Cursor Feedback

中文文档

One conversation, unlimited AI interactions - If you're on a per-request plan, it saves your monthly quota; plus it bridges your agent tool with Feishu (Lark) — when AI asks for feedback, it's pushed to Feishu and you can reply from your phone. An interactive feedback tool built on MCP (Model Context Protocol).

💡 Why Cursor Feedback?

If you're on Cursor's 500 requests/month plan or another coding 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
• 🔔 Feishu (Lark) Bridge - When AI requests feedback, the summary is pushed to Feishu so you can reply right from your phone
• 🌍 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:

<a href="https://cursor.com/en/install-mcp?name=cursor-feedback&config=eyJjb21tYW5kIjoibnB4IiwiYXJncyI6WyIteSIsImN1cnNvci1mZWVkYmFja0BsYXRlc3QiXX0%3D"> <img src="mcp-install-dark.png" alt="Install MCP Server" width="126" height="28" /> </a>

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

1. Restart Cursor
2. The Cursor Feedback icon appears in the sidebar
3. Ask AI to call the interactive_feedback tool in chat
4. 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	required	Absolute path of the project workspace you are currently in (the open workspace; 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

Notification Settings

Click the "Notification settings" icon at the top of the feedback panel to configure in-app and Feishu notifications, or adjust them in VS Code settings:

Setting	Type	Default	Description
cursorFeedback.systemNotification	boolean	true	In-app notifications (main switch): automatically show the feedback panel when AI requests feedback. When off, this window stays fully silent — no panel, no focus stealing, and nothing pushed here
cursorFeedback.osNotification	boolean	true	Notify when in background (sub-option): fire a native system notification only when the IDE window is not focused. When off, nothing pops even if you switch away (the panel still shows)
cursorFeedback.notificationSound	boolean	true	Play a sound with the system notification

macOS note: notifications are sent via osascript. If you don't see them, allow notifications for "Script Editor" in System Settings → Notifications.

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. Also toggleable via the "Keep-waiting" switch in the panel (priority: panel > env > default)

Feishu Notifications

Either way works — but you first need to set up the bot in the Feishu console: enable the bot, grant permissions, and turn on event subscription (long-connection mode + the im.message.receive_v1 event). Full walkthrough in the Feishu setup guide:

• On Cursor: fill in Feishu credentials via the "Notification settings" icon at the top of the panel.
• On other MCP hosts (agent tools without this panel): configure through env in mcp.json:

{
  "mcpServers": {
    "cursor-feedback": {
      "command": "npx",
      "args": ["-y", "cursor-feedback@latest"],
      "env": {
        "FEISHU_APP_ID": "cli_xxxxxxxx",
        "FEISHU_APP_SECRET": "your_app_secret"
      }
    }
  }
}

Environment Variable	Default	Description
FEISHU_APP_ID	-	Feishu app App ID (e.g. cli_xxxxxxxx)
FEISHU_APP_SECRET	-	Feishu app App Secret
FEISHU_ENABLED	true	Whether to push feedback to Feishu. Set to false to disable
FEISHU_ACK	true	Whether to react with a "Get" emoji after you reply. Set to false to disable

Priority: panel config (when credentials are filled) > env here > default. The panel wins when App ID/Secret are filled; otherwise it falls back to env. You still need to send the bot one message in Feishu to complete binding.

🏗️ Architecture

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

Workflow:

1. AI Agent calls MCP Server's interactive_feedback tool via stdio
2. MCP Server creates feedback request, exposes via HTTP API
3. Cursor extension polls for requests, displays in sidebar WebView
4. User inputs feedback (text/images/files), submits via HTTP
5. 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