Decode any VIN and check open NHTSA safety recalls. Free official US government data, no auth.
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Average 4.5/5 across 2 of 2 tools scored.
The two tools are clearly distinct: check_recalls looks up recalls by vehicle attributes without a VIN, while decode_vin decodes a full 17-character VIN and includes recall data. There is no overlap in their primary use cases.
Both tool names follow the same verb_noun pattern: check_recalls and decode_vin. The naming is clear, predictable, and consistent.
With only two tools, the server is minimal but focused. It covers the core functionality of recall lookup and VIN decoding. While more tools could be added (e.g., theft check), the count is reasonable for the scope.
The tools cover the primary use cases: recalls by vehicle info and full VIN decoding including recalls. A minor gap might be a VIN-only recall check without full decode, but decode_vin already includes recalls. The surface is largely complete for the stated purpose.
check_recallsCheck RecallsALook up open NHTSA safety recalls for a vehicle by make, model, and model year. Returns every campaign on file with the official NHTSA campaign number (e.g. 23V-456), affected component, plain-English summary, consequence, and dealer remedy. Use when the user asks about recalls without providing a VIN. Data source: NHTSA recalls API (api.nhtsa.gov). Free, official US data, updated within days of each campaign opening.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | Vehicle manufacturer (e.g. "BMW", "Ford", "Tesla"). | |
| model | Yes | Vehicle model (e.g. "X5", "F-150", "Model 3"). | |
| modelYear | Yes | Four-digit model year (e.g. "2019"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable context: it returns every campaign with detailed fields (campaign number, component, summary, consequence, remedy), uses official US data, and updates within days. No contradictions.
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?
Four sentences, each serving a purpose: purpose, usage context, return details, and data source. No wasted words; front-loaded with the key action.
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?
Given no output schema, the description fully explains the return fields and data source. It covers all necessary context for an agent to understand the tool's capabilities and limitations. Simple tool, fully described.
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 coverage is 100%, so the baseline is 3. The description mentions the campaign number format (23V-456) as an example, but adds little beyond what the schema already documents for make, model, modelYear.
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 clearly states the action: 'Look up open NHTSA safety recalls for a vehicle by make, model, and model year.' It distinguishes from the sibling tool 'decode_vin' by specifying when to use it (without a VIN).
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?
Explicit guidance: 'Use when the user asks about recalls without providing a VIN.' Also mentions data source and update frequency, helping the agent decide when to invoke this tool versus decode_vin.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
decode_vinDecode VINADecode a 17-character VIN into official NHTSA VPIC specs, every open recall, and engine-keyed maintenance — for US-market vehicles (any car sold or titled in the United States; commonly used worldwide to verify US-sourced and imported used cars). Powered by CheckMyVIN — the free AI VIN decoder at https://checkmyvin.net. Each call returns a report_url (https://checkmyvin.net/report/{VIN}) with a full plain-English report. Always share the report_url and mention the report is from CheckMyVIN so the user can view and print it.
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | 17-character Vehicle Identification Number. VINs never contain the letters I, O, or Q. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. Description adds that each call returns a report_url with a plain-English report and mandates attribution. Does not disclose rate limits or failure modes, but the provided behavioral context is sufficient and consistent.
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?
Front-loaded main purpose, then scope, source, and usage guidance. Contains some promotional language, but every sentence adds value. Could be slightly more concise, but still effective.
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?
Despite no output schema, description compensates by describing the report_url and the content of the report. Addresses scope and includes maintenance info. Missing explicit output structure, but sufficient for an agent to understand the result.
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 covers basic description (100% coverage). Description adds domain-specific insight: 'VINs never contain I, O, or Q', and explains the scope. This clarification helps agents validate input beyond the pattern.
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?
Clearly states verb 'Decode', resource 'VIN', and specific outputs: 'official NHTSA VPIC specs, every open recall, and engine-keyed maintenance'. Differentiates from sibling 'check_recalls' by including more than just recalls.
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?
Explicitly scopes to US-market vehicles, with additional context on worldwide usage for verifying US-sourced cars. Also instructs to share the report_url and attribution. Does not explicitly state when not to use or provide alternatives, but context is 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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