list-models

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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds that the return value is a slug used as input elsewhere, which is helpful context but not a deep behavioral disclosure (e.g., no mention of pagination, ordering, or potential variations). This meets the minimum bar but does not exceed it.

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, zero redundancy. The first sentence states the action and inputs; the second explains the output's usage. Perfectly front-loaded and concise.

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 simple list tool with strong schema coverage and annotations, the description is sufficient. It clarifies the output (slugs) and downstream consumers, making the tool's role in the broader API clear. No output schema is needed given the simplicity.

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%, with both 'make' and 'year' parameters fully described including examples and how to obtain valid values. The description itself only restates the parameters ('for a given make and year') without adding new semantic detail, so the baseline of 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 uses a specific verb ('List') and resource ('vehicle models for a given make and year'), and explicitly distinguishes from siblings by noting the slugs are used as the 'model' input for other tools. This makes the tool's purpose unmistakable.

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 clearly implies when to use the tool: when you need model slugs for a specific make and year, especially before using list-generations, list-modifications, or search-by-model. It does not explicitly state exclusions but contextualizes its place among sibling tools effectively.

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