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decode_vin

Decode a VIN into vehicle specs and original factory warranty coverage. Returns make, model, year, engine, body, drive, plus basic/powertrain/corrosion/roadside/EV-battery terms.

Instructions

Decode any 17-char VIN via NHTSA vPIC — normalized make/model/year/engine/body/drive — PLUS a factory-warranty block giving the ORIGINAL new-vehicle warranty terms as sold (basic/powertrain/corrosion/roadside/EV-battery), keyed off the decoded make + model-year (NOT warranty-remaining; excludes extended-warranty campaigns, recalls, and emissions warranties). Costs $0.01 per call. Pays USDC on Base via x402. Compute-first, settle-after: Vindex computes the full answer BEFORE charging, so you are never billed for an error or a below-threshold answer. Requires EVM_PRIVATE_KEY with USDC on Base Mainnet when the API is in live mode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYes17-character VIN, e.g. 1FTFW1ET5DFC10312
Behavior5/5

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

With no annotations provided, the description carries the full burden and does an excellent job: it details the NHTSA vPIC source, the warranty scope keyed to make/model-year, that it is NOT remaining warranty, cost per call, USDC payment on Base via x402, compute-first settle-after policy (never billed on error), and the need for EVM_PRIVATE_KEY in live mode. This is far beyond what a typical description provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and then provides essential details in a dense, structured manner. It is longer than two sentences, but every clause adds necessary value—warranty caveats, cost, billing model, and auth requirements—so it avoids useless filler while remaining compact for the amount of information conveyed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers all the critical context: what is decoded, the warranty block origin, exclusions, cost, payment path, compute-first error handling, and auth requirements. For a single-parameter tool, this is highly complete and equips an agent to invoke it correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single 'vin' parameter, which already includes a 17-character format and an example. The description does not add extra semantic detail about the parameter, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Uses the specific verb 'Decode' with a clear resource ('17-char VIN via NHTSA vPIC') and enumerates the exact output fields (make/model/year/engine/body/drive plus warranty block). This clearly distinguishes it from sibling tools like get_recalls, get_pricing, and get_health.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly communicates what the tool does and explicitly excludes recalls, extended warranties, and emissions warranties, which helps an agent know when NOT to use it. It does not explicitly name alternative sibling tools, but the exclusions imply that get_recalls would be for recall data, making guidance clear.

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

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