get_mainline_model_per

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

No annotations are provided, so the description carries the full burden. It discloses the update cycle (매월 1일) and the data lag (M-2), which is valuable behavioral context for data freshness. However, it does not mention that it is a read-only query, whether any authentication is required, or how results are structured. For a stats query, this is acceptable but not exhaustive.

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 sentence that packs the core purpose, update frequency, and both parameter explanations without any filler. It is front-loaded with the subject, making it easy to scan.

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 simple two-parameter stats tool and the existence of an output schema (which handles return format), the description covers the essential semantics: what the tool returns (passenger statistics per vehicle), the data cadence, and parameter meanings. It could be improved by stating the granularity (e.g., per month per vehicle) or explicitly noting that both parameters are filters, but the description is adequate for the tool's complexity.

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 0%, so the description fully compensates by explaining both parameters: run_ym = operation year-month in YYYYMM format, carmdl = vehicle type name (e.g., KTX). This is precise and includes a concrete example, exceeding the minimal requirement and providing the agent with the exact expected value formats.

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?

States the specific resource: mainline train per-vehicle passenger statistics, which is distinct from sibling tools for station, route, distance, etc. The verb is implied (get/query) but the action and subject are clear. It also names the two parameters that define the query scope, so an agent can tell exactly what this tool retrieves.

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?

Provides no guidance on when to use this tool versus the many sibling statistics tools (e.g., get_mainline_station_per, get_mainline_route_per). It gives a hint via '차량별' (by vehicle) but does not explicitly state when it applies or what alternatives exist. There is no mention of scenarios that would warrant a different tool.

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