Skip to main content
Glama

Nj Mvc Vehicle Inspections

nj_mvc_vehicle_inspections
Read-onlyIdempotent

Measure how often cars pass or fail New Jersey vehicle inspection, from the Motor Vehicle Commission's record-level results file — 2,314,032 individual tests, each carrying make, model, model year, fuel, and separate emissions and safety verdicts. Returns test counts with pass rate, fail rate, and how many failures were emissions rather than safety, grouped by make, model, model year, fuel, body style or test type. Answers "which cars fail NJ emissions inspection most", "Toyota Camry NJ inspection failure rate", "do older cars fail New Jersey inspection more often", "NJ inspection pass rate by make", and "how many diesels does New Jersey inspect". Statewide overall, roughly 10% of tests fail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fuelNoFuel; plain words map to New Jersey's codes, e.g. "gas" (GASO), "diesel" (DIES), "hybrid" (ELEG), "electric" (ELEC).
makeNoVehicle make, matched as a substring, e.g. "TOYOTA", "FORD", "HONDA".
sortNotests (default) ranks by volume; fail_rate ranks by the share of tests that failed.
limitNoMax groups to return (default 25, max 200).
modelNoVehicle model, matched as a substring, e.g. "CAMRY", "F150".
group_byNoBreakdown dimension: make, model, model_year, fuel, body_style, test_type, cylinders. Defaults to make.
min_testsNoDrop groups with fewer than this many tests before ranking, so a one-car model cannot top a failure-rate table. Default 100.
test_typeNoI for an initial test, R for a re-test. Defaults to every test.
model_yearNoFour-digit model year, e.g. "2015".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Description adds significant context beyond annotations: data source size, return structure (pass/fail rates, emissions vs safety), and overall fail rate. No contradiction with annotations.

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?

Single paragraph, front-loaded with purpose and examples. Dense but not verbose. Could be slightly more concise, but effective.

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 9 parameters, no output schema, but high schema coverage and annotations, the description sufficiently covers data source, output types, and example usage. Complete enough for agent to understand tool's capabilities.

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%; description provides overarching context but does not add meaning to individual parameters beyond what is in 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?

Verb 'measure' and resource 'inspection results' are clear. Example queries further clarify. Distinguishes from sibling tool 'nj_mvc_inspection_facilities'.

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?

Provides example questions demonstrating use cases, but does not explicitly state when not to use or mention alternatives. Context is clear enough for appropriate invocation.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.