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

Annotations only provide readOnlyHint, but the description adds a full outcome taxonomy (exact, suggest, needsYear, needsModel, notFound) plus the prescribed agent action for each, including an anti-hallucination rule ('never guess'). This is rich behavioral context beyond what the annotation declares.

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 critical routing instruction ('Use FIRST') and packs the outcome branches into one dense but waste-free paragraph. Every clause carries actionable information.

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 and annotations cover the safety profile, so the description needn't explain return values; its coverage of branching outcomes and follow-up actions makes it complete for an agent to call and act on correctly.

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 the baseline is 3. The description adds genuine intent guidance by instructing 'Pass the user's words in `query`,' clarifying that raw user phrasing is expected rather than a normalized string.

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 precise verb+resource ('resolve a vehicle to exact year/make/model') and the outcome is unambiguous. It also implicitly distinguishes itself from siblings like get_vehicle_summary by framing itself as the upstream resolution step.

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 says to use it FIRST whenever a vehicle is named, with the exclusion 'unless you already have exact slugs from a previous call.' It also routes the agent's follow-up behavior per outcome, so the when/when-not is fully specified.

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