Checklist MCP Server

The Checklist MCP Server is a dedicated Model Context Protocol service that turns any AI assistant into a powerful hierarchical task manager. By exposing a simple HTTP endpoint, it lets agents create, modify, and visualize nested to‑do lists that can grow arbitrarily deep. Each session is isolated, so multiple agents or users can work on separate plans without interference, and the server keeps a lightweight LRU cache of completed work summaries that can be handed off between agents.

What problem does it solve? In many real‑world workflows, an LLM needs to keep track of a multi‑step plan, remember what has been finished, and update its own state as new information arrives. Traditional prompt‑based approaches force the assistant to re‑generate the entire plan each time, which is slow and error‑prone. The Checklist MCP Server eliminates this overhead by providing a persistent, structured representation of the plan that can be queried or updated in constant time. The server’s ASCII tree output offers an instant, human‑readable snapshot of progress, making it easy to audit or debug the agent’s reasoning.

Key features are built around developer convenience and robustness. The HTTP streamable transport is the recommended, modern interface that scales to high request rates and supports pipelined responses. Hierarchical task management is native: agents can add sub‑tasks at any depth, rename them, or change their status using a simple path notation. Validation logic ensures that task identifiers stay within a 1‑20 character alphanumeric limit, preventing accidental key collisions. Real‑time updates mean that every change is immediately reflected in the full tree view, which can be streamed back to the client for live dashboards.

Use cases span from simple personal productivity tools—where an assistant drafts a shopping list that expands into sub‑categories—to complex multi‑agent orchestration. For example, an orchestrator LLM can hand off a sub‑task list to a specialized agent, the latter updates its status, and the former resumes with a fresh snapshot of remaining work. The built‑in LRU cache keeps memory usage bounded, automatically purging stale sessions and ensuring that long‑running deployments remain stable.

Integrating the server into an AI workflow is straightforward: configure the MCP client to point at the endpoint, then use the standard MCP verbs (, , ) to manipulate tasks. Because the server is stateless between sessions, it can be deployed behind a load balancer or in a Docker container without additional state management. Its minimal dependencies and HTTP‑only design make it an attractive choice for developers looking to add reliable task tracking to conversational agents without reinventing persistence or visualization layers.