Autumn Leaves Forecast Now

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

Annotations already declare readOnlyHint and idempotentHint, so the description does not need to repeat safety. It adds context about the data source (Japan Meteorological Corporation maple and ginkgo forecast data) and the nature of the answer (concise, current). No contradictions.

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 three sentences, front-loaded with the key directive, and every sentence adds value. No wasted words.

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 description covers purpose, usage, and exclusions. An output schema exists so return values are documented elsewhere. For a simple query tool with optional parameters, this is sufficient. It does not discuss pagination or edge cases, but that is acceptable.

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 coverage is 100% with clear descriptions for all parameters. The description adds no additional detail beyond the schema, so the baseline of 3 is appropriate.

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 it answers broad autumn leaves prompts with a concise current answer from live Japan Meteorological Corporation data. It includes example queries and explicitly excludes cherry blossoms and other topics, but does not explicitly distinguish from closely related koyo siblings like koyo_forecast or koyo_best_dates.

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 explicitly states when to use ('Use this first for broad autumn leaves prompts'), provides example queries, and specifies when not to use (cherry blossoms, fruit picking, hotels, etc.). This gives strong guidance for choosing this tool over alternatives.

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