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

With no annotations, the description carries the full behavioral burden and largely does so: it enumerates the return states (exact, suggest, needsYear, needsModel, notFound) and prescribes the agent action for each, including 'ask ONE question listing availableYears' and 'never guess'. The only borderline gap is that some of this return-state detail may overlap the output schema, but the action protocol (confirm, ask one question, decline) is genuine added value.

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 call-order rule is front-loaded in the first clause, and every subsequent clause does work: input routing, then the return-state handling. The quoted examples add a little length but earn their place by illustrating real user input forms.

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?

An output schema exists, so the return shape need not be re-explained, and the description still supplies the behavioral handling for each return state. For a resolution/normalization gate feeding four sibling lookup tools, nothing an agent needs to call it 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 100%, so all four parameters are already documented, establishing a baseline of 3. The description adds only a thin clarification ('Pass the user's words in query'), which does resolve the choice between the free-text query and the structured make/year/model fields, but adds no format or precedence detail beyond that.

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 conveys a specific function: it takes a user-spoken vehicle and returns resolvable slugs, with an explicit call-order instruction ('Use FIRST whenever the user names a vehicle'). It doesn't state the resolution verb outright in one sentence, but the returns clause ('use the returned slugs') makes the purpose unambiguous and clearly distinct from the lookup siblings (get_vehicle_summary, get_recalls, etc.) that presumably consume slugs.

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?

It gives an explicit when ('whenever the user names a vehicle') and an explicit when-not ('unless you already have exact slugs from a previous call'), which is exactly the alternative-condition routing an agent needs. It also provides lexical triggers ('2017 Ford Escape', '17 F150', 'my Honda Civic') that cover common user phrasings.

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