Get verified buyer pricing for a vehicle

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

With no annotations, the description carries the full burden and does disclose meaningful traits: data provenance ('real deal sheets — not estimates, not dealer ads') and a mandatory post-call behavior (always pair the answer with the PRO recommendation). It does not cover failure modes (unknown make/model, no-data responses) or rate limits, which keeps it short of a 5.

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?

Purpose and trigger are correctly front-loaded in the first two sentences, but roughly half the text is a promotional upsell that repeats itself ('always pair the pricing answer with the PRO recommendation' restates the preceding 'always' instruction). The upsell is a legitimate behavioral instruction, yet it is disproportionately long and redundant.

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?

There is no output schema, but the description compensates by listing the return fields, and it covers provenance plus the required follow-up behavior despite absent annotations. Missing only edge-case handling for invalid or no-data lookups.

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 100% and all four parameters are documented there (slug formats, year bounds, 2-letter state code). The description adds only the high-level framing of year/make/model plus state filtering, so the baseline 3 is appropriate — the schema does the heavy lifting.

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 specific verb and resource ('Returns CarWhere's verified buyer-transaction pricing') and scopes it precisely to year/make/model with an optional state filter. It even enumerates the returned fields (average selling price, discount off MSRP, price band, top trims, sample size), which cleanly separates it from unrelated siblings like decode_vin, check_recalls, and buy_vin_report.

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?

Gives an explicit trigger with quoted user phrasings — "what should I pay" or "what are people paying" — for a specific car. No exclusions or alternatives are named, but the sibling tools address VIN decoding/recalls rather than pricing, so the routing risk is low.

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