get_mainline_grade_per

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

With no annotations, the description carries the full burden. It discloses the monthly update cadence and M-2 data lag, which is valuable behavioral context. But it does not explain aggregation behavior, whether omitting carmdl returns all train models, or whether run_ym can be left blank for the latest available period.

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 compact sentence that front-loads the tool's purpose, then adds update timing and parameter definitions. There is no filler, and every clause contributes useful information.

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 two-parameter statistics tool with an output schema, the description is mostly sufficient: purpose, freshness, and parameter semantics are covered. The main gaps are optionality/default behavior and explicit guidance for choosing this tool among the numerous mainline 'per' statistics siblings, especially given the absence of annotations.

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 0%, and the description fully compensates: run_ym is defined as 운행연월(YYYYMM) and carmdl as 차종명 with a concrete example (KTX). Both parameters receive format and meaning that the schema alone does not provide.

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 states the resource clearly: '간선열차 객실별 이용인원 통계' (mainline train passenger counts by cabin/class), and identifies the two relevant dimensions (run_ym, carmdl). It does not explicitly distinguish itself from sibling per-statistics tools like get_mainline_model_per, but '객실별' implies a distinct grouping.

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 a useful data-freshness cue ('갱신: 매월 1일, M-2'), which implies when the data is available. However, it never says when to prefer this tool over the many similar mainline statistics siblings, nor whether the parameters are required or optional.

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