datalab_shopping_keyword_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 but offers minimal information. It states the tool performs 'trend analysis' but doesn't describe what the analysis entails (e.g., returns data, visualizations, or insights), potential rate limits, authentication needs, or data freshness. For a tool with 6 required parameters and no annotation coverage, this is a significant gap in transparency.

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 in English with a Korean translation for clarity, front-loading the core purpose without unnecessary details. It avoids redundancy and wastes no words, making it easy for an agent to parse quickly.

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 tool's complexity (6 required parameters, no output schema, and no annotations), the description is incomplete. It lacks behavioral context (e.g., what the analysis returns, any side effects), usage differentiation from siblings, and doesn't compensate for the absence of output schema by describing expected results. For a data analysis tool with multiple inputs, this leaves significant gaps for an agent.

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%, with all parameters well-documented in the schema (e.g., 'ages' as age groups with enum values, 'timeUnit' with enum options). The description adds no parameter-specific semantics beyond implying analysis involves 'keywords' and 'age', which the schema already covers via 'keyword' and 'ages' parameters. Baseline 3 is appropriate when 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 tool performs 'trend analysis on Naver Shopping keywords by age' with a Korean translation for clarity. It specifies the verb ('perform trend analysis'), resource ('Naver Shopping keywords'), and dimension ('by age'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_keyword_by_gender', which would require a 5.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'datalab_shopping_by_age' (which might analyze shopping trends by age without keyword focus) or 'datalab_shopping_keyword_by_gender' (which does similar analysis by gender), leaving the agent to infer usage from names alone. There are no explicit when/when-not instructions or prerequisites.

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