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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile and the fact that data comes from an external register are covered. The description adds the concrete source (MOLIT register) and the granularity of results (district/month, trends, single complexes), but says nothing about data freshness, the period covered, or response size.

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?

One compact sentence plus a short input instruction; the domain and granularity are front-loaded and nothing is padded. It could be marginally more efficient by folding the language note into the same sentence.

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 single-parameter natural-language query tool with no output schema, the description covers the data domain, source, granularity, and input language. It still omits the historical coverage window, output shape, and any limits on the question, which an agent would want before committing a query.

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?

With a single 'question' parameter at 0% schema description coverage, the description must carry the burden. It partially does: it signals a natural-language question, permits Korean or English, and enumerates the dimensions the question can address (district, month, trends, complexes). It does not clarify length limits, how ambiguity is resolved, or how to phrase multi-condition queries.

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 resource (Korean apartment sale prices), its source (the MOLIT real-transaction register), and its queryable dimensions (district, month, trends, single complexes). This is enough to distinguish it from sibling market-data tools like kr_fx, kr_rates, and kr_export_pulse, though no sibling is named 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?

The only guidance is the input hint 'Ask in Korean or English'; there is no statement of when to reach for this tool versus another, no prerequisites, and no exclusions. An agent must infer the use case entirely from the purpose statement.

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