wheel-upsteps

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

The description discloses that suggestions maintain overall tyre diameter within 'acceptable tolerance' and includes the set of returned fitment terms, which goes beyond the read-only annotation. It does not quantify the tolerance or cover error conditions, but with annotation coverage that's a reasonable level of detail.

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?

Three direct sentences, with the main action and scope in the first sentence and no filler. The usage note is valuable and the fitment-term list is compact.

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?

Despite no output schema, the description explains both what is returned (fitment terms) and the constraint it satisfies. It is sufficient for an agent to select and invoke the tool, though it lacks details on failure modes or data availability.

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?

All four parameters are fully described in the schema with examples and constraints, so the 100% coverage baseline applies. The description adds context about region-specific suggestions and key fitment terms, but does not materially extend parameter semantics.

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 object: 'Get aftermarket wheel upsize suggestions', and clarifies scope by naming the returned fitment terms. This clearly differentiates it from sibling list/search tools by focusing on plus-sizing recommendations rather than raw data lookup.

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 final sentence explicitly says 'Use this to recommend wheel upgrades while preserving speedometer accuracy', providing a clear use case. However, it doesn't name alternatives or explicitly state when not to use it, so it stops short of full differentiation.

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