MCP Feedback Enhanced
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Feedback EnhancedPause and ask for my feedback on the next steps."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Feedback Enhanced
🌐 Language / 語言切換: English | 繁體中文 | 简体中文
Original Author: Fábio Ferreira | Original Project ⭐ Enhanced Fork: Minidoracat UI Design Reference: sanshao85/mcp-feedback-collector
📢 Maintenance Status (2026-08)
The project is maintained again. Please upgrade to v2.6.1 — it fixes a command execution vulnerability:
uvx mcp-feedback-enhanced@latestWhat changed in v2.6.1:
🔒 Command execution removed — fixes #219 (unauthenticated WebSocket could execute arbitrary programs). The old blocklist only caught shell metacharacters, but since execution used
shell=Falsemetacharacters were never the risk —cat,curl,wget,pythonpassed straight through, and auto-command was enabled by default. The feature is gone for good. See SECURITY.md.🔒 Cross-Site WebSocket Hijacking fixed (reported privately as
GHSA-cmr5-gpm3-79vf,GHSA-2wx7-r4rh-f663): browsers are not restricted by the same-origin policy when opening a WebSocket, so a malicious page could make your browser connect to the local/ws.Originis now validated beforeaccept(), and cross-origin attempts are rejected with 403.🐛 Fixed the Starlette breaking change that made the Web UI return 500 (#213, #217, #221, #228).
🐛 Fixed image serialization (#154 and related) by switching to standard
mcp.types.ImageContent.Current maintenance scope: security issues, and compatibility breaks that make installs unusable (dependency updates, upstream breaking changes). Anything beyond that will be decided from community feedback — see the pinned discussion.
One thing worth stating plainly: the original selling point was "consolidate multiple round-trips into a single Cursor request to save quota". Cursor moved to token-based usage pricing in June 2025, so that premise no longer holds (see #115, #200). The positioning is now "insert human checkpoints into long-running tasks" — not a quota-saving tool.
Also note MCP and its clients now natively support Elicitation (server-initiated requests for user input) and MCP Apps (tools returning interactive UI). If native capabilities cover your needs, just use those — if there's something native can't do, please say so in the discussion. That's what will decide what gets fixed next.
Related MCP server: MCP Feedback Collector
🎯 Core Concept
This is an MCP server that establishes feedback-oriented development workflows, providing Web UI and Desktop Application dual interface options, perfectly adapting to local, SSH Remote environments, and WSL (Windows Subsystem for Linux) environments. By guiding AI to confirm with users rather than making speculative operations, it inserts human checkpoints into long-running tasks, reducing drift and rework.
🌐 Dual Interface Architecture Advantages:
🌐 Web UI: No GUI dependencies required, suitable for local, remote and WSL environments (the primary maintained interface)
🖥️ Desktop Application: A Tauri shell that loads the same Web UI, supporting Windows, macOS, Linux (maintenance-only since v2.8.0, no new features, scheduled for removal in v3 — see "Desktop application maintenance status" below)
📦 Unified Functionality: Both interfaces provide exactly the same functional experience
Supported Platforms: Cursor | Cline | Windsurf | Augment | Trae
🔄 Workflow
AI Call →
mcp-feedback-enhancedtoolInterface Launch → Auto-open desktop application or browser interface (based on configuration)
Smart Interaction → Prompt selection, text input, image upload, auto-submit
Real-time Feedback → WebSocket connection delivers information to AI instantly
Session Tracking → Auto-record session history and statistics
Process Continuation → AI adjusts behavior or ends task based on feedback
🌟 Key Features
🖥️ Dual Interface Support
Desktop Application: Cross-platform native application based on Tauri, supporting Windows, macOS, Linux
Web UI Interface: Lightweight browser interface suitable for remote and WSL environments
Automatic Environment Detection: Intelligently recognizes SSH Remote, WSL and other special environments
Unified Feature Experience: Both interfaces provide exactly the same functionality
📝 Smart Workflow
Prompt Management: CRUD operations for common prompts, usage statistics, intelligent sorting
Auto-Timed Submit: 1-86400 second flexible timer, supports pause, resume, cancel with new pause/resume button controls
Session Management & Tracking: Local file storage, privacy controls, history export (supports JSON, CSV, Markdown formats), real-time statistics, flexible timeout settings
Connection Monitoring: WebSocket status monitoring, auto-reconnection, quality indicators
AI Work Summary Markdown Display: Support for rich Markdown syntax rendering including headers, bold text, code blocks, lists, links and other formats for enhanced content readability
🎨 Modern Experience
Responsive Design: Adapts to different screen sizes, modular JavaScript architecture
Audio Notifications: Built-in multiple sound effects, custom audio upload support, volume control
System Notifications (v2.6.0): System-level real-time alerts for important events (like auto-commit, session timeout)
Smart Memory: Input box height memory, one-click copy, persistent settings
Multi-language Support: Traditional Chinese, English, Simplified Chinese, instant switching
🖼️ Images & Media
Full Format Support: PNG, JPG, JPEG, GIF, BMP, WebP
Convenient Upload: Drag & drop files, clipboard paste (Ctrl+V)
Unlimited Processing: Support for any size images, automatic intelligent processing
🌐 Interface Preview
Web UI Interface (v2.5.0 - Desktop Application Support)
Web UI Interface - Supports desktop application and Web interface, providing prompt management, auto-submit, session tracking and other smart features
Desktop Application Interface (v2.5.0 New Feature)
Desktop Application - Native cross-platform desktop application based on Tauri framework, supporting Windows, macOS, Linux with exactly the same functionality as Web UI
Shortcut Support
Ctrl+Enter(Windows/Linux)/Cmd+Enter(macOS):Submit feedback (both main keyboard and numeric keypad supported)Ctrl+V(Windows/Linux)/Cmd+V(macOS):Direct paste clipboard imagesCtrl+I(Windows/Linux)/Cmd+I(macOS):Quick focus input box (Thanks @penn201500)
🚀 Quick Start
1. Installation & Testing
# Install uv (if not already installed)
pip install uv2. Configure MCP
Basic Configuration (suitable for most users):
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"autoApprove": ["interactive_feedback"]
}
}
}Advanced Configuration (requires custom environment):
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"env": {
"MCP_DEBUG": "false",
"MCP_WEB_HOST": "127.0.0.1",
"MCP_WEB_PORT": "8765",
"MCP_LANGUAGE": "en"
},
"autoApprove": ["interactive_feedback"]
}
}
}Desktop Application Configuration (v2.5.0 new feature - using native desktop application):
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"env": {
"MCP_DESKTOP_MODE": "true",
"MCP_WEB_HOST": "127.0.0.1",
"MCP_WEB_PORT": "8765",
"MCP_DEBUG": "false"
},
"autoApprove": ["interactive_feedback"]
}
}
}⚠️ Desktop application maintenance status (since v2.8.0)
The desktop application is maintenance-only: no new features (always-on-top, staying resident, keeping the window after submit), only security fixes and "cannot launch at all" compatibility fixes; it is scheduled for removal in v3, and the release notes will name the last version that still ships the desktop binaries. Why: it is a thin Tauri shell around the Web UI, yet it accounts for ~80% of the package size, needs three-platform CI plus code signing, and most reported issues are platform-compatibility problems that cannot be reproduced in CI (antivirus false positives, glibc, Gatekeeper, high DPI, multi-monitor).
To keep using desktop mode: change the
argsin your IDE's MCP configuration frommcp-feedback-enhanced@latestto a pinned version (e.g.mcp-feedback-enhanced@2.8.0) and keepMCP_DESKTOP_MODE=true. Since 2.8.0, if the desktop shell cannot start (quarantined by antivirus, glibc too old, blocked by Gatekeeper, exits with an error right after launch), that call automatically opens the browser instead and prints the URL to stderr rather than waiting silently until the timeout; restarting the MCP server retries the desktop shell. Do not pin to 2.6.0 or earlier (unauthenticated command execution, see SECURITY.md).To switch to web mode: remove
MCP_DESKTOP_MODE. The functionality is identical, the tab stays open after submit and refreshes on the next call. If you want a standalone window, open the feedback page in Chrome/Edge and choose "Install as app" — but that is a browser feature, not an equivalent of the desktop shell: the backend is still started by the MCP call, once the app window is closed the next call opens a normal browser tab rather than the app window, the app must be reinstalled if the port changes, and notification permission must be granted again; use an OS-level tool for always-on-top.
Configuration File Examples:
Desktop Mode: examples/mcp-config-desktop.json
Web Mode: examples/mcp-config-web.json
3. Prompt Engineering Setup
For optimal results, add the following rules to your AI assistant:
# MCP Interactive Feedback Rules
follow mcp-feedback-enhanced instructions⚙️ Advanced Settings
Environment Variables
Variable | Purpose | Values | Default |
| Debug mode |
|
|
| Web UI host binding | IP address or hostname |
|
| Web UI port |
|
|
| Desktop application mode |
|
|
| Force UI language |
| Auto-detect |
MCP_WEB_HOST Explanation:
127.0.0.1(default): Local access only — keep this setting0.0.0.0: Binds all interfaces. ⚠️ Not recommended: the Web UI and/wsendpoint have no authentication, so anyone who can reach the port can read session content (including project paths and AI summaries) and submit feedback. Use SSH port forwarding instead (see Common Issues).
MCP_LANGUAGE Explanation:
Used to force the interface language, overriding automatic system detection
Supported language codes:
zh-TW: Traditional Chinesezh-CN: Simplified Chineseen: English
Language detection priority:
MCP_LANGUAGEenvironment variable (highest priority; when set, the language picker in the UI only applies to the current session)User-saved language settings in the interface
System environment variables (LANG, LC_ALL, etc.)
System default language
Fallback to default language (Traditional Chinese)
Testing Options
# Version check
uvx mcp-feedback-enhanced@latest version # Check version
# Interface testing
uvx mcp-feedback-enhanced@latest test --web # Test Web UI (auto continuous running)
uvx mcp-feedback-enhanced@latest test --desktop # Test desktop application (v2.5.0 new feature)
# Debug mode
MCP_DEBUG=true uvx mcp-feedback-enhanced@latest test
# Specify language for testing
MCP_LANGUAGE=en uvx mcp-feedback-enhanced@latest test --web # Force English interface
MCP_LANGUAGE=zh-TW uvx mcp-feedback-enhanced@latest test --web # Force Traditional Chinese
MCP_LANGUAGE=zh-CN uvx mcp-feedback-enhanced@latest test --web # Force Simplified ChineseDeveloper Installation
git clone https://github.com/Minidoracat/mcp-feedback-enhanced.git
cd mcp-feedback-enhanced
uv syncLocal Testing Methods
# Functional testing
make test-func # Standard functional testing
make test-web # Web UI testing (continuous running)
make test-desktop-func # Desktop application functional testing
# Or use direct commands
uv run python -m mcp_feedback_enhanced test # Standard functional testing
uvx --no-cache --with-editable . mcp-feedback-enhanced test --web # Web UI testing (continuous running)
uvx --no-cache --with-editable . mcp-feedback-enhanced test --desktop # Desktop application testing
# Desktop application build (v2.5.0 new feature)
make build-desktop # Build desktop application (debug mode)
make build-desktop-release # Build desktop application (release mode)
make test-desktop # Test desktop application
make clean-desktop # Clean desktop build artifacts
# Unit testing
make test # Run all unit tests
make test-fast # Fast testing (skip slow tests)
make test-cov # Test and generate coverage report
# Code quality checks
make check # Complete code quality check
make quick-check # Quick check and auto-fixTesting Descriptions
Functional Testing: Test complete MCP tool functionality workflow
Unit Testing: Test individual module functionality
Coverage Testing: Generate HTML coverage report to
htmlcov/directoryQuality Checks: Include linting, formatting, type checking
🆕 Version History
📋 Complete Version History: RELEASE_NOTES/CHANGELOG.en.md
Latest Version Highlights (v2.6.0)
📊 Session Export Feature: Support exporting session records to multiple formats for easy sharing and archiving
⏸️ Auto-commit Control: Added pause and resume buttons for better control over auto-commit timing
🔔 System Notifications: System-level notifications for important events with real-time alerts
⏱️ Session Timeout Optimization: Redesigned session management with more flexible configuration options
🌏 I18n Enhancement: Refactored internationalization architecture with full multilingual support for notifications
🎨 UI Simplification: Significantly simplified user interface for improved user experience
🐛 Common Issues
🌐 SSH Remote Environment Issues
Q: Browser cannot launch or access in SSH Remote environment A: Use SSH port forwarding (secure, nothing exposed):
Use default configuration (
MCP_WEB_HOST:127.0.0.1)Set up SSH port forwarding:
VS Code Remote SSH: Press
Ctrl+Shift+P→ "Forward a Port" → Enter8765Cursor SSH Remote: Manually add port forwarding rule (port 8765)
Open in local browser:
http://localhost:8765
⚠️ Older READMEs recommended
MCP_WEB_HOST=0.0.0.0to expose the service directly. No longer recommended: the Web UI and/wsendpoint have no authentication, so binding publicly lets anyone on the network read your session and submit feedback.
For detailed solutions, refer to: SSH Remote Environment Usage Guide
Q: Why am I not receiving new MCP feedback? A: Likely a WebSocket connection issue. Solution: Directly refresh the browser page.
Q: Why isn't MCP being called? A: Please confirm MCP tool status shows green light. Solution: Repeatedly toggle MCP tool on/off, wait a few seconds for system reconnection.
Q: Augment cannot start MCP A: Solution: Completely close and restart VS Code or Cursor, reopen the project.
🔧 General Issues
Q: How to use desktop application?
A: v2.5.0 introduces cross-platform desktop application support. Set "MCP_DESKTOP_MODE": "true" in MCP configuration to enable:
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"env": {
"MCP_DESKTOP_MODE": "true",
"MCP_WEB_PORT": "8765"
},
"autoApprove": ["interactive_feedback"]
}
}
}Configuration File Example: examples/mcp-config-desktop.json
Q: How to use legacy PyQt6 GUI interface?
A: v2.4.0 completely removed PyQt6 GUI dependencies. To use legacy GUI, specify v2.3.0 or earlier: uvx mcp-feedback-enhanced@2.3.0
Note: Legacy versions don't include new features (prompt management, auto-submit, session management, desktop application, etc.).
Q: "Unexpected token 'D'" error appears
A: Debug output interference. Set MCP_DEBUG=false or remove the environment variable.
Q: Chinese character garbled text
A: Fixed in v2.0.3. Update to latest version: uvx mcp-feedback-enhanced@latest
Q: Window disappears or positioning errors in multi-screen environment A: Fixed in v2.1.1. Go to "⚙️ Settings" tab, check "Always show window at primary screen center" to resolve. Especially suitable for T-shaped screen arrangements and other complex multi-screen configurations.
Q: Image upload failure A: Check file format (PNG/JPG/JPEG/GIF/BMP/WebP). System supports any size image files.
Q: Web UI cannot start A: Check firewall settings or try using different ports.
Q: UV Cache occupies too much disk space
A: Due to frequent use of uvx commands, cache may accumulate to tens of GB. Regular cleanup recommended:
# View cache size and detailed information
python scripts/cleanup_cache.py --size
# Preview cleanup content (no actual cleanup)
python scripts/cleanup_cache.py --dry-run
# Execute standard cleanup
python scripts/cleanup_cache.py --clean
# Force cleanup (attempts to close related programs, solving Windows file occupation issues)
python scripts/cleanup_cache.py --force
# Or directly use uv command
uv cache cleanFor detailed instructions, refer to: Cache Management Guide
Q: AI models cannot parse images A: Various AI models (including Gemini Pro 2.5, Claude, etc.) may have instability in image parsing, sometimes correctly recognizing and sometimes unable to parse uploaded image content. This is a known limitation of AI visual understanding technology. Recommendations:
Ensure good image quality (high contrast, clear text)
Try uploading multiple times, retries usually succeed
If parsing continues to fail, try adjusting image size or format
🙏 Acknowledgments
🌟 Support Original Author
Fábio Ferreira - X @fabiomlferreira Original Project: noopstudios/interactive-feedback-mcp
If you find it useful, please:
Design Inspiration
sanshao85 - mcp-feedback-collector
Contributors
penn201500 - GitHub @penn201500
🎯 Auto-focus input box feature (PR #39)
leo108 - GitHub @leo108
🌐 SSH Remote Development Support (
MCP_WEB_HOSTenvironment variable) (PR #113)
Alsan - GitHub @Alsan
🍎 macOS PyO3 Compilation Configuration Support (PR #93)
fireinice - GitHub @fireinice
📝 Tool Documentation Optimization (LLM instructions moved to docstring) (PR #105)
Community Support
Discord: https://discord.gg/Gur2V67
Issues: GitHub Issues
☕ Support Development
MCP Feedback Enhanced is free and open source. If it saves you time, you can support its development on Ko-fi:
📄 License
MIT License - See LICENSE file for details
📈 Star History
🌟 Welcome to Star and share with more developers!
Available Tools
2 toolsget_system_infoGet System InfoC
獲取系統環境資訊
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing beyond an implicit read ('獲取'). It says nothing about auth requirements, cost, caching, or latency. The output schema covers what is returned, so that omission is excusable, but the rest is a gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single short sentence with no filler, and the purpose is front-loaded. It is under-specified rather than padded, so it earns credit for economy while leaving room to say more.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with an output schema, the minimal description is arguably adequate to invoke it correctly — the agent knows it returns system environment info. However, with no annotations and no indication of scope or read semantics, it is only just viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so the baseline is 4 per the rubric. There is nothing for the description to clarify beyond what the empty schema already establishes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description '獲取系統環境資訊' (get system environment information) is essentially a translation of the tool name and title, adding no specificity about what environment aspects are returned (OS, version, memory, runtime). It uses a verb+resource but conveys no more than the name already does, placing it in tautology territory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no when-to-use guidance, no prerequisites, and no mention of the sibling tool interactive_feedback as an alternative. The agent gets no context for deciding whether this tool is relevant to its task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
interactive_feedbackInteractive FeedbackB
收集用戶的互動回饋,支援文字和圖片
此工具使用 Web UI 介面收集用戶回饋,支援智能環境檢測。
用戶可以:
執行命令來驗證結果
提供文字回饋
上傳圖片作為回饋
查看 AI 的工作摘要
調試模式:
設置環境變數 MCP_DEBUG=true 可啟用詳細調試輸出
生產環境建議關閉調試模式以避免輸出干擾
| Name | Required | Description | Default |
|---|---|---|---|
| summary | No | AI 工作完成的摘要說明 | 我已完成了您請求的任務。 |
| timeout | No | 等待用戶回饋的超時時間(秒) | |
| project_directory | No | 專案目錄路徑 | . |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It usefully discloses the Web UI mechanism, the four user interaction paths, and the MCP_DEBUG environment toggle, but omits key traits such as whether the call blocks until user input, what happens on timeout, and any permission or environment requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Purpose is front-loaded and the numbered list is scannable, but there is redundancy (the opening line already says text and images, then repeats it in the list) and the debug-mode section is tangential operational advice rather than tool-selection content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No annotations and no output schema, so the description must fully orient the agent. It explains the interaction flow adequately but does not describe the return shape (feedback text, image data, command results) or the consequence of the timeout, leaving gaps for a zero-required-parameter interactive tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all three parameters (summary, timeout, project_directory) are already documented in the schema, establishing the baseline of 3. The description mentions the summary concept but adds no format, unit, or edge-case detail beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource: collecting user interactive feedback via a Web UI, supporting text and images. This clearly distinguishes it from the only sibling get_system_info. It stops short of explicitly naming that sibling, but the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (collecting feedback after an AI task, letting users verify results) but never states when to use this tool versus alternatives or any preconditions. Usage must be inferred from the enumerated user actions rather than an explicit trigger condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v2.4.2- First observed
get_system_info - First observed
interactive_feedback
TDQS
Scored across 2 tools
The two tools serve clearly different purposes: interactive_feedback collects user feedback, while get_system_info retrieves environment details. There is no overlap or ambiguous boundary between them.
Both tools use snake_case, which is consistent. However, get_system_info follows a verb_noun pattern while interactive_feedback is adjective_noun, a minor deviation from a strict convention.
With only two tools, the server is very thin even for a focused feedback utility. It provides one primary action and one auxiliary utility, landing at the borderline of 'too few'.
The surface covers feedback collection (text, images, command validation) and system information. It lacks obvious lifecycle operations such as listing, retrieving, or clearing past feedback, which may limit real-world use.
Related MCP Connectors
Give any AI agent a way to ask a person — for approval, a decision, an answer or a review.
Control a real browser from AI agents: navigate, click, type, screenshot, read the page.
Comment on AI-generated webpages; feedback flows back to your coding agent. Free, MIT, local-first.
Let your AI agents ask you for decisions and get your answers back, from your phone or the web.
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