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

Goes well beyond the read-only annotations by disclosing the computation basis (live, published-rate cars only, never estimated), the null-figure behavior for empty scopes, the meaning of the floor_note output field, and the did_you_mean fallback for unknown inputs. These are exactly the behavioral traits an agent cannot infer from readOnlyHint/idempotentHint.

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?

Front-loads the purpose and what is returned, then layers when-to-use, caveats, and edge cases in a logical order. It is on the long side for a five-sentence definition, but nearly every clause carries non-redundant information, so little could be cut without loss.

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 an output schema present, the description need not enumerate return fields, yet it still covers the edge cases an agent must anticipate: null figures on empty scopes, floor_note interpretation, and did_you_mean on unknown inputs. Nothing needed to call this tool correctly is missing.

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 already 100%, so the baseline is 3; the description adds how the five filters act as alternative scopes ('for a make, model family, state, city or class') and warns that a scope yielding no published rate returns nulls. It does not document accepted formats itself, but it meaningfully reinforces the parameter model.

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?

States a specific verb and resource ('Summarize the published daily rental rates of exotic cars') and enumerates exactly what the summary contains: count, publish count, min, quartiles, median, max, state split, and extremes with links. This is clearly distinguishable from siblings like search_vehicles or vehicle_availability, which return individual listings rather than aggregates.

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?

Gives concrete triggering questions ('what does a 911 Turbo S cost per day in California?', state comparisons, cheapest class to rent), which tells an agent precisely when to reach for this tool. It stops short of naming an alternative sibling or stating when NOT to use it, so it falls just below the top band.

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