to form subtasks. The break‑down-task tool is particularly valuable: it lets an assistant automatically split a vague objective into concrete, prioritized subtasks. Each task can be marked as complete, queried for detailed information, or listed alongside its siblings. The ability to set a priority level (low, medium, high) adds an extra dimension for scheduling and resource allocation. All data is persisted in a local JSON file, ensuring persistence across sessions while keeping the implementation lightweight.

Use cases abound. In personal productivity apps, a user might ask Claude to “Plan my vacation,” and the assistant can generate a structured itinerary with subtasks for flights, accommodation, activities, and packing. In software development teams, the server can serve as a lightweight project tracker: tasks such as “Implement authentication” can be broken into subtasks for database schema, API endpoints, and UI components. Because the MCP tools are stateless from the AI’s perspective, they can be combined with other MCP services—such as calendar integration or file management—to create end‑to‑end workflows that keep the user in a single conversational context.

Integration with AI workflows is straightforward. The assistant calls the appropriate tool via the MCP interface, passing parameters like task titles or IDs. The server responds with structured JSON that Claude can embed directly into the conversation, enabling dynamic updates and real‑time progress tracking. This pattern removes the need for the model to maintain complex internal state, reduces hallucination risk, and gives developers fine‑grained control over task lifecycle.

What sets this MCP apart is its focus on hierarchical task management coupled with a clean, priority‑aware API. Developers can extend or replace the underlying storage without touching the AI logic, and the server’s minimal footprint makes it ideal for prototyping or embedding in larger toolchains. Whether you’re building a personal assistant, an internal workflow bot, or a collaborative project manager, the Task Planner MCP server provides a solid foundation for turning abstract goals into executable plans.