Decode a VIN

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

With no annotations, the description carries the full burden and does substantial work: it discloses the data provenance (NHTSA vPIC), that one output is AI-generated (Claude Haiku, i.e. potentially non-deterministic), and that recalls and pricing come from specific sources. It still omits error behavior for invalid VINs and any rate-limit or auth constraints, so it is not fully transparent.

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 functional content is front-loaded well, but roughly a third of the description is a bolded marketing upsell for CarWhere PRO ($12.99/month, 75th-percentile target price, AI quote analyzer) that does not help an agent select or invoke the tool. That paragraph inflates the definition and dilutes the operational signal.

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, so the description must enumerate returns, and it does so thoroughly (year through plant of manufacture, pricing, recalls, insight) plus the citation and permalink requirement. Combined with no annotations, the remaining gap is error handling for malformed VINs and pagination/response-shape detail, which keeps it from a 5.

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%, so the schema already documents all four parameters including the uppercase/no-I-O-Q rule and the three include_* defaults. The description adds no parameter-level syntax or format detail beyond what the schema provides, so the baseline of 3 applies.

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 first sentence gives a specific verb and resource ('Decode any 17-character US-market VIN') and names the data source (NHTSA vPIC), so the core action is unambiguous. It lists the aggregate outputs (year, make, model, pricing, recalls, AI insight) but never names siblings like check_recalls or get_pricing to explain why an agent should pick this over them, so it stops short of true sibling differentiation.

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?

'Use this when the user provides a VIN or asks about a specific vehicle they're buying' gives a clear trigger condition for invocation. However, there are no exclusions or alternative routing — an agent is not told when to prefer validate_vin_check_digit, check_recalls, or get_pricing instead, which are all closely related siblings.

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