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

Annotations already carry readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds genuinely useful context beyond annotations: the KMA ultra-short-term observation data source, the observation-vs-forecast semantics, nationwide scope, and the 'right now' temporal guarantee. Minor gap: no mention of update latency or output format.

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 English portion is two tight sentences with the purpose front-loaded and the limiting exclusion second. The bilingual Korean repetition adds length and is somewhat redundant for an LLM agent, though it increases accessibility for Korean users. Overall efficient and well-structured, with a slight redundancy cost.

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 zero-parameter, read-only tool, the description is nearly complete: it lists returned data elements (temperature, humidity, wind, warnings), scope, source, and temporal semantics. With no output schema present, it stops short of describing return format or units, but the agent knows what kind of data to expect and what questions it can answer.

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 tool has zero parameters, so the schema covers 100% of parameter documentation. Baseline 4 applies because there is nothing for the description to add — no parameter names, defaults, or constraints exist to explain. The description appropriately focuses on behavior instead.

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 names a specific resource and scope: nationwide Korean current temperature, humidity, wind, and active warnings from KMA observations. It explicitly distinguishes itself from a forecast tool ('not a forecast', 'cannot answer about tomorrow'), and none of the sibling tools overlap with weather, so an agent can identify its job immediately.

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 gives an explicit when-to-use ('use when asking about current weather/warnings') and an explicit when-not-to-use ('cannot answer about tomorrow's weather'). The Korean sentence states the usage condition directly, and the exclusion of forecast questions is unambiguous even though no alternative weather tool exists among siblings.

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