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 deploying"
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
🎯 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 can consolidate multiple tool calls into a single feedback-oriented request, dramatically reducing platform costs and improving development efficiency.
🌐 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
Related MCP server: mcp-feedback-enhanced
🌟 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
Auto Command Execution (v2.6.0): Automatically execute preset commands after creating new sessions or commits for improved development efficiency
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, higher security0.0.0.0: Allow remote access, suitable for SSH remote development environments
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)
🚀 Auto Command Execution: Automatically execute preset commands after creating new sessions or commits, improving workflow efficiency
📊 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: Two solutions available:
Solution 1: Environment Variable Setting (v2.5.5 Recommended)
Set "MCP_WEB_HOST": "0.0.0.0" in MCP configuration to allow remote access:
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"env": {
"MCP_WEB_HOST": "0.0.0.0",
"MCP_WEB_PORT": "8765"
},
"autoApprove": ["interactive_feedback"]
}
}
}Then open in local browser: http://[remote-host-IP]:8765
Solution 2: SSH Port Forwarding (Traditional Method)
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
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?
The description discloses that it returns a JSON string of system information, but lacks detail on what environment data is included, whether there are any prerequisites, or potential side effects. Since no annotations exist, the description carries the full burden and only partially meets it.
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 exceptionally concise: a single purpose line plus a return-type note. Every word adds value, and the key information is front-loaded. No redundancy or filler.
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?
Given the tool's simplicity (0 parameters), the presence of an output schema, and a clear statement of the return type, the description is almost complete. It only lacks a bit more detail on what 'system environment information' entails, but the output schema likely covers that.
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, and the schema covers 100% of them (none). Per the rubric, a 0-parameter tool gets a baseline of 4, and the description needs no parameter-specific details.
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 clearly states the tool's purpose: '獲取系統環境資訊' (get system environment information). This is a specific verb+resource combination that distinguishes it from the sibling tool interactive_feedback, which is clearly different in function.
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 explicit when-to-use guidance is provided, but the purpose is self-evident and the only sibling tool (interactive_feedback) is unrelated in scope. Usage context is implied rather than stated, which is acceptable for a simple read-only tool.
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) | |
| 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 provided, the description carries the burden of behavioral disclosure. It explains that the tool must be invoked iteratively, that feedback is used to adjust behavior, and that the agent should provide a summary and project directory. However, it does not explicitly state that the tool blocks execution while waiting for feedback, which is implied but not fully disclosed.
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 well-structured with numbered rules and front-loaded with a clear purpose. It is a bit longer than necessary, and rules 1 and 2 overlap in meaning ('during any process...' and 'all steps must repeatedly call this tool'), but each sentence contributes to the overall usage guidance.
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 description is sufficiently complete for a feedback tool: it explains when to call, what to include in arguments, and when to stop. However, it does not describe what happens on timeout, which is a parameter with a default of 3600 seconds but no behavioral explanation for the agent.
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 schema already covers all three parameters with descriptions, giving a baseline of 3. The description adds value by explaining how summary and project_directory should be used in rule 5, which goes beyond the schema definitions by tying them to the agent's feedback loop.
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 clearly states the tool's purpose: 'Interactive feedback collection tool for LLM agents.' This is a specific verb+resource pairing, and it is clearly distinct from the sibling tool get_system_info, which collects system information rather than user 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?
The USAGE RULES section provides explicit, prescriptive guidance: call this tool during any process, repeatedly, until the user explicitly ends. It also tells the agent to summarize work and provide the project directory. This is a clear when-to-use instruction, with a defined stopping condition.
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 entirely different purposes: interactive_feedback collects user feedback, while get_system_info retrieves system environment details. There is no overlap or confusion between them.
The naming conventions are mixed: 'interactive_feedback' uses an adjective-noun structure, while 'get_system_info' uses a verb-noun structure. Although both use snake_case, the lack of a consistent verb-first pattern across the set reduces coherence.
With only two tools, the server feels thin for its stated purpose of enhanced feedback collection. The count is borderline, as it sits at the low end of the acceptable range but may be insufficient for robust workflows.
The domain appears to be feedback collection, but the tool surface is severely limited. There is only one feedback-related tool (interactive_feedback) with no way to retrieve, update, or manage feedback history. The unrelated get_system_info tool does little to round out the domain, leaving significant gaps.
Maintenance
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