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

The description discloses key behavioral traits: returns official values/categories as-is (no composite score, no judgment), indicates that landslide and tsunami data are license-restricted and return 'available:false' with a link. Annotations already include readOnlyHint, idempotentHint, etc., but the description adds valuable context about data availability and output nature. No contradiction with 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 long but every sentence carries essential information: source, categories, restrictions, input format, disclaimer. It is front-loaded with the primary purpose. While not extremely concise, it avoids redundancy and is well-structured.

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 complexity of the tool (multiple hazard categories, license restrictions, special return behavior), the description is complete: it covers data source, included/excluded categories, input format, output behavior, and appropriate usage disclaimer. The existence of an output schema (not shown) further supports completeness. An agent can confidently select and invoke this tool.

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 a description for station_name. The description adds value by clarifying that the station name can be in Japanese or romaji, and provides examples (新宿, 武蔵小杉, Shinjuku, Musashi-Kosugi). This goes beyond the schema's generic description, aiding correct input.

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 retrieves official disaster-risk categories for a Japanese train station, specifying the data source (MLIT Real Estate Information Library), the categories included (flood inundation-depth rank, landform/liquefaction classification, storm-surge inundation-area presence), and what is excluded (landslide and tsunami). The verb 'get' and resource 'station hazard' are precise. This distinguishes it from sibling tools like get_station_alerts or get_station_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 usage context: 'For research/analytics; NOT a substitute for official government hazard maps or evacuation decisions.' It implies when to use (analytics, not emergency) and notes license restrictions for some categories. However, it does not explicitly contrast with alternative sibling tools or state when to prefer this over get_station_alerts, etc. Still, the guidance is clear enough.

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