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

Beyond the annotations (readOnly, idempotent, openWorld=false), the description discloses substantive behavior: spellings and aliases resolve to the stored vehicle, a zero is confirmed only when coverage_complete is true, Jev tags cover only MY2012+ and ~43% of complaints, and per-label confidence is not accuracy. It also explicitly disclaims what the data is not (prediction, safety rating, verdict, repair advice, individual vehicle history), which prevents misuse.

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 core purpose and returned record families are front-loaded in the first sentence, and the caveats follow in a logical order. It is dense and somewhat long, with a couple of run-on sentences, but every clause carries information an agent needs.

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?

With no output schema, the description must describe the return payload, and it does: it lists the record types, the optional verified rate, source coverage, and the coverage_complete flag that governs whether a zero is meaningful. Combined with the Jev-tag caveats, an agent has enough to call the tool and interpret the result without surprises.

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 0%, so the description must carry the parameter burden. It names the three inputs and adds genuine meaning for make/model ('spellings and aliases resolve to the stored vehicle'), but says nothing about the year parameter's bounds or format, leaving one of three parameters semantically unexplained.

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 opens with a specific verb and resource: 'Look up the recorded risk facts for one make, model and model year,' then enumerates the exact record families returned (complaint counts by system/subsystem, recalls, investigations, manufacturer communications, rate, coverage). It is clearly a single-vehicle record lookup, which separates it from compare_vehicles, listing_changes and recall_changes.

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?

Usage is implied rather than stated: the 'one make, model and model year' framing tells the agent this is a single-vehicle lookup, but there is no explicit when-to-use this vs. compare_vehicles or the change-tracking siblings. No prerequisites or exclusion conditions are given.

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