correction_versions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does state a read-only intent ('조회한다') and the nature of the returned data (version history with performance metrics), but it does not mention pagination, ordering, response shape, or any prerequisites. This is adequate for a simple lookup tool, though richer behavior context would be beneficial.

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 a single, focused sentence with no filler, redundancy, or unsupported claims. It conveys the core purpose efficiently and is appropriately front-loaded.

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 4-parameter query tool with no output schema and no annotations, the description provides only the essential purpose and omits usage context, default behavior, and differentiation from overlapping siblings like correction_trend or correction_compare. The schema covers parameter details, but an agent would benefit from explicit guidance on when to call this tool and how it relates to sibling tools.

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 schema description coverage is 100%, so the schema already documents all parameter semantics. The description adds no parameter-specific meaning beyond the schema, only indicating the overall output content of version history and metrics, which matches the baseline expectation.

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 the verb '조회한다' (query) with a specific resource '보정 모델의 버전 이력' (calibration model version history) and adds '성능 지표 포함' (including performance metrics), which clearly differentiates it from sibling tools like correction_factors or correction_trend. The resource is distinct and the scope is explicit even without naming an alternative.

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 provides no explicit guidance on when to use this tool versus alternatives such as correction_factors, correction_trend, or correction_compare. Usage context is only implied by the stated purpose of querying version history, so an agent must infer the appropriate situation from the tool name and description.

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