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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), so the bar is for the description to add context. It discloses substantive temporal behavior: response meta.data_as_of is the true recency point, warnings must be forwarded to users, and lagging figures must not be asserted as 'current'. It also reveals a first-party data collection defect for households and the correct alternative route — genuinely useful non-obvious behavior beyond the annotations.

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 compact, front-loaded with purpose and example questions, then progressive: temporal caveats, then the metric-specific bug and fallback. Each sentence adds value and none repeats schema content, though the opening could be slightly shorter without losing clarity.

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 tool with no output schema, the description provides the most critical response-level guidance: full data recency via meta.data_as_of, warning propagation, and base-year framing for annual series. It does not describe the response shape or grouping beyond what metric implies, which is a minor gap given no output schema exists, but the operational essentials for the agent are covered.

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 the schema already documents each enum value, the months window (1–60, max 5 years), and age's row-level constraints, so the description need not repeat them. The description adds only indirect param context (which metrics are monthly vs annual, that months cap applies to population·migration). Baseline 3 applies because the schema carries the weight and the prose adds limited extra meaning.

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 uses a specific verb+resource ('지역 인구통계를 조회한다') plus concrete example questions ('인구 줄고 있어?', '1인 가구 비율은?') that make the tool's scope unmistakable. The title and opening line also enumerate the four metric dimensions. It does not explicitly name or differentiate from sibling tools like realty_macro_indicators, so it falls just short of a 5.

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?

Provides strong 'when to use' signals via realistic user questions covering population decline, household composition, aging, and migration. It also gives a metric-specific exclusion (households rejects same-name 시군구) with an instructed fallback to population·migration. It stops short of explicitly contrasting with sibling alternatives (e.g., when to prefer realty_macro_indicators or realty_compare_regions).

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