Crypto, web, finance and app data tools

Vehicle Safety Recalls

vehicle_recalls

US safety recalls for a vehicle make, model and year from NHTSA: campaign number, date, component, summary, consequence, remedy and do-not-drive or park-outside warnings. Use before buying or servicing.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "make": "<make>",
      "model": "<model>",
      "year": "<year>"
    },
    "name": "vehicle_recalls"
  }
}

Input Schema

Name	Required	Description	Default
`make`	Yes
`year`	Yes
`model`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 26, 2026

TDQS

B3/5.0

Behavior2/5

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

No annotations exist, so the description carries the full disclosure burden. It adds useful scoping (US-only, NHTSA as source, presence of do-not-drive/park-outside warning flags), but says nothing about whether results are paginated, whether an empty result means no recalls vs unrecognized vehicle, or whether the lookup is exhaustive by model year.

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?

Two tight sentences, front-loaded with the resource and scope. The enumeration of return fields (campaign number, date, component, summary, consequence, remedy) is somewhat wasted since an output schema already exists, but it does signal that the payload is detailed.

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

Completeness3/5

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

For a three-required-parameter lookup with an output schema, the description covers what the tool is and roughly what comes back. It falls short on input conventions and on empty-result semantics, which are the main things an agent would otherwise get wrong when calling it.

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

Parameters2/5

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

Schema description coverage is 0%, so all three parameters (make, model, year) are documented only by their bare names. The description merely restates 'make, model and year' without format examples (e.g. 'Toyota' vs 'TOYOTA'), model-name conventions, or a valid year range, leaving the agent to guess acceptable values.

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

Purpose4/5

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

The description names a specific resource (US safety recalls) and scope (make/model/year, sourced from NHTSA), which lets an agent distinguish it from the other recall-style sibling fda_recalls and from vin_decode. It never states the retrieval verb explicitly, but 'safety recalls for a vehicle make, model and year' is unambiguous enough about what is returned.

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

Usage Guidelines3/5

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

The closing phrase 'Use before buying or servicing' gives a real usage context, and 'US' implicitly excludes non-US vehicles. However there is no explicit when-not guidance, no mention of vin_decode as the VIN-based alternative, and no statement about what to do when make/model spelling varies.

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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