mcp-feedback-enhanced
Click on "Install 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-enhancedask user for confirmation before committing changes"
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 Enhanced
🎯 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:
🖥️ Desktop Application: Native cross-platform desktop experience, supporting Windows, macOS, Linux
🌐 Web UI: No GUI dependencies required, suitable for remote and WSL environments
🔧 Flexible Deployment: Choose the most suitable interface mode based on environment requirements
📦 Unified Functionality: Both interfaces provide exactly the same functional experience
🖥️ Desktop Application: v2.5.0 introduces cross-platform desktop application support based on Tauri framework, supporting Windows, macOS, and Linux platforms with native desktop 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"]
}
}
}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:
User-saved language settings in the interface (highest priority)
MCP_LANGUAGEenvironment variableSystem 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
📄 License
MIT License - See LICENSE file for details
📈 Star History
🌟 Welcome to Star and share with more developers!
Available Tools
2 toolsget_system_infoA
獲取系統環境資訊
Returns: str: JSON 格式的系統資訊
| 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 transparency burden. It does disclose the return format (JSON) but does not explicitly state side-effect-free behavior or other potential behavioral traits like caching or network dependence.
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?
The description is extremely concise: two short lines with no filler. It front-loads the action and resource, followed by the return type.
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?
With zero parameters and the presence of an output schema, the description is sufficient. It states the tool's purpose and return format, making it complete for a simple getter.
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 has zero parameters, so there is no parameter documentation needed. The baseline for 0 parameters is 4, and the description adds no unnecessary information.
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?
Description clearly states the action ('獲取' / get) and the resource ('系統環境資訊' / system environment information). This distinguishes it from the sibling tool 'interactive_feedback', which is about interactive feedback.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or why one might choose this over 'interactive_feedback'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
interactive_feedbackA
Interactive feedback collection tool for LLM agents.
USAGE RULES:
During any process, task, or conversation, whether asking, replying, or completing phased tasks, you must call this tool to ask for feedback.
Unless receiving termination instructions, all steps must repeatedly call this tool.
Whenever user feedback is received, if the feedback content is not empty, you must call this tool again and adjust behavior based on the feedback content.
Only when the user explicitly indicates "end" or "no more interaction needed" can you stop calling this tool, and the process is considered complete.
You should summarize what have done, and provide project directory through args to let user know what you have done to provide feedback for next step.
| 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)。有效範圍 60–86400;超出範圍會由伺服器端鉗制,因為使用者需要實際可用的回饋時間。 | |
| 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 burden of behavioral disclosure. It does reveal the looping behavior, the need to adjust based on feedback, and the explicit exit condition. However, it never clearly states what the tool returns, whether the call blocks until user feedback, or what to do when feedback is empty or times out.
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?
The description is front-loaded with a clear purpose line and the usage rules are organized as a numbered list, which helps parsing. However, the rules are repetitive: rules 1, 2, and 3 all restate the need to call the tool repeatedly, just with different framing. The content is useful but could be trimmed.
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?
The tool has no annotations and no output schema, so the description must explain the full invocation contract. It covers the call cadence, the summary/project-directory purpose, and the exit condition. It is incomplete, though, because it does not describe the tool's return payload or define the behavior when feedback is empty or the call times out.
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 summary, timeout, and project_directory are already defined in the input schema. The description adds some context by saying the project directory should be provided so the user knows what was done, but it does not add meaningful semantics for the timeout or summary parameters beyond what the schema already 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?
The opening line states the tool is an interactive feedback collection tool for LLM agents, and the usage rules consistently say the agent must call it to ask for feedback. This makes the verb, resource, and overall intent unambiguous and clearly distinct from the unrelated get_system_info sibling.
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 explicitly defines when to call the tool (during any process/task/conversation, repeatedly at every step), when to call it again (after non-empty feedback), and when to stop (only on explicit end/no-more-interaction). It does not discuss alternatives, but the only sibling is an unrelated system-info tool, so the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools serve completely different purposes: one is for interactive feedback collection and the other for retrieving system information. There is no overlap or ambiguity between them.
Naming conventions are mixed: 'interactive_feedback' is an adjective-noun phrase with no verb, while 'get_system_info' follows a verb_noun pattern. Both names are readable, but there is no consistent pattern across the set.
With only two tools, the server feels thin for a general-purpose utility or even a feedback-focused server. The count is borderline but not extreme, as the tools themselves are fairly broad in scope.
The core feedback collection tool is present and well-defined, but the server offers no supplementary tools for managing, retrieving, or analyzing feedback history. The unrelated system info tool does not fill these gaps, leaving the surface slightly incomplete.
Maintenance
Resources
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