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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond that: the $0.10 USDC cost per call, the data sources, and a concrete example of the output. This helps the agent set expectations for cost and response format.

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 front-loaded with the primary use case, then efficiently covers output, example, sources, and cost. The example is lengthy but serves to illustrate the output structure and adds value. No sentence is redundant, and the structure is logical.

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?

For a read-only single-parameter tool with annotations covering safety and no output schema, the description supplies essential context: output composition, a detailed example, data sources, and cost. The example compensates for the missing output schema and gives the agent a solid picture of what to expect. Slightly more detail on the exact return structure would round it out, but it is largely complete.

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 provides 100% coverage for the location parameter, so the baseline should be 3. However, the description's example uses 'Miami-Dade FL' (a county) while the schema specifies 'US city and state' (e.g., Miami FL). This inconsistency could mislead an agent into passing a county name when the tool expects a city, actively reducing the clarity of parameter semantics.

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 a specific function ('pricing physical climate risk for a location') and lists concrete use cases such as real estate acquisition and underwriting. It identifies the output as a composite risk score across six perils, which distinguishes it from generic benchmark tools. However, it does not explicitly differentiate from sibling tools like get_climate_risk_benchmark, so it misses the top tier.

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 opens with 'Use when pricing physical climate risk' and enumerates specific scenarios, which is clear contextual guidance. It does not mention when not to use the tool or alternatives, but the when-to-use guidance is explicit and actionable.

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