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mcp-feedback-ultra

by YangHang0210
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JavaScript

MCP Feedback Ultra

MCP Feedback Ultra 是一个用于 AI 辅助开发的交互式反馈服务器，支持 Web UI 和桌面应用双界面模式。

原始项目: noopstudios/interactive-feedback-mcp

核心特性

  • 双界面支持：Web UI（浏览器）+ 桌面应用（原生应用）

  • 智能环境检测：自动识别 SSH Remote、WSL 等特殊环境

  • 交互式反馈：AI 完成任务后主动收集用户反馈

  • 跨平台支持：Windows、macOS、Linux

Related MCP server: mcp-feedback-enhanced-gw

快速开始

安装

uvx mcp-feedback-ultra@latest

MCP 配置

在 Cursor 的 MCP 配置文件中添加：

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

环境变量（可选）

变量

说明

默认值

MCP_DEBUG

调试模式

false

MCP_WEB_HOST

Web UI 绑定地址

127.0.0.1

MCP_WEB_PORT

Web UI 端口

8765

MCP_DESKTOP_MODE

启用桌面应用模式

false

使用方式

Web UI 模式（默认）

{
  "mcpServers": {
    "mcp-feedback-ultra": {
      "command": "uvx",
      "args": ["mcp-feedback-ultra@latest"]
    }
  }
}

桌面应用模式

{
  "mcpServers": {
    "mcp-feedback-ultra": {
      "command": "uvx",
      "args": ["mcp-feedback-ultra@latest"],
      "env": {
        "MCP_DESKTOP_MODE": "true"
      }
    }
  }
}

开发

本地测试

# 克隆项目
git clone https://github.com/YangHang0210/mcp-feedback-ultra.git
cd mcp-feedback-ultra

# 安装依赖
uv sync --dev

# 测试 Web UI
uv run python -m mcp_feedback_ultra test --web

# 测试桌面应用
uv run python -m mcp_feedback_ultra test --desktop

常见问题

SSH Remote 环境无法访问？

设置 MCP_WEB_HOST 为 0.0.0.0 允许远程访问：

{
  "env": {
    "MCP_WEB_HOST": "0.0.0.0",
    "MCP_WEB_PORT": "8765"
  }
}

然后在本地浏览器访问：http://[远程IP]:8765

许可证

MIT License

链接

Available Tools

2 tools
get_system_infoA

獲取系統环境資訊

Returns: str: JSON 格式的系統資訊

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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It only mentions the return format (JSON string) but does not disclose whether the operation is read-only, if it has side effects, or requires any permissions. For a tool that retrieves system info, the lack of explicit safety traits is a significant omission.

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 extremely concise, using only two brief clauses to state the purpose and return type. It is front-loaded with the core function and provides minimal but necessary detail. No unnecessary words are included, and it adheres to the principle that every sentence should earn its place.

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?

Given the tool's simplicity (zero parameters, no side effects likely), the description is adequate. It states what the tool does and the return format. However, it could be more complete by explicitly indicating that it is a safe, read-only operation, which would reassure an agent. Since this is not stated, it falls slightly short of a perfect score.

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, and the input schema coverage is 100% (empty). The description adds no parameter-specific information, but none is needed. The baseline for zero parameters is 4, and this description meets it without requiring further elaboration.

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's function: '獲取系統环境資訊' (get system environment information). The verb '獲取' (get) and resource '系統資訊' (system info) are specific, and the tool is easily distinguished from the sibling 'interactive_feedback'.

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?

There is no guidance on when to use this tool or when not to. The description only provides a basic statement of what it does, without any context, prerequisites, or alternatives. This is a clear gap in usage direction.

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.

IMPORTANT: The response may include a [NEW TASK] prefix. This means the user explicitly wants to switch to a completely new task. When you see [NEW TASK], you should: (1) deprioritize any ongoing work, (2) clear your TODO list, and (3) focus entirely on the new request that follows.

Args: project_directory: Project directory path for context summary: Summary of AI work completed for user review timeout: Timeout in seconds for waiting user feedback (default: 600 seconds)

Returns: list: List containing TextContent and MCPImage objects representing user feedback

ParametersJSON Schema
NameRequiredDescriptionDefault
summaryNoAI 工作完成的摘要說明我已完成了您請求的任務。
timeoutNo等待用戶反馈的超時時間（秒）
project_directoryNo项目目錄路徑.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It reveals mandatory call patterns, stopping conditions, reaction to non-empty feedback, and [NEW TASK] handling—going far beyond a simple tool summary.

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

Conciseness4/5

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

The description is longer than typical but well-organized with sections and numbered rules. Some repetition exists (e.g., 'must call this tool' repeated across rules), but the structure makes the behavioral mandates clear and easy to follow.

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

Completeness5/5

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

The tool has no output schema, but the description explicitly states the return type (list of TextContent and MCPImage). It fully covers usage rules, argument purposes, default timeout, and edge cases like [NEW TASK], making it self-contained for an agent.

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%, so the baseline is 3. The description adds some context—e.g., project_directory is for user context and summary is for user review—but largely restates information already present in the input schema defaults and descriptions.

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 identifies the tool as an 'Interactive feedback collection tool for LLM agents,' specifying its resource (user feedback) and purpose. It is sharply distinguished from the sibling tool get_system_info, which is unrelated.

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?

Contains explicit USAGE RULES with numbered steps detailing when to call the tool (during any process, after feedback, until termination), when to stop ('end' or 'no more interaction needed'), and how to handle [NEW TASK] prefixes. This is exemplary guidance.

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. 2 tool updatesv2.8.0
    • First observedget_system_info
    • First observedinteractive_feedback
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TDQS

A3.9/5.0

Scored across 2 tools

Disambiguation5/5

The two tools serve completely different purposes—feedback collection and system information—so there is no risk of misselection. An agent would never confuse them.

Naming Consistency3/5

Both tools use snake_case, but the patterns differ: 'get_system_info' follows a verb_noun style, while 'interactive_feedback' is adjective_noun. This inconsistency makes the naming slightly less predictable.

Tool Count3/5

With only two tools, the set is minimal and borderline appropriate. The inclusion of an unrelated system utility alongside the core feedback tool dilutes the server's focus.

Completeness4/5

The interactive_feedback tool appears to cover the core feedback loop with its built-in usage rules, making the feedback process self-contained. However, there are no supporting tools for managing or analyzing feedback, and the system info tool doesn't contribute to the domain.

Maintenance

ActivityNo data
ResponsivenessNo issues

Resources

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