Autumn Leaves Forecast

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

Annotations already declare the tool as read-only and idempotent. The description adds behavioral context: it returns city-level forecast dates, maps, and regional commentary from a specific source (Japan Meteorological Corporation). It also implies a seasonal scope (October-December). This exceeds annotation information.

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 usage guidance. Every sentence adds value: usage conditions, output summary, exclusion and alternative. No filler or repetition.

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?

With 2 optional parameters, no output schema, and annotations covering safety, the description fully covers tool behavior: inputs, outputs, usage boundaries, and alternative tool. It is complete for an agent to decide and invoke 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?

Schema description coverage is 100% for both parameters. The description indirectly references parameters (e.g., 'maple and ginkgo' for tree_type, 'national overview' for region) but does not add significant syntactic or semantic detail beyond the schema. Given high coverage, baseline 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 the tool's purpose: providing autumn leaves peak forecast, city comparisons, and national overview. It uses a specific verb ('use this when') and resource ('autumn leaves forecast'). It distinguishes itself from siblings like koyo_spots by specifying the scope (city-level vs. specific locations).

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 the tool (user asks about peak, city comparison, national overview) and when not to (specific temples, gardens, GPS-tagged locations). It also names the alternative tool (koyo_spots). This provides clear decision guidance for the agent.

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