datalab_shopping_category

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

No annotations are provided, so the description must disclose behavioral traits. It only says 'perform a trend analysis' and does not state whether the operation is read-only, what response format to expect, whether pagination exists, or any rate limits or authentication requirements. This is a substantial gap.

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

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

The description is a single front-loaded sentence in English and Korean, with no filler or redundant content. It efficiently states the action and resource.

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

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

Given the lack of output schema and annotations, the description is too thin. It does not explain return values, date range semantics, category code structure, or any edge cases. The tool has four required parameters and is a data analysis tool, so more context is needed for complete understanding.

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

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

Schema description coverage is 100%, so the input schema already documents all four parameters, including the enum for timeUnit. The description itself adds no parameter-specific meaning, making baseline 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs trend analysis on Naver Shopping category, with the action 'perform' and resource 'Naver Shopping category.' The category focus distinguishes it from sibling device/gender/age/keyword tools, though it does not explicitly call out those differences.

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

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

The purpose sentence implies the tool should be used for category-level Naver Shopping trend analysis, but there is no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or why this tool is preferable to sibling datalab_shopping_by_device, by_gender, or by_age tools.

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