u/cleverquokka

I'm a product manager and spend a lot of time iterating on long prompts and specs that AI agents then act on. The review loop has been the worst part. When the model gives me a 5-page draft, leaving feedback meant copy-pasting chunks back into chat with "change this to that". Twenty round trips on a long doc. Comments without specific anchors get misinterpreted half the time.

So I built md-redline. It opens a markdown file in a local app, you highlight specific text spans and leave inline comments, and the comments persist as invisible HTML markers in the markdown file itself. When you hand it back to your AI agent, the agent reads each comment with its exact anchor text. No more "which paragraph did you mean?".

The MCP server is what made it click for me. Your agent calls `mdr_request_review`, the file opens in the app, you leave precise inline feedback, click Send Review, and the agent picks up where it left off with your annotations as structured input. The model sees not just "make it shorter" but "make THIS sentence shorter, this one changed to X, this paragraph moved before that one". Way more precise than chat-based revision.

Works with anything that speaks MCP. I've tested Claude Code, Codex CLI, Gemini CLI, Claude Desktop.

Local-first. The markdown file stays the source of truth, which means even GitHub renders it as plain markdown (the comment markers are invisible in normal renderers).

Repo: https://github.com/dejuknow/md-redline (free, MIT)

30 second demo

Curious how others handle prompt revision loops. Is everyone just chat-iterating, or do people have better tooling for this?