Skip to main content
Glama
leenbj

MCP Feedback Enhanced

by leenbj

Interactive Feedback

interactive_feedback

Pause AI tasks to collect text or image feedback through a browser UI, letting users verify results, review work summaries, and submit responses or auto-submit on timeout.

Instructions

收集用戶的互動回饋,支援文字和圖片

此工具使用 Web UI 介面收集用戶回饋,支援智能環境檢測。

用戶可以:

  1. 執行命令來驗證結果

  2. 提供文字回饋

  3. 上傳圖片作為回饋

  4. 查看 AI 的工作摘要

調試模式:

  • 設置環境變數 MCP_DEBUG=true 可啟用詳細調試輸出

  • 生產環境建議關閉調試模式以避免輸出干擾

Input Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.4.2

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the Web UI mechanism, the four user interaction paths, and the MCP_DEBUG environment toggle, but omits key traits such as whether the call blocks until user input, what happens on timeout, and any permission or environment requirements.

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?

Purpose is front-loaded and the numbered list is scannable, but there is redundancy (the opening line already says text and images, then repeats it in the list) and the debug-mode section is tangential operational advice rather than tool-selection content.

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

Completeness3/5

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

No annotations and no output schema, so the description must fully orient the agent. It explains the interaction flow adequately but does not describe the return shape (feedback text, image data, command results) or the consequence of the timeout, leaving gaps for a zero-required-parameter interactive tool.

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 all three parameters (summary, timeout, project_directory) are already documented in the schema, establishing the baseline of 3. The description mentions the summary concept but adds no format, unit, or edge-case detail beyond what the schema provides.

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?

States a specific verb+resource: collecting user interactive feedback via a Web UI, supporting text and images. This clearly distinguishes it from the only sibling get_system_info. It stops short of explicitly naming that sibling, but the purpose is unambiguous.

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

Usage Guidelines3/5

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

The description implies usage (collecting feedback after an AI task, letting users verify results) but never states when to use this tool versus alternatives or any preconditions. Usage must be inferred from the enumerated user actions rather than an explicit trigger condition.

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

Deploy Server

Other Tools