NHTSA Vehicle Safety
Server Details
Vehicle safety recalls, complaints, and crash data from NHTSA
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Score is being calculated. Check back soon.
Available Tools
6 toolscomplaint_trends_by_componentAInspect
Get complaint counts by vehicle component for a specific make/model/year.
Shows which parts of the vehicle consumers report the most problems with
(e.g. brakes, engine, electrical system). Useful for identifying systemic
vehicle defect patterns.
Args:
make: Vehicle manufacturer name (e.g. 'Toyota', 'Ford').
model: Vehicle model name (e.g. 'Camry', 'F-150').
year: Model year (e.g. 2023).| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | ||
| year | Yes | ||
| model | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It adequately discloses the aggregation behavior (grouping by component like brakes/engine) but omits operational details such as read-only safety, rate limits, or handling of missing data.
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?
Well-structured with clear paragraph break between purpose statement and argument documentation. Every sentence adds value; no filler or tautology.
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?
Appropriate for a query tool with an output schema (which removes need to describe return values). All 3 required parameters are documented. Minor gap: does not explicitly state the read-only/non-destructive nature given lack of annotations.
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 0% (no parameter descriptions in JSON schema). The Args section fully compensates by providing clear semantic meanings and concrete examples for all 3 required parameters (make, model, year).
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?
Specific verb 'Get' + resource 'complaint counts' + scope 'by vehicle component'. Distinguishes from sibling 'get_complaints' by emphasizing aggregation and trend analysis ('systemic vehicle defect patterns') rather than individual complaint retrieval.
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?
Provides implied usage context ('Useful for identifying systemic vehicle defect patterns') but lacks explicit when-not-to-use guidance or comparison to siblings like 'get_complaints' or 'get_recalls'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
decode_vinAInspect
Decode a Vehicle Identification Number (VIN) to get vehicle details.
Returns make, model, year, body class, engine info, safety features,
and other attributes encoded in the 17-character VIN.
Args:
vin: A 17-character Vehicle Identification Number (e.g. '1HGBH41JXMN109186').| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description adds context about what data is returned (safety features, engine info) but omits rate limits, auth requirements, or idempotency characteristics.
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?
Appropriately brief with clear front-loaded purpose statement; Args section follows standard conventions without redundant filler.
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 low complexity (1 param) and existence of output schema, description adequately covers parameter semantics and return value overview; minor gap on validation rules (I/O/Q exclusions).
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?
With 0% schema description coverage, the Args section compensates effectively by providing the 17-character constraint and concrete example VIN not present in the schema.
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?
Specific verb 'decode' with clear resource 'VIN', and return details distinguish it from siblings like get_complaints/get_recalls which handle safety incidents rather than vehicle specifications.
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?
Usage is implied by the specific return values listed (make, model, year vs complaints/crashes), but lacks explicit when-to-use/when-not-to-use guidance versus alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_complaintsAInspect
Get consumer complaints about vehicles filed with NHTSA.
Search for safety complaints by make, model, and/or year. Returns
complaint descriptions, components involved, and crash/injury data.
At least one filter (make, model, or year) should be provided.
Args:
make: Vehicle manufacturer name (e.g. 'Toyota', 'Ford').
model: Vehicle model name (e.g. 'Camry', 'F-150').
year: Model year (e.g. 2023).
limit: Maximum number of complaints to return (default 25).| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| year | No | ||
| limit | No | ||
| model | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return payload contents (descriptions, components, crash/injury data) and the limit default, but omits other behavioral traits like pagination or empty result handling; no annotations to contradict.
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?
Well-structured with front-loaded action statement followed by constraints and Args block; every sentence adds value without redundancy.
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?
Appropriately complete given presence of output schema; captures essential usage constraint (minimum one filter) and previews return data without duplicating full schema.
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?
Comprehensively compensates for 0% schema description coverage by providing semantic meaning and concrete examples for all four parameters (make, model, year, limit) in the Args section.
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 it retrieves NHTSA consumer vehicle complaints with specific verb and resource, implicitly distinguishing from sibling 'get_recalls' (complaints vs 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?
Provides critical constraint that at least one filter (make/model/year) must be provided, but lacks guidance on when to choose this over 'get_recalls' or 'get_crash_statistics'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_crash_statisticsAInspect
Get fatal crash statistics from the NHTSA Fatality Analysis Reporting System (FARS).
Returns fatal motor vehicle crash data for a state, including total
fatalities, fatalities by person type (drivers, passengers, pedestrians),
and alcohol-involved crashes.
Args:
state: Two-digit state FIPS code (e.g. '06' for California, '48' for Texas)
or two-letter state abbreviation (e.g. 'CA', 'TX').
year: Year for statistics (e.g. 2022). Defaults to the most recent available year.| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description carries burden well: discloses data source (FARS), default year behavior, and specific return categories (alcohol-involved, person types).
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?
Well-structured with front-loaded purpose, concise return value summary, and clear Args section; every sentence provides necessary context without redundancy.
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?
Adequate for tool complexity (2 simple params); leverages existence of output schema to avoid over-describing returns while summarizing key data categories.
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?
With 0% schema coverage, description fully compensates by providing format examples for state (FIPS codes vs abbreviations) and explaining year defaults.
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?
Explicitly states it retrieves fatal crash statistics from NHTSA FARS, clearly distinguishing from vehicle-specific siblings (decode_vin, get_complaints, get_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?
Provides clear context that it returns state-level data including fatalities by person type, implicitly guiding when to use it vs vehicle-specific alternatives, though lacks explicit 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_recallsAInspect
Get vehicle safety recalls from NHTSA.
Search for recalls by make, model, and/or year. Returns recall campaigns
including the defect description, remedy, and affected vehicles.
At least one filter (make, model, or year) should be provided.
Args:
make: Vehicle manufacturer name (e.g. 'Toyota', 'Ford', 'Honda').
model: Vehicle model name (e.g. 'Camry', 'F-150', 'Civic').
year: Model year (e.g. 2023).| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| year | No | ||
| model | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses data source (NHTSA) and return value contents (defect description, remedy, affected vehicles) beyond what annotations provide (none provided).
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?
Well-structured with front-loaded purpose and logical flow; Args section is necessary given schema deficiencies, though slightly verbose.
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?
Complete for tool complexity: covers input constraints, data source, and return value overview despite existence of output schema.
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?
With 0% schema description coverage, the Args section fully compensates by providing clear semantics and concrete examples for all three parameters.
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?
Explicitly states it retrieves vehicle safety recalls from NHTSA, clearly distinguishing from siblings (complaints, crash statistics, 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?
Provides critical constraint that at least one filter (make, model, or year) must be provided, though lacks explicit comparison to when to use get_complaints vs get_recalls.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
local_vehicle_safety_profileAInspect
Get a vehicle safety profile using national complaint and recall trends.
NHTSA complaints are not geocoded by state, so this returns national-level
trends as context for local community safety assessments. Includes the most
recent recalls and top complained-about vehicle makes.
Args:
state: Two-letter state abbreviation (e.g. 'CA', 'TX'). Used for crash
statistics; complaint data is national.| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full disclosure burden. Explains critical behavioral traits: complaints are national-only despite state parameter, state filters only crash statistics, and return includes 'most recent recalls and top complained-about vehicle makes'. Missing rate limits or error conditions, but strong data-scope transparency.
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?
Well-structured with clear progression: purpose statement → data scope disclaimer → parameter documentation. Every sentence earns its place. The 'Args:' section is slightly verbose but necessary given 0% schema coverage. No redundant or tautological content.
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?
Appropriate for a data aggregation tool with output schema present. Does not need to describe return values (handled by output schema). Covers all parameters, explains the multi-source aggregation (complaints + recalls + crashes), and discloses the national/state data split. Could explicitly note it combines functionality of sibling tools.
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 0% (state property lacks description). Description fully compensates by documenting format ('Two-letter state abbreviation'), providing examples ('CA', 'TX'), and explaining semantic purpose ('Used for crash statistics; complaint data is national'). Essential parameter documentation absent from schema.
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?
Clear specific verb ('Get') + resource ('vehicle safety profile') + method ('using national complaint and recall trends'). Distinguishes from siblings like get_complaints and get_crash_statistics by emphasizing national-level trends as context for local assessments and noting the hybrid data scope.
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?
Provides clear context about data limitations ('NHTSA complaints are not geocoded by state') and scope ('national-level trends as context for local community safety assessments'). Explains that state parameter applies only to crash statistics, not complaints. Lacks explicit naming of sibling alternatives, but clearly implies when this aggregated view is appropriate versus raw data tools.
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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