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. It conveys only that this is an analysis operation; it says nothing about the returned values being relative/normalized indices rather than absolute search volumes, nothing about limits on keyword-group count, and nothing about rate limits or auth.

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 core sentence is well front-loaded, but the emoji, the promotional 'Perfect for market research and trend analysis' clause, and the duplicated Korean translation pad it without adding signal.

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?

Four required parameters, no annotations, and no output schema. Given the absence of an output schema, the description should explain what the time series looks like (per-group relative index values), which would materially change how an agent interprets and reports results.

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% and both the date format and the timeUnit enum are documented in the schema. The description adds only loose semantics ('over time' maps to start/endDate and timeUnit, 'compare multiple keywords' maps to keywordGroups), which is baseline 3 territory.

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?

States a specific verb and resource: analyze search keyword trends over time via Naver DataLab, including popularity changes, seasonality, and multi-keyword comparison. That distinguishes it from the search_* siblings (which retrieve documents) and loosely from the datalab_shopping_* siblings (shopping-specific trends), though it never names the latter explicitly.

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?

'Perfect for market research and trend analysis' implies a use context but gives no explicit when-to-use or when-not. Critically, it never distinguishes itself from datalab_shopping_keywords, a sibling doing apparently similar keyword-trend work, leaving the agent to guess which to pick.

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