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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive nature. The description goes further by disclosing that landslide and tsunami data are license-restricted and return available:false with a link to official maps, and that the tool returns official values as-is with no composite score or judgment. This adds valuable behavioral context about data availability and interpretation beyond the annotations.

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 moderately sized and front-loaded with the core purpose. Each sentence provides substantive information (source, categories, licensing, return semantics, input format, usage caveat), though the input format sentence is redundant with the schema and could be trimmed without loss. Overall, the structure is clear and information-dense.

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 output schema exists, the description doesn't need to explain return values, but it still addresses key contextual elements: data source, specific hazard categories, licensing limitations, output semantics, input format, and appropriate usage. This makes the tool's behavior and limitations clear for an AI agent, especially for a niche Japanese data source.

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 schema fully documents the single parameter 'station_name' with examples in Japanese and romaji, achieving 100% coverage. The description repeats this input format but adds no semantic detail beyond the schema, so it meets the baseline but does not exceed it.

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 function: returning official disaster-risk categories at a Japanese train station from the MLIT Real Estate Information Library. It specifies the exact categories (flood inundation-depth rank, landform/liquefaction classification, storm-surge presence) and distinguishes itself from siblings like get_station_alerts and get_station_context by focusing on hazard categories rather than alerts or general context.

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 provides clear context for when to use the tool ('For research/analytics') and explicitly warns against using it as a substitute for official government hazard maps or evacuation decisions. However, it does not explicitly name alternative tools or state when NOT to use this tool in favor of a sibling, though the distinct subject matter implies appropriate use cases.

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