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mcp-feedback-enhanced-gw

MCP Feedback Enhanced

🌐 Language / 語言切換: English | 繁體中文 | 简体中文

Original Author: Fábio Ferreira | Original ProjectEnhanced Fork: nullmastermind 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

  1. AI Callmcp-feedback-enhanced-gw tool

  2. Interface Launch → Auto-open desktop application or browser interface (based on configuration)

  3. Smart Interaction → Prompt selection, text input, image upload, auto-submit

  4. Real-time Feedback → WebSocket connection delivers information to AI instantly

  5. Session Tracking → Auto-record session history and statistics

  6. 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 images

  • Ctrl+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 uv

2. Configure MCP

Basic Configuration (suitable for most users):

{
  "mcpServers": {
    "mcp-feedback-enhanced-gw": {
      "command": "uvx",
      "args": ["mcp-feedback-enhanced-gw@latest"],
      "timeout": 600,
      "autoApprove": ["interactive_feedback"]
    }
  }
}

Advanced Configuration (requires custom environment):

{
  "mcpServers": {
    "mcp-feedback-enhanced-gw": {
      "command": "uvx",
      "args": ["mcp-feedback-enhanced-gw@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-gw": {
      "command": "uvx",
      "args": ["mcp-feedback-enhanced-gw@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:

3. Prompt Engineering Setup

For optimal results, add the following rules to your AI assistant:

# MCP Interactive Feedback Rules

follow mcp-feedback-enhanced-gw instructions

⚙️ Advanced Settings

Environment Variables

Variable

Purpose

Values

Default

MCP_DEBUG

Debug mode

true/false

false

MCP_WEB_HOST

Web UI host binding

IP address or hostname

127.0.0.1

MCP_WEB_PORT

Web UI port

1024-65535

8765

MCP_DESKTOP_MODE

Desktop application mode

true/false

false

MCP_LANGUAGE

Force UI language

zh-TW/zh-CN/en

Auto-detect

MCP_WEB_HOST Explanation:

  • 127.0.0.1 (default): Local access only, higher security

  • 0.0.0.0: 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 Chinese

    • zh-CN: Simplified Chinese

    • en: English

  • Language detection priority:

    1. User-saved language settings in the interface (highest priority)

    2. MCP_LANGUAGE environment variable

    3. System environment variables (LANG, LC_ALL, etc.)

    4. System default language

    5. Fallback to default language (Traditional Chinese)

Testing Options

# Version check
uvx mcp-feedback-enhanced-gw@latest version       # Check version

# Interface testing
uvx mcp-feedback-enhanced-gw@latest test --web    # Test Web UI (auto continuous running)
uvx mcp-feedback-enhanced-gw@latest test --desktop # Test desktop application (v2.5.0 new feature)

# Debug mode
MCP_DEBUG=true uvx mcp-feedback-enhanced-gw@latest test

# Specify language for testing
MCP_LANGUAGE=en uvx mcp-feedback-enhanced-gw@latest test --web    # Force English interface
MCP_LANGUAGE=zh-TW uvx mcp-feedback-enhanced-gw@latest test --web  # Force Traditional Chinese
MCP_LANGUAGE=zh-CN uvx mcp-feedback-enhanced-gw@latest test --web  # Force Simplified Chinese

Developer Installation

git clone https://github.com/nullmastermind/mcp-feedback-enhanced.git
cd mcp-feedback-enhanced
uv sync

Local 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-gw test --web   # Web UI testing (continuous running)
uvx --no-cache --with-editable . mcp-feedback-enhanced-gw 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-fix

Testing Descriptions

  • Functional Testing: Test complete MCP tool functionality workflow

  • Unit Testing: Test individual module functionality

  • Coverage Testing: Generate HTML coverage report to htmlcov/ directory

  • Quality 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-gw": {
      "command": "uvx",
      "args": ["mcp-feedback-enhanced-gw@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)

  1. Use default configuration (MCP_WEB_HOST: 127.0.0.1)

  2. Set up SSH port forwarding:

    • VS Code Remote SSH: Press Ctrl+Shift+P → "Forward a Port" → Enter 8765

    • Cursor SSH Remote: Manually add port forwarding rule (port 8765)

  3. 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-gw": {
      "command": "uvx",
      "args": ["mcp-feedback-enhanced-gw@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-gw@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-gw@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 clean

For 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:

  1. Ensure good image quality (high contrast, clear text)

  2. Try uploading multiple times, retries usually succeed

  3. 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_HOST environment 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

📄 License

MIT License - See LICENSE file for details

📈 Star History

Star History Chart


🌟 Welcome to Star and share with more developers!

Available Tools

2 tools
get_system_infoA

Get system environment information

Returns: str: System information in JSON format

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must fully disclose behavioral traits. It mentions that the return value is a JSON string, but it omits whether the operation is read-only, any required permissions, or potential side effects. This lack of disclosure is a notable gap for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is succinct and front-loaded, with the core purpose stated first and return details following. It contains no extraneous content and is appropriately sized for a simple, no-argument tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and an output schema, the description covers the essential information: it retrieves system info and returns it as JSON. It does not specify the exact fields or scope of 'system information', but the output schema likely provides that detail. A brief mention of data scope or usage context would make it more complete, but it is largely sufficient for its simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, which sets a baseline of 4. The description implicitly confirms that no inputs are needed, and the empty schema corroborates this. No additional parameter semantics are required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves system environment information, using a specific verb ('Get') and resource. It is well-differentiated from the sibling tool 'interactive_feedback', which serves a completely different purpose. Even though the name already implies this, the added return format detail strengthens clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as conditions, prerequisites, or comparisons with 'interactive_feedback'. It simply states what it does, leaving the agent without context for selecting this tool over others.

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:

  1. During any process, task, or conversation, whether asking, replying, or completing phased tasks, you must call this tool to ask for feedback.

  2. Unless receiving termination instructions, all steps must repeatedly call this tool.

  3. 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.

  4. Only when the user explicitly indicates "end" or "no more interaction needed" can you stop calling this tool, and the process is considered complete.

  5. 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.

ParametersJSON Schema
NameRequiredDescriptionDefault
summaryNoSummary of AI work completedI have completed the task you requested.
timeoutNoTimeout in seconds for waiting user feedback
project_directoryNoProject directory path.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of disclosure. It reveals key behavioral traits: the tool must be called repeatedly, waits for user feedback with a timeout, and requires the agent to adjust based on feedback. It also instructs to provide the project directory for user awareness. However, it does not specify what happens when the timeout expires or the exact output format, though an output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a heading and numbered rules, but it contains redundancy: rules 1 and 2 both mandate calling the tool frequently, and rule 3 repeats the need to call again after feedback. It could be more concise while retaining the same information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three simple parameters and an output schema present, the description is largely complete. It thoroughly explains when and how to use the tool, what to include, and termination conditions. Minor gaps include lack of detail on timeout expiry behavior, but overall it provides sufficient context for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, meaning all three parameters (summary, timeout, project_directory) are already documented in the schema. The description adds minimal extra meaning by explicitly stating that project_directory should be provided so the user knows what was done, but it does not enrich the semantics of summary or timeout beyond their schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as an interactive feedback collection tool for LLM agents, with a specific purpose of soliciting and adjusting based on user feedback. It distinguishes from the sibling tool get_system_info by focusing on feedback collection rather than system information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'USAGE RULES' with detailed directives: when to call (during any process, after receiving feedback), when to stop (when user says 'end' or 'no more interaction needed'), and what to include (summary and project_directory). This is exceptional guidance that leaves little ambiguity for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

A3.6/5.0
Disambiguation5/5

The two tools have completely unrelated purposes: one handles interactive feedback collection, the other retrieves system information. There is no overlap or confusion between them.

Naming Consistency4/5

Both names use snake_case, but 'interactive_feedback' uses an adjective+noun pattern while 'get_system_info' follows verb+noun. This minor inconsistency prevents a perfect score.

Tool Count2/5

With only two tools, the server feels underdeveloped. The name suggests a feedback-focused gateway, yet only one tool is feedback-related and the other is unrelated system info.

Completeness2/5

The feedback tool is described extensively but there is no explicit end tool, retrieval of prior feedback, or integration with the system info tool. The overall domain coverage is sparse and ad hoc.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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