datalab_search

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 mentions 'trend analysis' but doesn't specify what the analysis entails (e.g., returns data points, charts, insights), whether it requires authentication, rate limits, or data freshness. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 concise with two short phrases, front-loading the main purpose. The Korean translation adds clarity for Korean users without being redundant. However, it could be more structured by explicitly stating the tool's scope or output.

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 no annotations and no output schema, the description is incomplete for a tool with 4 parameters and complex input (keyword groups). It doesn't explain what the trend analysis returns (e.g., time-series data, visualizations) or behavioral aspects like permissions or limitations. For a data analysis tool, this leaves too much undefined.

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 parameters (startDate, endDate, timeUnit, keywordGroups) with descriptions. The description adds no additional meaning beyond the schema, such as explaining how keyword groups are used in analysis or date format nuances. Baseline 3 is appropriate when 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's purpose: 'Perform a trend analysis on Naver search keywords.' It specifies the verb ('perform trend analysis'), resource ('Naver search keywords'), and includes a Korean translation for clarity. However, it doesn't explicitly differentiate from sibling tools like 'search_blog' or 'search_news' which might also involve Naver search but for different content types.

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. With many sibling tools (e.g., 'search_blog', 'search_news', 'datalab_shopping_by_age'), there is no indication of whether this is for general search trends versus specific domains or demographics. Usage is implied by the name 'datalab_search' but not explicitly stated.

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