assetcache-mcp

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report_feedback

Record user feedback on asset recommendations to improve future search results. Submit positive, negative, or irrelevant signals for penalty learning.

Instructions

추천 결과에 대한 사용자 피드백을 기록한다 (페널티 학습 입력).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "req": "<req>"
    },
    "name": "report_feedback"
  }
}

Input Schema

Name	Required	Description	Default
`req`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.7Aug 1, 2026

TDQS

B3/5.0

Behavior3/5

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

No annotations are available, so the description carries the full burden. It discloses that feedback serves as penalty learning input, which is a meaningful behavioral implication affecting recommendation models. However, it does not mention reversibility, required permissions, or side effects beyond the learning context, leaving notable gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that efficiently conveys the core function and purpose, with no redundant words or unnecessary detail.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete for an agent to fully understand invocation context. It does not state when to use the tool, what response to expect, or any usage constraints, which are critical for a feedback recording tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain any parameters such as query_id, asset_id, or reason. The phrase 'recommendation results' indirectly hints that query_id and asset_id relate to a recommendation, but this is not explicit, and the description adds minimal value over the raw schema.

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 description clearly states the tool records user feedback on recommendation results using the specific verb 'records' and the resource 'recommendation results', also noting the purpose as penalty learning input. It distinguishes from sibling tools like record_asset_use by focusing on feedback rather than usage, but it does not explicitly mention the feedback categories (positive/negative/irrelevant) from the schema.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The description only states what the tool does, leaving the agent to infer the appropriate context from the tool name and schema.

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