kosis_trend

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 does disclose the computation semantics (직전 시점 대비 증감, 구간 요약, CAGR), which is the key behavioral trait, but says nothing about missing periods, minimum data requirements, or how the default 5-point window is chosen.

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?

Two sentences with zero filler: the first front-loads what is computed and the output shape, the second gives the motivating use case. Every clause earns its place.

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 10-parameter analytical tool with no annotations and no output schema, the description does help by naming the returned metrics. But it omits default behavior (5 recent points), error handling, and any guidance on the required classification identifiers beyond what the schema already says.

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%, covering the date formats, ALL convention, group_by, and include_series, so the baseline is 3. The description adds no parameter-level meaning beyond what the schema already states.

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 specific action (시계열로 묶어 증감·증감률·CAGR 계산) on a specific resource (통계표 자료), so an agent knows exactly what comes back. It is clear but does not distinguish itself explicitly from the sibling kosis_data, which likely returns raw observations.

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 second sentence gives a use case (거시 추이와 시장 규모 변화 파악), which implies when to reach for this tool. However, it never states when NOT to use it or points to kosis_data/kosis_export for raw series or export, so routing remains inferred.

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