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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds genuinely new operational context: the exact scope of returned data, an explicit negative guarantee (no Google search volume), and that a trial-quota credit is consumed per call — the last being important for an agent deciding whether to invoke.

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?

Three short sentences, each doing distinct work: what is returned, what is explicitly excluded, and what the call costs. The return contents are front-loaded and nothing is redundant.

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?

With no output schema, the description compensates by enumerating the four data components returned, and it discloses the quota cost. What remains thin is selection guidance against the many sibling analytics tools, which is the one thing an agent could still get wrong.

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%, so both `category` and `agent` are already documented in the schema, including the note that `agent` raises the daily limit. The description adds no parameter-level meaning beyond that. Baseline 3 applies when the 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 names a concrete verb and resource ('업종 하나의 대표 검색어 수요를 돌려줍니다') and enumerates the returned components: Naver monthly volume, Datalab 13-month trend, related keywords, Google autocomplete suggestions. It also carves out what it does not do ('구글 검색량은 제공하지 않습니다'). It stops short of naming a sibling such as get_industry_brief, so differentiation is only partial.

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?

Usage is implied by the resource — call it to get keyword demand for one industry — and the one-line quota warning ('맛보기 한도를 1회 씁니다') signals a cost constraint. There is no explicit when-to-use-this-versus-X guidance relative to siblings like get_industry_brief or get_totals, and no stated prerequisites.

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