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orime

mcp-feedback-enhanced-community-fix

by orime

interactive_feedback

Collect user feedback during LLM agent tasks to adjust behavior and proceed step-by-step until the user ends the interaction.

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.

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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description bears the full burden. It discloses the iterative feedback loop behavior and mentions timeout, but does not explain what happens on timeout expiry, if the tool is blocking, or any side effects. It adequately sets expectations for the core interaction pattern but lacks some behavioral details.

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?

The description is structured with clear bullet points but is somewhat verbose, repeating 'you must call this tool' multiple times. It could be more concise while retaining the same information.

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?

Given the tool's complexity (interactive loop) and the existence of an output schema, the description covers usage but does not mention what the tool returns or error handling. The output schema likely covers return structure, but the description should still reference it for completeness.

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 coverage is 100%, so baseline is 3. The description adds value by explaining how the parameters should be used in context: summary should be a work summary, project_directory should be provided for user reference, and timeout is for waiting. This goes beyond the schema 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 explicitly states the tool is for interactive feedback collection. The usage rules clarify its purpose as a loop mechanism for iterative feedback, distinguishing it from the sibling tool get_system_info which is for system info retrieval.

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 clear, prescriptive rules for when to call the tool (during any process, task, or conversation) and when to stop (explicit termination). It also specifies when to call again upon receiving non-empty feedback, leaving no ambiguity.

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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