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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds useful behavioral context the annotations cannot: the tool returns aggregated counts per dataset kind rather than records, which is exactly what an agent needs to know before calling it. No pagination or size limits are mentioned, though.

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 English text is front-loaded and every sentence earns its place: purpose, use context, return shape, and next step. The Korean line only partially restates the first sentence, which is intentional for a bilingual audience but does add mild duplication.

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?

There is no output schema, so the description carries the burden of explaining the return value, and it does so (counts per dataset kind). For a low-complexity two-parameter read tool this is nearly complete; only output ordering or empty-result behavior is left unstated.

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 both sido and sigungu (including its optionality and examples) are already documented in the schema. The description only restates that the unit is a Korean city/district, adding no new syntax or filtering semantics beyond baseline.

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 concrete verb and resource: it reports what dataset kinds exist for one sido/sigungu and counts records per kind. It also implicitly separates itself from search_data by framing itself as the aggregate view that precedes record-level drilling.

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?

"Good as a first look before planning a trip or a site survey" gives a clear use context, and "then drill in with search_data" names the follow-up alternative. It never states when this tool is the wrong choice, so it stops short of a full 5.

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