Cursor Feedback

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![Open VSX Version](https://open-vsx.org/extension/jianger666/cursor-feedback) ![Open VSX Downloads](https://open-vsx.org/extension/jianger666/cursor-feedback) ![npm](https://www.npmjs.com/package/cursor-feedback)

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).

!Demo

💡 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: ``bash 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 | 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.

Feishu round-trip on any MCP host

cursor-feedback is not tied to Cursor. The whole Feishu loop — long connection, card push, and reply routing — lives inside the MCP server process, so it works on any MCP-capable agent host: Cursor, Claude Desktop, Cline, command-line agents, or your own harness. No sidebar / VS Code panel required.

Three steps to enable the round-trip:

Register the MCP server in the host's config with Feishu credentials in env (same FEISHU_APP_ID / FEISHU_APP_SECRET as above; FEISHU_ENABLED / FEISHU_ACK optional):

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

Bind the chat once — send the bot any message in Feishu. The server records that chat as the push target (persisted to disk, shared across processes), so it knows where to deliver cards.

Round-trip — the agent calls interactive_feedback → a card is pushed to Feishu → you reply in Feishu → your reply is routed back to the agent as the tool result. Reply from your phone; no IDE needed.

No idle-kill outside Cursor. Only the Cursor extension keeps the server warm by polling. On every other host the server stays alive purely on the stdio connection: it exits on stdin EOF or parent-process death, and never on idle — and it always stays up while a feedback request is still waiting. So a reply that takes several minutes still makes it back.

🏗️ 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