A
licenseNot graded
qualityC
maintenanceEnables querying U.S. vehicle safety and specification data, including VIN decoding, recalls, complaints, investigations, and crash test ratings.
3
MIT
NHTSA Vehicle Safety
by com.olyport
Server Details
Vehicle safety recalls, complaints, and crash data from NHTSA
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | ||
| year | Yes | ||
| model | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description has full burden for behavioral disclosure. It only states the output (counts by component) and a use case, but does not mention any side effects, data source limitations, or whether the operation is read-only. For a query tool this is somewhat implicit, but the lack of any behavioral context beyond results is a gap.
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 front-loaded with the main action, followed by a brief explanation, a use-case sentence, and a clean Args list. Every sentence earns its place, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description adequately covers the tool's purpose, parameters, and typical use case. It does not mention edge cases (e.g., no data for a given combination) but is otherwise complete for this simple query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description's Args section fully compensates by defining each parameter (make, model, year) with clear examples ('Toyota', 'Camry', 2023). This adds meanings the schema does not provide.
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 opens with a specific verb+resource+scope: 'Get complaint counts by vehicle component for a specific make/model/year.' It clearly distinguishes the tool from siblings like get_complaints (raw complaints) and get_crash_statistics.
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?
The description provides a clear use case ('Useful for identifying systemic vehicle defect patterns') but does not explicitly name alternatives or state when to avoid this tool. This is more specific than most, but falls short of explicit when/when-not guidance.
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').
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It accurately describes the decode operation and return content, but does not mention potential error handling, validation of the VIN, or whether the operation requires any special permissions. The read-only nature is implied but not explicitly stated.
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 concise and front-loaded, with the primary purpose in the first sentence. The Args section is clearly structured and provides necessary parameter details without redundancy.
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 the simple tool with a single parameter and an output schema, the description is complete: it covers purpose, parameter format, and expected output fields. No additional context seems necessary for a user to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for the 'vin' parameter (0% coverage), but the description's Args section fully compensates by specifying the 17-character format and giving a concrete example. This adds meaningful guidance beyond the raw schema.
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 clearly states the tool decodes a VIN and lists the types of details returned, making its purpose specific to vehicle identification decoding. However, it does not explicitly differentiate from sibling tools, as most other tools focus on complaints, crashes, and recalls rather than 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user needs vehicle details from a VIN, but it does not explicitly state when to choose this tool over alternatives or provide any exclusion criteria. No sibling tools are referenced.
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| year | No | ||
| limit | No | ||
| model | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses return content (complaint descriptions, components, crash/injury data) and the default limit, but does not mention potential error behavior if filters are missing, pagination, or explicit read-only nature. It's adequate but not rich in behavioral context.
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 concise and well-structured, with an introductory sentence followed by a clear Args list. Every sentence adds value, and it avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and moderate complexity, the description sufficiently covers search criteria, return content, and the limit. It lacks minor details like pagination, but overall it is complete for the tool's purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no property descriptions, but the tool description explains each parameter with examples (e.g., 'Toyota', 'Camry') and the default limit. It also adds the critical constraint that at least one filter is required, going beyond the schema.
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 clearly states 'Get consumer complaints about vehicles filed with NHTSA,' using a specific verb and resource. It distinguishes itself from sibling tools by focusing on complaint descriptions, components, and crash/injury data rather than trends, VIN decoding, or recalls.
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 provides clear usage context—search by make, model, and/or year—and importantly states that at least one filter should be provided. However, it does not explicitly compare with alternatives or say when not to use it, so it falls short of the highest bar.
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.1/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden. It adds useful context such as the default year behavior and the output categories, but it does not disclose permissions, rate limits, or potential side effects. Since the tool is a read-only statistics lookup, the risk is low, but the transparency is incomplete.
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 concise and well-structured. It opens with a clear purpose statement, then lists return contents, and finally documents parameters. Each sentence adds value with no redundant or filler text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, parameters, output content, and default behavior. An output schema exists, reducing the need to detail return structure. It lacks discussion of error conditions or data limitations, but for a simple fetch tool with two parameters, it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates. It explains the state parameter with both FIPS and abbreviation formats and examples, and explains the year parameter's default behavior. This provides complete semantic meaning beyond the basic schema types.
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 specifies the exact action ('Get fatal crash statistics') and the resource ('NHTSA Fatality Analysis Reporting System (FARS)'), and lists specific returned data (fatalities, by person type, alcohol-involved). This clearly distinguishes it from sibling tools like get_complaints or get_recalls.
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 intended use is implied by the resource and output content, but there is no explicit guidance on when to use this versus alternative tools. No exclusions or alternative recommendations are given, relying on the user to infer based on the data type.
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| year | No | ||
| model | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It notes the return includes recall campaigns, defect description, remedy, and affected vehicles, and implies a read-only operation via 'Get'. It lacks explicit mention of safety, rate limits, or error handling, but these are less critical for a simple data retrieval tool.
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 efficiently structured: a purpose statement, search/return details, a requirement note, and an Args list. Every section serves a purpose and no content is wasted, though the Args list adds length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is adequately described for its simplicity, with return content and input requirement specified. The presence of an output schema covers return structure. However, it does not explain error behavior when no filter is provided, nor does it reference sibling tools for comparison, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has zero parameter descriptions, so the description's Args section fully compensates by defining make, model, and year with examples. This provides clear semantics beyond the raw schema.
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 clearly states the tool retrieves vehicle safety recalls from NHTSA, with search by make/model/year. This distinguishes it from sibling tools like get_complaints and get_crash_statistics, which address different NHTSA datasets.
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?
The description explains the search capability and specifies that at least one filter should be provided, giving clear context for invocation. However, it does not explicitly mention when to choose this over sibling tools or when not to use it.
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.6/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the key behavioral caveat that NHTSA complaints are not geocoded by state, and clarifies that state is used only for crash statistics. It also describes the returned content (recalls, top makes). This goes well beyond the schema, though it does not mention rate limits or error handling.
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 well-structured and appropriately sized: a clear opening sentence, a concise caveat, and a focused Args section. Every sentence adds value, and the most important information is front-loaded.
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?
The tool has a simple input (one state parameter) and an output schema (not shown but present). The description covers what the tool returns, the data sources, and the key limitation. It provides sufficient context for an agent to select and invoke the tool effectively, especially given the sibling tools that focus on narrower data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the schema has 0% description coverage, the description provides detailed parameter semantics in the Args section: format ('Two-letter state abbreviation'), examples ('CA', 'TX'), and purpose ('Used for crash statistics; complaint data is national'). This fully explains the parameter beyond the bare schema definition.
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?
Description uses a specific verb ('Get') and resource ('vehicle safety profile'), and clearly states the content: national complaint and recall trends, most recent recalls, top complained-about makes. It also distinguishes itself from siblings by explicitly noting that complaints are national, not state-level, which differentiates it from get_crash_statistics and get_recalls.
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?
The description provides clear usage context: 'as context for local community safety assessments' and explains the data limitation (complaints not geocoded). It implies when to use this tool (when needing a national composite profile) but does not explicitly list alternatives or exclusions. This is strong but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- Added
complaint_trends_by_component - Added
local_vehicle_safety_profile
4 tool updates
- First observed
decode_vin - First observed
get_complaints - First observed
get_crash_statistics - First observed
get_recalls
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TDQS
A4.2/5.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. complaint_trends_by_component analyzes component-level trends, decode_vin decodes VINs, get_complaints retrieves complaint details, get_crash_statistics provides fatal crash data, get_recalls fetches recall information, and local_vehicle_safety_profile combines national trends with local crash data. The boundaries between tools are well-defined, preventing misselection.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with minor deviations. Most tools use get_ or decode_ prefixes (get_complaints, get_recalls, decode_vin), but complaint_trends_by_component and local_vehicle_safety_profile deviate slightly by using descriptive phrases instead of simple verbs. However, all names are snake_case and clearly describe their functions, maintaining readability.
Tool Count5/5
With 6 tools, the count is well-scoped for the NHTSA vehicle safety domain. Each tool earns its place by covering distinct aspects: VIN decoding, complaint retrieval, recall searches, crash statistics, trend analysis, and safety profiling. This provides comprehensive coverage without being overwhelming or too sparse.
Completeness5/5
The tool surface provides complete coverage for the NHTSA vehicle safety domain. It includes data retrieval (complaints, recalls, crash stats), analysis (trends, safety profiles), and utility functions (VIN decoding). There are no obvious gaps—agents can perform typical workflows like assessing vehicle safety, investigating defects, or analyzing crash data without dead ends.