Taskqueue MCP

Taskqueue MCP: Structured AI Task Management for Complex Workflows

Taskqueue MCP is a Model Context Protocol server that turns an AI assistant into a full‑featured task manager. It solves the common pain point of keeping multi‑step projects organized, allowing the assistant to generate, track, and close tasks while respecting a clear approval workflow. By exposing dedicated tools for project creation, task manipulation, and status transitions, the server gives developers a reliable backbone for orchestrating long‑running AI processes without losing control over intermediate steps.

The server’s core value lies in its structured approach to planning and execution. When a user asks the assistant to build a website, for example, Taskqueue MCP can break that request into discrete tasks—design wireframes, write HTML, test responsiveness—and assign each a status (, , or ). The assistant can then request user approval at critical checkpoints, ensuring that every milestone meets expectations before moving forward. This pattern is especially useful in regulated industries or when stakeholders need visibility into the AI’s progress.

Key capabilities are presented as a set of purpose‑built tools:

• Project lifecycle – Create, list, read, delete, and finalize projects. Projects can be seeded with an initial task list or expanded on the fly.
• Task CRUD – Create, read, update, delete, and list tasks within a project. Each task carries metadata such as title, description, status, and completion details.
• Status management – Enforce a strict workflow: → → , with the ability to roll back. Completed tasks require a field, and approved tasks become immutable.
• Approval gates – Approve individual tasks or entire projects only when all underlying tasks are finished and approved, preventing accidental premature closure.

These tools integrate seamlessly with any MCP‑compatible client. A developer can configure the server in Claude Desktop, Cursor, or a custom UI, and the assistant will call tools like or through normal LLM prompts. Because the server supports multiple LLM providers (OpenAI, Gemini, Deepseek) via environment variables, teams can choose the best model for each planning or summarization step without changing code.

Real‑world scenarios that benefit from Taskqueue MCP include:

• Product development pipelines – Automate sprint planning, feature implementation, and QA approval in a single workflow.
• Content creation – Break large editorial projects into research, drafting, editing, and publishing tasks, with editors approving each stage.
• Compliance workflows – Ensure that every regulatory check is documented and approved before a project can be marked complete.

In essence, Taskqueue MCP transforms an AI assistant from a conversational partner into a disciplined project manager. Its explicit task states, approval mechanics, and clean tool interface give developers the control they need to embed AI into production workflows while keeping stakeholders informed and projects on track.