datalab_shopping_keyword_by_age

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

No annotations are provided, so the description carries the full behavioral burden. It mentions the find_category prerequisite but does not disclose response shape, data limitations, read-only behavior, or other operational traits for this analytics query.

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 purpose and prerequisite are front-loaded, but the emojis and Korean parenthetical largely duplicate the English text rather than adding information. The description remains short, though it is not as tight as it could be.

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?

The tool has six required parameters and no output schema, and the description omits what metrics or trend format are returned. It covers the main prerequisite but leaves the agent without enough context about return values and alternative tools.

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 already documents all six parameters. The description adds useful context that category codes should come from find_category, but it does not add meaning for dates, timeUnit, keyword, or ages beyond the schema.

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 states a specific verb/resource: analyzing keyword performance by age groups within shopping categories. It is fairly distinguishable from siblings like datalab_shopping_by_age and keyword_by_device/gender, though it does not explicitly contrast them.

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?

It gives a clear prerequisite to use find_category first for category codes and names age-targeted marketing as a use case. It does not explicitly state when to choose this over the closely related device/gender keyword tools.

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