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RealAlexandreAI

mcp-interactive

by RealAlexandreAI
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Python
Hybrid

mcp-interactive

English | 简体中文

MCP 服务器，用于在 AI 辅助开发过程中收集用户的交互式反馈。通过引导 AI 与用户确认而非自行猜测，减少不必要的工具调用，降低平台成本，提高开发效率。

工作流程

  1. AI 调用 interactive_feedback 工具

  2. 自动打开浏览器界面（Web UI）

  3. 用户输入反馈文本、上传截图、选择常用提示词

  4. 通过 WebSocket 实时传递给 AI

  5. AI 根据反馈调整行为或结束任务

Related MCP server: Interactive Feedback MCP

安装

pip install uv

配置

将以下内容添加到 MCP 配置文件：

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

支持的 AI 平台：Cursor | Cline | Windsurf | Augment | Trae

环境变量

变量

说明

默认值

MCP_WEB_HOST

Web UI 监听地址

127.0.0.1

MCP_WEB_PORT

Web UI 端口

9766

MCP_LANGUAGE

界面语言 (zh-CN / en)

自动检测

MCP_DEBUG

调试模式

false

SSH 远程开发时，将 MCP_WEB_HOST 设为 0.0.0.0 以允许远程访问，或使用 SSH 端口转发。

功能

  • 提示词管理 - 常用提示词的增删改查和智能排序

  • 自动定时提交 - 1-86400 秒可配置定时器，支持暂停/恢复

  • 图片上传 - 拖放、粘贴，支持 PNG/JPG/GIF/BMP/WebP

  • 多语言 - 简体中文、English，即时切换

  • WebSocket 实时通信 - 状态监控、自动重连

开发

git clone https://github.com/RealAlexandreAI/mcp-interactive.git
cd mcp-interactive
uv sync

# 测试
make test          # 单元测试
make test-web      # Web UI 测试
uv run python scripts/mcp_stdio_smoke.py  # MCP stdio 握手烟测（initialize/list/call，默认非阻塞）
uv run python scripts/mcp_stdio_smoke.py --exercise-interactive --call-timeout 5

# 代码检查
make check         # 完整检查（lint + format + type）

致谢

License

MIT

Available Tools

1 tool
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.

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
project_directoryNo專案目錄路徑.
summaryNoAI 工作完成的摘要說明我已完成了您請求的任務。
timeoutNo等待用戶回饋的超時時間（秒）

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It explains the tool collects feedback, should be called repeatedly, and returns a list of TextContent and MCPImage objects. It hints at blocking behavior via timeout parameter but does not explicitly state that it waits indefinitely. Overall, transparency is good but not perfect.

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 structured with bold headers and numbered rules, front-loading the purpose. It is not overly verbose but could be more concise. The usage rules are necessary for clarity but add length.

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?

Given no output schema, the description explains return values (list of TextContent and MCPImage). It covers the interactive workflow, repeated calls, and termination condition. The tool's complexity is moderate and the description fully equips an agent to use it correctly.

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?

Schema description coverage is 100%, providing baseline 3. The description adds English context for parameters (project directory, summary, timeout) beyond the schema's Chinese descriptions, helping English-speaking agents. However, it does not add significant new meaning beyond listing them.

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 it is an 'Interactive feedback collection tool for LLM agents,' which precisely defines the tool's purpose. No sibling tools exist, so differentiation is not an issue.

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 (1-5) detailing when to call the tool, how to handle feedback, and when to stop. This gives clear context for the agent to decide when to use it.

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. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.6
    • First observedinteractive_feedback
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TDQS

A4.5/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion between tools.

Naming Consistency5/5

With a single tool, naming consistency is inherently perfect.

Tool Count3/5

A single tool for interactive feedback is borderline; it serves a narrow purpose but might feel thin for broader interactive workflows.

Completeness4/5

The tool covers the core need for iterative feedback collection, though it lacks structured options for feedback types or granular control.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

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  • Interactive Feedback MCP

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  • Copilot Leecher

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  • mcp-feedback-enhanced

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