md-feedback

Name: md-feedback
Author: yeominux

by yeominuxversion 1.5.6i★ –iRuns on your computer

Registry nameio.github.yeominux/md-feedback

Install

Overview

Description from the maker (original English)

Review markdown plans before AI agents build. Annotate, gate-check, apply fixes, session handoffs.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "md-feedback": {
      "command": "npx",
      "args": [
        "-y",
        "md-feedback"
      ]
    }
  }
}

Run this in your terminal

claude mcp add md-feedback -- npx -y md-feedback

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "md-feedback": {
      "command": "npx",
      "args": [
        "-y",
        "md-feedback"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "md-feedback": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "md-feedback"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account yeominux, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32708 servers in this category
Published to the registry more recently than 4% of the other 32708 servers in this category
Basic checks equal to or better than 100% of the other 32708 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is md-feedback safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.5.6) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, OpenTakeoff

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago

Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago

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OpenTakeoff

by Kentucky-ai

Construction takeoff for AI agents: load plans, set scale, measure, count, export with provenance.

Runs on your computerUpdated today

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

MD Feedback

Review your plan. Guide your AI agent. Ship with confidence.

English | 한국어

MD Feedback is a VS Code extension and MCP server for reviewing markdown plans before AI agents implement them. Annotate plans with Fix, Question, and Highlight — AI agents read your structured feedback directly through MCP. No copy-paste, no export step, no context lost between sessions.

Install from VS Code Marketplace, open a .md plan, select text, press 1/2/3, and your agent can act on that review immediately.

You review. The agent builds. Gates track completion. Handoffs preserve context.

Latest (v1.5.7): Web UI for npx md-feedback — open your markdown workspace in any browser.

How It Works

From plan to implementation, the complete AI coding loop:

Step 1  YOU        Write a plan in markdown

│ Step 2 YOU Open in MD Feedback sidebar → highlight, fix, question │ (press 1, 2, or 3) │ Step 3 AGENT Reads annotations via MCP — no export step needed │ Step 4 AGENT Implements fixes, answers questions │ Step 5 YOU Review AI work → Approve, Request Changes, or Reject │ Step 6 AGENT Gates auto-evaluate │ "3 fixes remaining" → "All done, ready to merge" │ Step 7 AGENT Generates handoff → next session picks up where you left off

You do steps 1–2 and 5. The agent does the rest.

This is the MCP-first path. If you use export-based workflow, run export after step 2.

Features

3 annotation types: Highlight (reading mark), Fix (needs change), Question (needs clarification)
27 MCP tools for direct agent integration
Export to 11 AI tools: Claude Code, Cursor, Copilot, Codex, Cline, Windsurf, Roo Code, Gemini, Antigravity, Generic, Handoff
Quality gates with automatic pass/fail evaluation
Session handoffs preserve context across AI agent sessions
Checkpoints track review progress with snapshots
Plan cursor tracks current position in a document
Keyboard shortcuts: press 1, 2, 3 for instant annotation
AI applies fixes via MCP — agent reports implementations, you see inline before/after diffs
7 status badges: Open, Working, Review, Answered, Done, Failed, Won't Fix
Rollback: agent can undo its last change if something went wrong
Batch operations: multiple fixes applied in one transaction
Safe text replacement: when the same text appears multiple times, agents must specify which one to change (prevents accidental wrong-line edits)
File safety: blocks writes to .env, credentials, node_modules
Approve / Reject buttons — accept or dismiss annotations inline, always visible when review needed
CodeLens in editor — approve or reject directly in the markdown file, no sidebar needed
Activity Bar badge — see pending review count at a glance
Status bar + toast notifications — never miss when AI delivers work for review
Keyboard shortcuts — Ctrl+Shift+A to approve, Ctrl+Shift+X to reject
Gate override — manually control gate status when auto-evaluation isn't enough
External file diffs inline — see exactly what AI will change before applying
Gate transition notifications — know when gates unblock or complete
Concurrent safety — prevents data corruption when multiple AI operations run at the same time
Auto-refresh: document updates in real-time when AI writes changes
Portable format: annotations stored as HTML comments — works in any markdown renderer, survives git
Rich rendering: Mermaid diagrams, callout blocks, syntax-highlighted code

Quick Start (under 2 minutes)

Install from VS Code Marketplace
Annotate first — select text in a markdown file, then press 1 (highlight), 2 (fix), 3 (question)
Optional: Connect MCP — after first annotation, click Connect AI in the sidebar and add config to your MCP client:

{ "mcpServers": { "md-feedback": { "command": "npx", "args": ["-y", "md-feedback"] } } }

Done — MCP-compatible agents read annotations directly. If your agent is export-only (for example current Copilot flow), use Export.

Node.js 18+ required for MCP (npx).

Claude path: .claude/mcp.json

Cursor path: .cursor/mcp.json

Try it now: Install from Marketplace, open any .md file, and press 2 to add your first Fix annotation.

Use Cases

Vibe Coding Workflow

Write a plan in markdown. Review it with MD Feedback. Let your AI agent build exactly what you reviewed. The plan is the contract — annotations are the instructions.

AI Plan Review

An AI agent generates an implementation plan. You review it with highlights, fixes, and questions before the agent writes any code. Catch errors at design time, not after implementation.

Session Continuity

Working with AI across multiple sessions? Handoffs preserve every decision, open question, and key context point. The next session starts where the last one ended.

Team Plan Review

Annotations are HTML comments in the markdown file. They survive git commits, PRs, and branch merges. Share reviewed plans with your team through your normal version control workflow.

Quality Gate Enforcement

Set conditions that must be met before the agent proceeds. Gates auto-evaluate based on annotation resolution status — blocked, proceed, or done.

Design Philosophy

Humans only state what is wrong. AI decides how to fix it.
Three annotation types are sufficient. AI infers intent from context — whether a fix means a document edit or a code change.
Markdown is the source of truth. All state lives in the file itself.
Zero cognitive load. Status bar shows progress passively. No extra decisions required.
Portable and git-friendly. Annotations are HTML comments — they survive any markdown renderer and version control.

VS Code Settings

You can customize MD Feedback from VS Code Settings via md-feedback.*. Advanced timing and performance tuning options are available for large workspaces.

MCP Server

MD Feedback includes an MCP server with 27 tools that let AI agents read your annotations without manual export. Agents can query memos, mark tasks done, apply fixes, check gate status, and generate handoffs — all through the Model Context Protocol.

Setup:

npx md-feedback

Workspace override — if your MCP client doesn't set cwd to the project folder (e.g. Antigravity), specify it explicitly:

{ "command": "npx", "args": ["-y", "md-feedback", "--workspace=/path/to/project"] }

Windows example: { "command": "npx", "args": ["-y", "md-feedback", "--workspace=C:\\\\work\\\\my-project"] } Or via environment variable: MD_FEEDBACK_WORKSPACE=/path/to/project

For full details, see MCP Server documentation.

License

SUL-1.0 — Free for personal and non-commercial use.

FAQ

What is MD Feedback? MD Feedback is a VS Code extension and MCP server for reviewing markdown plans before AI agents implement them. Select text, press 1 (highlight), 2 (fix), or 3 (question) — annotations are stored as portable HTML comments in the markdown file itself. AI agents read annotations directly via MCP, or you can export to 11 AI tools.

Does it work with Claude Code / Cursor / Copilot? Yes. MD Feedback exports to Claude Code (CLAUDE.md), Cursor (.cursor/rules/), GitHub Copilot (.github/copilot-instructions.md), and 8 more tools. With MCP, agents read annotations directly — no export step needed.

What is MCP and why does it matter? MCP (Model Context Protocol) lets AI agents interact with external tools. MD Feedback's MCP server gives agents direct access to your annotations, so they can read feedback, mark tasks done, evaluate gates, and generate handoffs automatically. For example, when you mark a section as "Fix: use retry logic here," the agent reads that annotation via MCP, implements the fix, and marks it done — all without you switching tabs.

Can multiple people review the same plan? Yes. Annotations are HTML comments embedded in the markdown file. They travel with the file through git — commits, branches, pull requests, and merges all preserve annotations intact.

Is it free? Yes. MD Feedback is free for personal and non-commercial use under the SUL-1.0 license.

Who is this for? Developers using AI coding assistants who want to review plans before implementation, preserve context across sessions, and give agents structured feedback instead of unstructured chat messages.

More questions and advanced guidance: MCP Server docs and GitHub Issues.

md-feedback

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

OpenTakeoff

README from the maker

MD Feedback

How It Works

Features

Quick Start (under 2 minutes)

Use Cases

Vibe Coding Workflow

AI Plan Review

Session Continuity

Team Plan Review

Quality Gate Enforcement

Design Philosophy

VS Code Settings

MCP Server

Links

License

FAQ