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

Annotations already provide readOnlyHint=true and openWorldHint=false. The description adds useful behavioral context beyond that: the update frequency ('Updates: monthly') and the methodology (z-score vs 1980-present NOAA NCEI history), which helps set expectations. 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 appropriately sized and front-loaded. The first sentence states exactly what is returned, the second enumerates use cases, and the third gives update frequency. Every sentence earns its place with no fluff.

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 no output schema, the description fully inventories the return fields (trend, confidence, top_drivers, cost z-score, percentile rank, latest vs trailing-10yr mean) and explains the data source and update cycle. It is complete for a read-only, optional-parameter 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% for the single optional 'days' parameter, with a thorough schema description covering the history series and Gold tier requirement. The tool description does not add any parameter information, so the baseline 3 applies.

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 returns a specific 0-100 US climate-disaster cost-trend score and enumerates the output fields (trend, confidence, top_drivers, cost z-score, percentile rank, latest vs trailing-10yr mean). This makes its purpose unambiguous and distinct from siblings.

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 explicit call conditions: 'Call when the user asks about climate disaster costs, extreme-weather losses, hurricane/wildfire/flood damage, or catastrophe costs vs historical norms...' and lists timing use-cases. However, it does not specify when NOT to use or mention alternative tools, so it lacks the full 'when-not/alternatives' element.

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