공공데이터 인벤토리 커버리지 상태

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

The description goes beyond annotations by noting that the numbers are a static snapshot, not the current total of all public data in South Korea. It also mentions 'current runtime channel', providing useful context about the tool's scope. Annotations already indicate read-only and idempotent behavior, and the description complements them without contradiction.

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 two sentences long and front-loaded with the action ('returns') and the specific items returned. The second sentence adds an important caveat, keeping it concise without unnecessary detail. Every word contributes value.

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?

Given the tool's simplicity (zero parameters, no output schema), the description adequately lists all return values and their semantic meaning. The snapshot caveat adds necessary context about data freshness, making the tool's output interpretable without further documentation.

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?

The tool takes no parameters, so parameter semantics are trivially satisfied. The description does not need to explain parameters, and the empty input schema is fully consistent. The baseline score of 4 applies given the absence of parameters.

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 clearly states what the tool does: it returns specific inventory coverage metrics (runtime channel, base date, row counts, differences, type counts). The verb '반환합니다' (returns) and the explicit list of returned data make the purpose unambiguous and distinct from sibling tools that list or search datasets.

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 description implies usage for checking coverage status but does not explicitly state when to use this tool versus alternatives like 'search_public_datasets' or 'list_public_data_sources'. There is no mention of exclusions or alternative tools, so guidance is only implicit via the scope of the returned data.

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