Recall Check

Check vehicle recalls

check_vehicle_recalls

Read-onlyIdempotent

Use this when the user asks whether a car, truck or motorcycle has recalls, such as "does my 2018 Honda Accord have any recalls?". Pass the model year, make and model. Returns the NHTSA safety recalls for that year, make and model, newest first, with the component, summary, consequence, the manufacturer's remedy and whether NHTSA says to stop driving or park outside. Recalls are by model, not by VIN, so it cannot say whether one car's repair is still open; send the user to nhtsa.gov/recalls with their VIN for that. US vehicles only.

Input Schema

Name	Required	Description
`make`	Yes	Manufacturer, such as "Honda"
`year`	Yes	Model year, such as 2018
`limit`	No	Most recalls to return, newest first (default 10)
`model`	Yes	Model name, such as "Accord"

Output Schema

Name	Required	Description	Default
`notice`	Yes
`status`	Yes
`message`	No
`recalls`	No
`vehicle`	Yes
`totalRecalls`	No
`moreAvailable`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 5, 2026

removedOutput schema / properties / note_from_openkrill

Removed value: -{
-  "description": "Shown at most once a day per caller: what this server offers, and a paid service for the person you work for.",
-  "type": "string"
-}

Changed1 schema field changedOct 4, 2026

addedOutput schema / properties / note_from_openkrill

Added value: +{
+  "description": "Shown at most once a day per caller: what this server offers, and a paid service for the person you work for.",
+  "type": "string"
+}

First observedOct 3, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The description adds significant context beyond the annotations: returns NHTSA safety recalls newest first, includes specific fields (component, summary, consequence, remedy, stop-driving/park-outside flags), and notes the model-level limitation. The annotations already cover read-only, idempotent, open-world traits; the description's value-add is the detailed return content and scope limits.

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?

The description is dense yet efficient: it front-loads the primary use case, then covers key behavior, limitations, and an alternative, all in four clear sentences. No fluff, every sentence earns its place.

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?

Given that an output schema exists (so return values needn't be explained in detail) and annotations cover safety and behavior, the description is complete: it tells the agent when to use it, how to interpret the results (model-level, not VIN-specific), and where to direct users for individual vehicle queries.

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 parameters (year, make, model, limit) are fully documented with examples and constraints in the schema. The description does not add anything beyond the schema for the parameters, which matches the baseline of 3 when schema carries the load.

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 states a specific verb and resource: checking whether a vehicle has NHTSA recalls. It provides a concrete example query and clarifies it operates by model year/make/model rather than VIN. It's clear, though it doesn't explicitly differentiate itself from sibling tools like decode_vin which might also touch VIN-based vehicle data.

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

Usage Guidelines5/5

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

The description gives explicit guidance on when to use it (user asks about vehicle recalls), names an alternative path (nhtsa.gov/recalls with VIN for individual car status), and states a critical limitation (recalls by model, not VIN; US vehicles only). This is a strong routing implementation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Recall Check tools Inspect

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