쇼핑 카테고리 트렌드

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

The read-only annotation is already present, but the description adds valuable behavioral context: the output is normalized 0-100 relative trend, not absolute values. This helps prevent misuse and goes beyond what annotations provide.

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 well-structured sentence with a clear caution. It is front-loaded, concise, and every word adds value without repetition.

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 rich schema and read-only annotation, the description is fairly complete. It explains the core behavior and caveat. It doesn't mention response format or limits, but these are not critical given the schema clarity and the tool's simplicity.

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 coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond the schema; it only mentions that categories are queried, but the schema already covers all parameter meanings.

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 queries Naver DataLab Shopping Insight for category-specific relative trend trends normalized 0-100. It explicitly differentiates from absolute sales volume and real-time popular search ranking, distinguishing it from potential sibling 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 context: use this for relative category trends over a specified period. It warns against misinterpreting as absolute sales or real-time rankings, which implies when not to use it, though it does not explicitly name alternative tools.

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