v1_vin_decodeVIN decode: Decode a US-market Vehicle Identification Number. Source: NHTSA vPIC. $0.01 per call · GET /v1/vin-decode
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | 11-17 char VIN. Example: '1HGCM82633A004352'. | |
| year | No | Model year hint. Example: '2003'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable context beyond annotations: data source (NHTSA vPIC), cost ($0.01 per call), and HTTP method/endpoint (GET /v1/vin-decode). It does not describe response format or error behavior, but for a safe read-only lookup 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 compact and front-loaded with the core purpose, followed by source, cost, and endpoint. The leading 'VIN decode:' label is slightly redundant with the tool name, but it does not meaningfully detract from the overall efficiency.
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?
The tool is simple with one required parameter, the schema fully documents parameters, and annotations cover the safety profile. The description provides source, cost, endpoint, and US-market scope. No output schema exists, but the return value is implied by the decode operation, so nothing essential for calling it correctly is missing.
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%, with both 'vin' and 'year' already documented through types, examples, and descriptions. The description adds the 'US-market' qualifier and source, which enriches the context but does not add param-specific syntax beyond the schema. Baseline 3 is appropriate.
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 explicitly states 'Decode a US-market Vehicle Identification Number' and names the source 'NHTSA vPIC'. This is a specific verb+resource and clearly distinguishes it from all sibling tools, none of which relate to VIN decoding.
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 description does not explicitly state when to use this tool versus alternatives, nor does it name exclusions. However, the purpose is so domain-specific that usage is strongly implied: when a US-market VIN needs decoding. Sibling tools are all unrelated, so no alternative routing is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.