get_urban_train_environment

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

With no annotations, the description carries the full burden and does well: it explains that operator is required, train_no and measure are optional with defaults, only some operators are supported, and includes detailed answer guidelines about handling data recency, tone, and empty results. It adds significant behavioral context beyond the raw schema, though it does not describe the exact response structure.

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 core purpose and parameters are front-loaded and clearly structured, but the answer guidelines section is lengthy and detailed, covering tone rules, date handling, and when to include phone numbers. While valuable, these instructions add significant verbosity and go beyond the typical tool description scope, reducing conciseness.

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 that an output schema exists (though not shown) and the description covers purpose, parameters, data availability, and answer formatting, it is fairly complete for a query tool. It does not mention pagination, limits, or error handling, but the presence of an output schema and detailed parameter notes mitigate these gaps.

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%, so the description is the sole source of parameter meaning. It explains operator (code or name), train_no (optional, defaults to all measurements), and measure (environmental measurement code with concrete examples like 1 for PM10, 2 for CO2, 21 temperature, 22 humidity, 23 noise). This fully compensates for the absent schema descriptions.

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 a specific verb and resource: '도시철도 열차 차내 환경정보 조회' (query urban railway train in-car environment information), enumerating metrics (CO2, fine dust, temperature, humidity, noise) and notes '역 무관' (station-independent). This clearly differentiates it from sibling tools like get_urban_environment (station environment) and get_urban_train_facility.

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 a note on which operators supply data (S1, BS, DG vs. KR, AR), which aids in deciding if the tool will return results, but it does not explicitly name alternatives or state when to prefer this over get_urban_environment. Usage context is implied rather than explicit.

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