SearchShop AI

skincare_report_issue

Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.

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": {
      "tool_name": "<tool_name>",
      "issue_type": "<issue_type>",
      "description": "<description>"
    },
    "name": "skincare_report_issue"
  }
}

Input Schema

Name	Required	Description
`tool_name`	Yes	Which tool had the issue (skincare_recommend, skincare_cart, skincare_report_issue)
`issue_type`	Yes	Type of issue
`user_query`	No	The original user query if available
`description`	Yes	What went wrong and what you expected instead
`expected_products`	No	What products should have been recommended

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedJul 10, 2026
- addedInput schema / properties / description / maxLength
```
Added value: +2000
```
- addedInput schema / properties / user_query / maxLength
```
Added value: +2000
```
First observedApr 16, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations mark the tool as not read-only and not destructive, and the description adds that it helps improve the engine, implying a write/feedback action. However, it does not disclose side effects, such as whether reports are stored or if there are limits, leaving moderate behavioral transparency.

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?

Three sentences that front-load the primary action and then provide usage conditions and rationale. No filler words; every sentence adds value.

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

Completeness4/5

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

For a simple feedback tool with full schema coverage and clear trigger conditions, the description is sufficient for correct invocation. It lacks an explicit mention of the expected response, but no output schema exists and the function is straightforward, so a small gap remains.

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 five parameters are documented (tool_name, issue_type, user_query, description, expected_products). The description does not add extra parameter-level detail, but the schema already carries the burden, so baseline 3 is appropriate.

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 uses a specific verb ('Report') and clearly identifies the resource (tool result issues), distinguishing it from sibling recommendation/cart tools. It explains the tool's role in flagging unhelpful, incomplete, or wrong results, which is unique among siblings.

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?

Explicitly states when to call it ('whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match...') and when not to ('Do not use proactively — only when you observe an actual issue'). This provides clear decision rules without needing separate alternative tools.

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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