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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds the useful behavioral fact that model strings must match exactly and are validated by the upstream source, but says nothing about result size, ordering, or error behavior when a make/year is unknown.

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?

Two sentences, front-loaded with purpose, then a conditional routing rule. The example output strings earn their place by showing the exact format the agent will need to feed downstream.

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?

Though there is no output schema, the description effectively demonstrates the return value with sample model strings, and the two required params are fully covered. For a simple lookup tool an agent has everything needed to call it and interpret the result.

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 description coverage is 100% and both parameters are documented with examples there, so the baseline is 3. The description reinforces that make is the manufacturer and year is the model year via the 2024/Toyota example, but adds no formatting rules (casing, aliases, multi-year ranges) beyond the schema.

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?

Specific verb ("List") plus a precisely scoped resource: the exact model strings fueleconomy.gov recognizes, keyed on year + make. The inline example (2024/Toyota -> "Camry", "Camry Hybrid LE", "RAV4 AWD") makes the output shape concrete and distinguishes it from the sibling fuel_economy_mpg.

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?

Explicitly states when to call it: before fuel_economy_mpg when unsure of the exact model string, or when fuel_economy_mpg has rejected a model as unrecognized. That is a clear trigger condition plus a named alternative, leaving nothing to inference.

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