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interactive_feedback

Collects user feedback on AI-completed work to confirm progress and adjust next steps, reducing unnecessary tool calls and costs.

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, 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

Input Schema

TableJSON 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)
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.

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