Cursor Feedback

Save your Cursor monthly quota! An interactive feedback tool for Cursor that enables unlimited interactions within a single conversation through MCP (Model Context Protocol).

Demo

💡 Why Cursor Feedback?

If you're on Cursor's 500 requests/month plan, every conversation counts. With Cursor Feedback:

One conversation, unlimited interactions - Keep chatting without consuming extra quota
Human-in-the-loop workflow - AI waits for your feedback before proceeding
Sidebar integration - No external browser needed, everything stays in your IDE

✨ Features

🎯 Sidebar Integration - Feedback UI embedded directly in the IDE sidebar
💬 Interactive Feedback - AI Agent requests feedback via MCP tool
🖼️ Image Support - Upload images or paste directly (Ctrl+V / Cmd+V)
📁 File Support - Select files/folders to share paths with AI
📝 Markdown Rendering - Full Markdown support for AI summaries
⏱️ Auto-retry on Timeout - 5-minute default timeout, AI automatically re-requests
🌍 Multi-language - Supports English, Simplified Chinese, Traditional Chinese
🔒 Project Isolation - Multiple windows work independently

🚀 Quick Start

1. Install the Cursor Extension

Search for "Cursor Feedback" in the Cursor extension marketplace.

Can't find it? Install via command line:
cursor --install-extension jianger666.cursor-feedback

2. Configure MCP Server

Option A: One-click Install (Recommended)

Click the button below to automatically configure the MCP Server:

Option B: Manual Configuration

Add to your Cursor MCP config file (~/.cursor/mcp.json):

{
  "mcpServers": {
    "cursor-feedback": {
      "command": "npx",
      "args": ["-y", "cursor-feedback@latest"]
    }
  }
}

Option C: Global Installation

npm install -g cursor-feedback

Then configure:

{
  "mcpServers": {
    "cursor-feedback": {
      "command": "cursor-feedback-mcp"
    }
  }
}

3. Usage

Restart Cursor
The Cursor Feedback icon appears in the sidebar
Ask AI to call the interactive_feedback tool in chat
AI displays work summary in sidebar, waiting for your feedback

4. Configure User Rules (Recommended)

Add these User Rules in Cursor settings for best experience:

# Cursor Feedback Rules

1. Always call MCP cursor-feedback during any workflow, task, or conversation.
2. After receiving user feedback, call MCP cursor-feedback again and adjust behavior accordingly.
3. Before completing a task, use MCP cursor-feedback to ask for user feedback.
4. All content to show users must be passed through cursor-feedback's summary parameter.

This enables continuous interactive dialogue where AI automatically uses Cursor Feedback throughout the conversation.

📖 MCP Tool

interactive_feedback

Interactive feedback collection tool.

Parameters:

Parameter	Type	Default	Description
`project_directory`	string	`.`	Absolute path of project directory (for multi-window isolation)
`summary`	string	`I have completed the task you requested.`	AI work summary (supports Markdown)
`timeout`	number	`300`	Timeout in seconds (default 5 minutes)

Timeout Mechanism:

Default wait time: 5 minutes (300 seconds)
On timeout, AI receives notification
AI automatically re-calls the tool based on instructions
Even if you step away, AI will still be waiting when you return

Returns:

User feedback content including text, images, and attached file paths.

⚙️ Configuration

Language Settings

Method 1: Click the 🌐 button in the sidebar (Recommended)

Click the globe icon in the Cursor Feedback sidebar to switch languages.

Method 2: Through VS Code Settings

Search "Cursor Feedback" in settings:

Setting	Type	Default	Description
`cursorFeedback.language`	string	`zh-CN`	UI language

Available languages:

zh-CN - Simplified Chinese (简体中文)
en - English

MCP Server Configuration

Basic config:

{
  "mcpServers": {
    "cursor-feedback": {
      "command": "npx",
      "args": ["-y", "cursor-feedback@latest"]
    }
  }
}

Custom timeout (optional, default 5 minutes):

{
  "mcpServers": {
    "cursor-feedback": {
      "command": "npx",
      "args": ["-y", "cursor-feedback@latest"],
      "env": {
        "MCP_FEEDBACK_TIMEOUT": "600"
      }
    }
  }
}

Environment Variable	Default	Description
`MCP_FEEDBACK_TIMEOUT`	`300`	Timeout in seconds (default 5 minutes)
`MCP_AUTO_RETRY`	`true`	Whether AI should auto-retry on timeout. Set to `false` to disable

🏗️ Architecture

┌─────────────────┐     stdio      ┌──────────────────┐
│   AI Agent      │ ◄──────────► │   MCP Server     │
│   (Cursor)      │               │  (mcp-server.js) │
└─────────────────┘               └────────┬─────────┘
                                           │ HTTP API
                                           ▼
                                  ┌──────────────────┐
                                  │  Cursor Extension│
                                  │  (extension.js)  │
                                  └────────┬─────────┘
                                           │ WebView
                                           ▼
                                  ┌──────────────────┐
                                  │   User Interface │
                                  │   (Sidebar)      │
                                  └──────────────────┘

Workflow:

AI Agent calls MCP Server's interactive_feedback tool via stdio
MCP Server creates feedback request, exposes via HTTP API
Cursor extension polls for requests, displays in sidebar WebView
User inputs feedback (text/images/files), submits via HTTP
MCP Server returns feedback result to AI Agent

📊 Comparison with mcp-feedback-enhanced

Feature	mcp-feedback-enhanced	cursor-feedback
MCP Tool	✅	✅
Text Feedback	✅	✅
Image Upload	✅	✅
Image Paste	✅	✅
File/Folder Selection	❌	✅
Markdown Rendering	✅	✅
Multi-language	✅	✅
Auto-retry on Timeout	✅	✅
IDE Sidebar Integration	❌	✅
Multi-window Project Isolation	❌	✅
Command Execution	✅	⏳

🛠️ Development

# Clone the project
git clone https://github.com/jianger666/cursor-feedback-extension.git
cd cursor-feedback-extension

# Install dependencies
npm install

# Compile
npm run compile

# Watch mode
npm run watch

# Run lint
npm run lint

# Package extension
npx vsce package

📄 License

MIT

🙏 Acknowledgments

mcp-feedback-enhanced - Original Python implementation
Model Context Protocol - MCP Protocol

cursor-feedback-extension