datalab_shopping_by_age

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs 'trend analysis' but doesn't describe what that entails—whether it returns aggregated data, visualizations, or statistical insights. It also omits information about rate limits, authentication needs, or data freshness, which are critical 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, efficient sentence that front-loads the core purpose. The Korean translation adds redundancy but may aid clarity for Korean-speaking users. No extraneous information is included, making it appropriately concise.

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 data analysis tool with 5 required parameters and no output schema, the description is insufficient. It doesn't explain the output format (e.g., time-series data, charts), data granularity, or how results are structured. Without annotations or output schema, users lack critical context to interpret results effectively.

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 schema fully documents all parameters. The description adds no additional meaning beyond implying 'trend analysis' involves the parameters, but doesn't explain how they interact (e.g., how ages affect trends over time). Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('perform a trend analysis') and resource ('Naver Shopping by age'), with the Korean translation reinforcing the purpose. It distinguishes from some siblings (e.g., datalab_shopping_by_device) by specifying 'by age', but doesn't explicitly differentiate from datalab_shopping_keyword_by_age, which is a similar but distinct tool.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention sibling tools like datalab_shopping_by_gender or datalab_shopping_keyword_by_age, nor does it specify prerequisites or appropriate contexts for trend analysis versus other shopping-related tools.

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