nhtsa_makes

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

The annotation readOnlyHint=true already signals a safe read operation. The description adds no behavioral traits beyond that, but it doesn't contradict annotations. For a simple list, this is adequate.

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 extremely concise with two sentences and an example. Every sentence adds value, and it is front-loaded with the purpose.

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 tool's simplicity (2 parameters, read-only, no output schema), the description is complete. It explains what the tool does, how to use it, and provides an example. No gaps.

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%, and the schema already describes both parameters. The description adds context (e.g., example usage) but does not provide meaning beyond what the schema offers, so baseline 3 is appropriate.

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 that the tool lists vehicle makes for a model year that have recalls or complaints. It uses a specific verb ('List') and resource ('vehicle makes'), and the context of sibling tools (e.g., nhtsa_recalls, nhtsa_complaints) helps distinguish it.

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 guidance on use with the example 'model_year=2024, issue_type='r'' and clarifies the meaning of issue_type values. It doesn't explicitly mention when not to use, but the intent is clear for a simple list tool.

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