vin_decode_infoList which fields are available for decoding a given VIN (free endpoint).
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It discloses that this is a 'free endpoint' and that it lists fields (not actual decoded values), which clarifies its read-only, informational nature. It does not mention return format or any limitations, but the output schema exists and the tool is simple enough that this is acceptable.
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 description is a single, information-dense sentence. It front-loads the action ('List') and the target ('fields'), with a useful parenthetical about being a free endpoint. There is no wasted wording or repetition.
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?
For a tool with one parameter, sibling context, and an output schema, the description is adequately complete. It clearly states what the tool does and that it is free. It could explicitly mention that this is a metadata endpoint for vin_decode, but the sibling tool names and the tool name itself (vin_decode_info) imply this relationship, so completeness is strong.
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 0%, so the description must compensate. It mentions 'for a given VIN', which clarifies the single 'vin' parameter's purpose and adds meaning beyond the bare type 'string'. It does not specify VIN format or validation rules, but the name 'vin' is highly self-explanatory, so this is adequate.
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 uses a specific verb 'List' and a clear resource: 'fields available for decoding a given VIN'. It distinguishes itself from sibling tools like vin_decode (which likely performs the actual decoding) by explicitly stating it lists available fields rather than decoding the VIN itself.
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 phrase 'List which fields are available for decoding a given VIN' implies this is a precursor to vin_decode, providing clear context for when to use it. However, it does not explicitly name alternatives or state when not to use it, though the sibling tool names make the distinction obvious.
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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Each tool has a clearly distinct purpose: stolen check, market value, VIN decoding, and field listing. Even vin_decode and vin_decode_info are separate since one returns decoded data and the other returns available fields.
All tool names use lower_snake_case and are descriptive, but they mix noun-phrase patterns (vehicle_market_value) with verb-noun patterns (vin_decode). This is a minor inconsistency, though still readable and predictable.
Four tools is a well-scoped size for a vehicle data API, covering the essential VIN-related lookups without unnecessary redundancy or overwhelming breadth.
The surface covers core VIN tasks: decode, market value, stolen status, and field availability. Minor gaps like VIN validation or vehicle history reports exist but are not obvious dead ends for common use cases.