Overview of mcp-micromanage

mcp-micromanage tackles a common pain point in AI‑augmented software development: keeping a coding agent focused, accountable, and transparent. While agents can rapidly produce code, they often drift from the original task, generate unnecessary changes, or fail to solicit timely user feedback. This server imposes a disciplined workflow that mirrors conventional git‑based development—each ticket is broken into pull requests (PRs) and commits, each commit becomes a checkpoint for review, and the entire plan is visualized in real time. The result is a structured, observable process that aligns the agent’s output with human expectations.

The server exposes three core tools—plan, track, and update—that together form a dynamic development lifecycle. With plan, users describe the desired implementation in terms of PRs and their constituent commits, creating a hierarchical work plan that the agent must follow. The track tool queries the current state of every item, allowing the assistant or a human reviewer to see which commits are pending, in progress, or completed. Finally, update lets the agent or user transition items through their lifecycle, enforcing that each commit receives explicit approval before the next step proceeds. This explicit hand‑off mechanism eliminates silent regressions and ensures that every change is vetted.

Visualization is a standout feature. A lightweight React dashboard renders the entire plan as an interactive tree, with color‑coded status indicators and auto‑refresh. Developers can zoom, pan, and drill down into individual PRs or commits to inspect diffs or comments. By providing an at‑a‑glance view of progress, the dashboard turns abstract planning into tangible metrics, making it easier to spot bottlenecks or stalled tasks. The visual layer is optional; the server can run headless, integrating seamlessly into any MCP‑enabled workflow.

In practice, mcp-micromanage shines in scenarios where rigorous code quality and traceability are mandatory: regulated software, security‑critical projects, or collaborative teams that rely on formal pull‑request reviews. It also benefits solo developers who want to enforce discipline while working with an AI partner, preventing the “agent goes off track” problem that often leads to wasted effort. By mapping the agent’s actions onto a commit‑based workflow, developers gain fine‑grained control without sacrificing the speed of AI assistance.

Overall, mcp-micromanage turns a creative coding agent into a disciplined collaborator. Its commit‑driven planning, mandatory feedback loops, and real‑time visualization provide a robust framework that aligns AI output with human intent—making it an essential tool for developers who need both speed and accountability in their AI‑powered workflows.