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

Beyond the readOnly/openWorld=false annotations it discloses the crucial behavioral trait: the tool reads from a fixed dataset where each spec is cited and marked verified or unverified. It also documents the failure mode (not_in_database / year_covered false, no figures returned) and forbids filling gaps from memory — exactly the context an agent needs to avoid hallucinated fitment data.

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 use cases are front-loaded in a single dense sentence, then failure semantics. Every clause carries information, but the paragraph is long and run-on, which slightly slows scanning for the critical 'no figures returned' constraint.

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 return values need not be spelled out, yet the description still flags the key response keys (not_in_database, year_covered) and the verified/unverified marking. For a fixed-dataset lookup with four simple parameters, 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 75%, and the description adds real meaning beyond the schema by explaining that optional wheel_center_bore_mm 'runs the hub-centric check' rather than just echoing 'center bore of a wheel you want to check'. The year parameter gains implicit semantics from the year_covered false outcome, though make/model normalization behavior is not described.

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 names a specific resource (bolt pattern, lug torque, center bore, thread size, offset, tire size) and anchors it with concrete example queries ('what is the bolt pattern on a 2019 Ford F-150'). It also carves out the boundary against wheel_fitment_check by limiting itself to cases 'when the vehicle is named'.

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 clear trigger conditions via the example phrasings and an explicit exclusion ('when the vehicle is named', and the wheel_center_bore_mm path for fitment checks). It does not name sibling tools such as wheel_fitment_check or tire_size_compare directly, leaving the routing inference slightly implicit.

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