MCPFax Vehicle Diagnostics

NHTSA safety recalls

vehicle_recalls

Official NHTSA safety recalls for a vehicle. Returns campaign id, affected component, the safety consequence, and the free remedy, newest first (max 50). Query by make (optionally model and year) or by a known campaign id. Use this before buying a used vehicle or when a customer asks whether their car has open recalls. A zero count means nothing matched the ingested NHTSA subset — it does not prove the vehicle is recall-free. Costs $0.005 USDC per call via x402 on Base.

Input Schema

TableJSON Schema

Name	Required	Description
`make`	No	Consumer make name, e.g. 'FORD', 'TOYOTA'. Required unless 'campaign' is given.
`year`	No	Four-digit model year, e.g. '2018'. Optional.
`model`	No	Model name, e.g. 'F-150', 'CAMRY'. Optional.
`campaign`	No	NHTSA campaign id to fetch directly, e.g. '21V123000'. Optional.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 4, 2026
- addedInput schema / properties / campaign / examples
```
Added value: +[
+  "21V123000"
+]
```
- addedInput schema / properties / make / examples
```
Added value: +[
+  "FORD"
+]
```
- addedInput schema / properties / model / examples
```
Added value: +[
+  "F-150"
+]
```
- addedInput schema / properties / year / examples
```
Added value: +[
+  "2018"
+]
```
First observedAug 22, 2026

TDQS

A4.5/5.0

Behavior5/5

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 covers ordering (newest first), result limit (max 50), data-source limitations ('ingested NHTSA subset'), the zero-count caveat, and even cost per call. This is unusually transparent.

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

Conciseness5/5

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

Every sentence carries distinct information: purpose, return fields, query modes, use cases, a caveat, and pricing. It is front-loaded and dense without being verbose.

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?

Despite having no output or annotation schema, the description explains what results will look like, how to query, when to use the tool, what a zero count means, and the cost. It is fully actionable for an agent.

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%, so the schema already documents all four parameters. The description adds useful context about query modes (by make with optional model/year, or by campaign id) but does not provide significant new parameter-level detail beyond what the schema already states.

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?

The description states a specific resource ('NHTSA safety recalls for a vehicle') and the exact outputs returned (campaign id, affected component, safety consequence, free remedy). It also distinguishes itself from sibling tools by focusing on recalls rather than DTCs, TSBs, or VIN decoding.

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?

It gives explicit use cases: before buying a used vehicle or when a customer asks about open recalls. It does not explicitly say when not to use it or name alternatives, but the context is clear enough for an agent to select it appropriately.

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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