correction_trend

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

With no annotations provided, the description carries the full burden. It discloses that the operation is a read-oriented query and adds the 12-month scope, but it does not describe the output format, ordering, grouping, or any other behavioral details. This is adequate but not rich.

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, well-structured sentence that front-loads the resource and time scope. Every word earns its place, with no redundancy or filler.

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?

The description is minimally viable for invoking the tool: it states purpose, scope, and the 12-month window, and the schema covers parameters. However, since there is no output schema, it would be stronger if it described what the returned trend data looks like or how it relates to siblings like correction_factors.

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 the input schema already documents both parameters. The description does not add parameter-specific semantics beyond implying that the trend is filtered by device and possibly by measurement item, which is baseline acceptable.

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 ('조회한다', meaning queries/retrieves) and names the resource ('측정기의 보정 인자 변화 추이'), including an explicit 12-month time window. It clearly conveys what the tool does, though it does not explicitly distinguish it from sibling correction-related tools.

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 implicitly indicates when to use the tool: when a recent 12-month correction factor trend is needed. However, it provides no explicit guidance about when not to use it or which sibling tool (e.g., correction_factors, correction_compare) should be chosen instead.

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