mcp-feedback-enhanced
This MCP server inserts human-in-the-loop feedback checkpoints into AI agent workflows via a Web UI or desktop app.
Collect interactive feedback (
interactive_feedback): submits a work summary and project directory, then waits for the user's response (timeout 60–86400s, default 600, server-clamped) and returns it to the agent so it can adjust behavior.Rich feedback input: prompt selection, free-text input, image upload (PNG/JPG/JPEG/GIF/BMP/WebP via drag-drop or paste), and auto-timed submit (1–86400s with pause/resume/cancel).
Dual interface: lightweight browser-based Web UI (primary) or a Tauri desktop app, auto-opened based on configuration.
Real-time delivery: WebSocket connection pushes feedback to the AI instantly, with connection status monitoring and auto-reconnect.
Session management: local history storage, export to JSON/CSV/Markdown, usage statistics, and configurable session timeouts.
System info (
get_system_info): returns JSON describing the host environment for diagnostics.Environment flexibility: works locally, in SSH remote setups (recommended via SSH port forwarding), and WSL; host/port/debug/language configurable via env vars.
Multi-language UI: Traditional Chinese, Simplified Chinese, and English with instant switching.
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-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:
🌐 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
📄 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 InfoB
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 behavioral burden. It discloses only the return type (a JSON string) and says nothing about side effects, permissions, rate limits, or whether this is read-only.
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 definition is short and front-loads the purpose before a return-type note. Both parts are brief, though the 'Returns' line is somewhat redundant given an output schema already exists.
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 an output schema present, the description need not explain return values, and it correctly avoids doing so in depth. For a zero-parameter read tool it is minimally adequate, but it omits any behavioral context about the environment it inspects.
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 there is nothing to document and the baseline is 4. The description does not need to compensate for any parameter semantics.
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 states a specific verb and resource ('獲取系統環境資訊' = get system environment info), making the tool's function immediately clear. However, it offers no differentiation from its sibling interactive_feedback, though the two are functionally distinct.
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 guidance on when to use this tool, no prerequisites, and no mention of alternatives. The agent is left to infer usage purely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
interactive_feedbackInteractive 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, or this tool returns a "no user response" result, 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.
You can stop calling this tool, and the process is considered complete, when the user explicitly indicates "end" or "no more interaction needed", or when this tool returns a "no user response" result (the wait timed out or the user closed the feedback UI) — in that case finish the task and do NOT call this tool again.
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?
No annotations are provided, so the description carries the full burden and does so well: it discloses that the call blocks awaiting user input, that a 'no user response' outcome is possible via timeout or UI closure, and effectively that the tool is expected to be called repeatedly. The main omission is that it never states the timeout parameter's server-side clamping or the default wait duration, though the schema covers the range.
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?
Front-loaded with a one-line purpose followed by compact numbered rules, which is a good structure for procedural guidance. It loses a point because rules 2, 3, and 4 restate overlapping call/stop conditions, adding redundancy without new information.
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 no output schema, the description must convey return semantics itself, and it does: it names the 'no user response' return state and the timeout/closure causes behind it. Combined with the exhaustive call-lifecycle rules, an agent has everything needed to invoke and terminate correctly.
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 the baseline is 3 and the schema already documents all three parameters including the 60–86400 clamping behavior. Rule 5 adds genuine intent for summary and project_directory ('let user know what you have done to provide feedback for next step'), but timeout is never mentioned in the description, so the added value is modest.
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 a specific verb and resource ('interactive feedback collection tool') and scopes it to LLM agents, which cleanly separates it from the only sibling, get_system_info. It stops short of explicitly naming the sibling or contrasting the two, so it lands at 4 rather than 5.
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?
Five numbered rules give explicit when-to-call ('all steps must repeatedly call this tool'), when-to-stop ('user explicitly indicates end... or a no user response result'), and the post-stop behavior ('finish the task and do NOT call this tool again'). This is unusually complete routing guidance and leaves essentially nothing to inference.
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.
1 tool update
v2.6.2- Changed
interactive_feedback2 fields changed- changed
Input schema / properties / timeout / descriptionPrevious value: -"等待用戶回饋的超時時間(秒)"New value: +"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。" - changed
Output schema / (root)Previous value: -{ - "properties": { - "result": { - "items": {}, - "type": "array" - } - }, - "required": [ - "result" - ], - "type": "object", - "x-fastmcp-wrap-result": true -}New value: +null
2 tool updates
v2.6.0- First observed
get_system_info - First observed
interactive_feedback
TDQS
Scored across 2 tools
The two tools are completely distinct: interactive_feedback is for user interaction, while get_system_info retrieves system information. There is no overlap in purpose.
Both use snake_case, but one is a noun phrase (interactive_feedback) and the other is a verb phrase (get_system_info), showing minor inconsistency in naming convention.
With only two tools, the server is very thin; while both are useful, it feels under-scoped for a feedback-enhanced purpose and could benefit from additional related utilities.
The interactive feedback tool covers a feedback loop but lacks a dedicated tool to submit feedback or query feedback history. get_system_info is unrelated to the core feedback domain, suggesting incomplete integration.
Maintenance
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