stat_weather

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

With no annotations, the description carries the burden; it does convey read-only behavior via 조회한다 and historical scope via 관측 이력, which adds context. However, it omits authentication needs, rate limits, and how missing or incomplete observations are handled, with no output schema to fill in the gap.

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 Korean sentence with no filler; the resource, data fields, and granularity are all front-loaded. Every word contributes to the agent's understanding.

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 purpose is adequately stated and the schema covers all five parameters, but with no annotations and no output schema, the description leaves out response structure, default behaviors, and alternative routing. It is sufficient for a simple read-only query but has clear 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 100%, so the baseline is 3; the description adds no parameter-specific detail beyond what the schema already documents. The mention of hourly/daily reinforces the mode parameter but does not enhance meaning.

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 clearly states a specific verb (조회한다 – queries), a concrete resource (기상청 관측 이력 – KMA observation history), and the available granularity (hourly/daily). This distinguishes it from sibling tools like stat_weather_stations or stat_daily_avg, which revolve around station metadata or daily averages.

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 offers no guidance on when to use this tool versus alternatives such as data_raw_search or stat_daily_avg, and no prerequisites or exclusions are mentioned. Usage must be inferred solely from the purpose statement.

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