datalab_shopping_keywords

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only describes the basic comparison of trends and does not disclose additional behaviors such as data source restrictions, rate limits, or the fact that the output is a time series. Without annotations, this is a significant gap.

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 two sentences long, front-loaded with the tool name and a dash-separated summary. Every sentence adds value: the first explains the core function, the second adds a use case. No redundancy.

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 8 parameters, no output schema, and no annotations, the description provides the core purpose and a use case but lacks guidance on how it relates to the sibling datalab_shopping_keyword_by_age/by_gender/by_device tools. It also does not explicitly state the return format (e.g., a time series), leaving the agent to infer from the word 'trend'.

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 descriptions cover 63% of parameters (startDate, endDate, timeUnit, category, and keyword are described; ages, device, and gender are not). The description adds context for category and keyword ('within a specific category' and 'by keyword') but does not explain the optional filter parameters or the keyword group structure, which is only present in 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 it 'compares click volume trends by keyword within a specific category' (특정 카테고리 내 키워드별 클릭량 추이를 비교합니다). This clearly identifies the verb (compare), resource (keyword click volume trends), and scope (within category), distinguishing it from sibling tools like datalab_shopping_category, datalab_shopping_keyword_by_age, etc.

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 says it is 'useful for competitive keyword analysis' (경쟁 키워드 분석에 유용합니다), providing a clear use case. However, it does not explicitly state when not to use it or mention alternatives among the closely related sibling datalab_shopping_keyword_by_* tools.

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