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mcp-feedback-enhanced

by Minidoracat
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JavaScript
Hybrid

Interactive Feedback

interactive_feedback

Collect user feedback during AI agent tasks to guide next steps, adjust behavior, and confirm task completion.

Instructions

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, or this tool returns a "no user response" result, 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. 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.

  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.

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）。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_directoryNo專案目錄路徑.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.6.2
    • changedInput schema / properties / timeout / description
      Previous 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。"
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "items": {},
-      "type": "array"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "type": "object",
-  "x-fastmcp-wrap-result": true
-}New value: +null
  2. First observedv2.6.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries 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.

Conciseness4/5

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.

Completeness5/5

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.

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 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.

Purpose4/5

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.

Usage Guidelines5/5

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.

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