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

No annotations are provided, so the description must carry the full behavioral disclosure. It conveys that the tool returns statistical normals rather than real-time forecast data, which is a meaningful behavioral trait. However, it does not describe the output format, error behavior, or any side effects (though it is clearly a read-only query). The disclosure is minimal but adequate for a simple statistical lookup.

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 compact two-part sentence that front-loads the core information (period, data types, scope) and then adds the use-case clarification. Every sentence earns its place with zero filler.

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 simple statistical query tool with no output schema, the description adequately explains the data source (30-year normals), the fields returned, and the intended use case. It omits explicit return format details, but given the low complexity and clear data scope, the description is sufficiently complete for an agent to call the tool correctly.

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 input schema already covers both parameters with descriptions (stationId examples, month range and optionality). The description does not add any new semantic detail about the parameters beyond what the schema provides, so the baseline of 3 is appropriate given 100% schema coverage.

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 (provides) and resource (Korean climate normals 1991–2020), enumerates the exact data fields (monthly mean/max/min temperatures) and scope (by station). It also distinguishes itself from weather forecasts, making its purpose unambiguous and distinct from sibling tools that handle other domains.

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?

It gives an explicit usage example ("What is October usually like in that region?") and an explicit exclusion: "It's not a forecast, it's normal statistics." This tells the agent when to invoke this tool and when not to, even though it does not name alternative tools.

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