Autumn Leaves Viewing Spots

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

Annotations already declare readOnlyHint and idempotentHint as true, so safety is clear. Description adds behavioral context: returns data from Japan Meteorological Corporation, shows behavior when prefecture is omitted, and what fields are returned. No contradiction.

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?

Four sentences, each serving a distinct purpose: use case, return data, alternative behavior, and sibling guidance. No redundant information. Front-loaded with key usage context.

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 tool's simplicity (1 optional param, no output schema), the description covers when to use, what it returns, alternative usage, and how it relates to sibling tools. No gaps are evident.

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 description adds meaning beyond the input schema by explaining that omitting the prefecture yields a top-destinations guide. Schema coverage is 100% but the description provides crucial behavioral guidance not inferable from the schema alone.

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 the tool returns exact autumn leaves viewing spots for a specific prefecture, with data fields listed. It distinguishes from siblings by explicitly mentioning not to use for cross-city date matching.

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?

Explicitly states when to use ('when the user already knows the prefecture') and when not ('Do not use for cross-city date matching'), with specific alternative tools named (koyo_forecast, koyo_best_dates). Also covers alternative usage (omitting prefecture for top destinations).

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