datalab_search

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

With no annotations, the description carries the burden of disclosing behavior. It mentions 'relative search volume changes' (상대적 검색량 변화), which is a key behavioral trait, but does not describe the output format, rate limits, authentication requirements, or limitations like date range constraints. It provides minimal but relevant context beyond the schema.

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, front-loading the core function and immediately giving use cases. Every clause adds value; there is no redundancy, fluff, or repetition of schema information. It is concise yet informative.

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 a moderate complexity (7 params, nested keywordGroups) and no output schema, so the description should explain return values more explicitly. It mentions analyzing changes but doesn't describe the response format (e.g., time series data) or operational constraints like max keyword groups. However, the schema covers parameters comprehensively, and the use case hint adds context.

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% with detailed descriptions for every parameter (e.g., dates, timeUnit, keywordGroups). The description adds general context but no specific parameter-level details beyond what the schema already provides. The baseline of 3 applies since the schema fully documents parameters.

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's function: analyzing relative search volume changes by keyword group ('키워드 그룹별 상대적 검색량 변화를 분석합니다'). This specific verb+resource combination distinguishes it from sibling tools like search_* (which search documents) and datalab_shopping_* (which focus on shopping data).

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 gives practical use cases ('마케팅 시즌 파악, 트렌드 비교에 유용합니다') indicating when to use it. It does not explicitly mention alternatives or exclusions, but the sibling tools are functionally distinct enough to imply appropriate usage contexts.

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