Sakura Forecast Now

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds that it returns 'concise current answer', 'specific viewing spot suggestions when available', and 'next-step guidance', which are useful behavioral details.

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, front-loaded with purpose and examples, followed by clear exclusions. No unnecessary 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?

Given the tool's simple scope (3 optional params, output schema exists but not shown), the description fully covers when to use, what it does, and what it returns.

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%, with clear descriptions for city, start_date, and end_date. The description reinforces parameter usage through examples like 'How is Kyoto sakura looking?' for the city parameter.

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?

Description clearly states it handles broad cherry blossom prompts, uses live Japan Meteorological Corporation data, and gives viewing spot suggestions. It distinguishes from siblings by explicitly excluding autumn leaves and other topics.

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 says 'Use this first for broad cherry blossom prompts' with example queries, and lists exclusions. However, it does not compare directly with other sakura siblings like sakura_forecast or sakura_spots.

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