interactive_feedbackLABSCO SUMMARY
One question beats twenty speculative tool calls.
The economics are the argument: an assistant that is unsure will branch and try things, each attempt a paid request, and asking instead collapses that into one exchange. The panel earns the extra step by carrying more than text — pasted screenshots go back as images the model can read, and command output comes back in the same reply.
What it is
A human-in-the-loop server for Cursor and similar tools. When the assistant would otherwise guess, it opens a Qt window showing what it has done, waits for your reply, and hands both your feedback and any command output back. The README is in Chinese, with an English version alongside it.
What you get
interactive_feedbacktakes aproject_directoryand asummary, and returnslogsplus your typedinteractive_feedback- Quick-reply buttons for the common answers — approve, ask for a small change, work through the remaining checklist
- RIPER-5 buttons that push a structured pass: research, innovate, plan, execute, review
- Images by drag-and-drop or Ctrl+V, compressed to a target you set and sent as Base64 for the model to look at
- A command panel with live output, process start and stop, and per-project command history
- Settings stored per project — the run command, button layout, language and image options are remembered separately for each directory
- An interface that switches between Chinese and English on the spot
Requirements
Python 3.11 or newer and uv, plus a desktop session — this opens a real window, so it is not something to run on a headless host. Clone it, uv venv, uv sync, then point Cursor's mcp.json at server.py through uv. Two config details matter: raise the client timeout, since the call waits for a person, and auto-approve interactive_feedback so the prompt itself does not need approving. Version 1.0.0.
Setup effort
Build from source — git clone https://github.com/liaosiqian/interactive-feedback-mcp.git
USE CASES
01
Checking direction before a big edit
Have the assistant summarise its plan and wait for approve or adjust rather than proceeding on a guess.
02
Showing the assistant what you are looking at
Paste a screenshot into the panel and let it come back as an image with your comment.
03
Running the build and reacting to what it prints
Keep the project's command in the panel, run it, and send the output back with your reply.
1 tool
Listed in project docs. Your client may show a different list.
BEFORE YOU INSTALL
What to know first
Tools you get
1 tool named in the project's documentation.
Requires
Python 3.11 or later
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
You clone this one yourself.
Server setup
TERMINAL
git clone https://github.com/liaosiqian/interactive-feedback-mcp.git
cd interactive-feedback-mcp
uv venv
uv syncMCP Clients
Cursor
CURSOR · JSON
{
"mcpServers": {
"interactive-feedback-mcp": {
"command": "uv",
"args": [
"--directory",
"<YOUR_PATH_TO_INTERACTIVE_FEEDBACK_MCP>",
"run",
"server.py"
],
"timeout": 600,
"autoApprove": [
"interactive_feedback"
]
}
}
}
