datalab_shopping_category

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

No annotations are provided, so the description carries the full burden. It only states that it 'analyzes click volume trends' without disclosing behavioral aspects such as maximum category count (3), date range constraints, or output format. This is a significant gap for a data analysis tool.

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, concise sentence that front-loads the core purpose and adds a brief use-case note. No redundant words or repetition of schema details.

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?

For a tool with 7 parameters and no output schema, the description is too sparse. It does not explain return values, data granularity, category limits, or how to interpret results. The description is insufficient for a complex analytical tool.

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% (all 7 parameters have descriptions), so the description adds no extra parameter meaning. Baseline 3 applies as the schema already documents parameters well.

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 it analyzes Naver Shopping category click volume trends ('카테고리별 클릭량 추이를 분석합니다') and mentions use cases ('시장 규모 파악 및 카테고리 비교'). It distinguishes from sibling keyword-focused tools by explicitly focusing on categories, though it does not name alternative tools.

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 description provides clear use cases (market size understanding and category comparison) that imply when to use this tool, but it does not give explicit exclusions or mention alternative tools like keyword trend tools.

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