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New York State DMV

by io.github.pipeworx-io
OverviewAdmin

Ny Dmv Vehicle Registrations

ny_dmv_vehicle_registrations
Read-onlyIdempotent

Count vehicles registered in New York State from the NYS DMV record-level registration file, broken down by county, ZIP code, city, make, model year, body type, registration class or fuel type. Each of the 12.6 million source rows is one registration record, so this answers "how many cars are registered in Kings County", "how many Teslas are registered in New York", "how many pickup trucks are registered in ZIP 11201", "which New York county has the most registered vehicles", and how many registrations carry a scofflaw, suspension or revocation flag. County names are unabbreviated and upper case, with Brooklyn filed as KINGS. For electric-vehicle share use ny_dmv_ev_adoption; for a DMV office address use ny_dmv_offices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipNoFive-digit New York ZIP code, e.g. "11201".
cityNoCity or town of the registrant, e.g. "BROOKLYN", "BUFFALO", "ALBANY".
makeNoVehicle make; pass the everyday name, e.g. "TOYOTA", "TESLA", "FORD", "MERCEDES". New York stores makes cut to five characters (TOYOTA is filed as TOYOT), and the full name is cut to match automatically.
limitNoMax rows to return (default 25, max 200).
countyNoNew York county name, unabbreviated and matched case-insensitively. Brooklyn is "KINGS", Manhattan is "NEW YORK", Staten Island is "RICHMOND", Queens is "QUEENS", the Bronx is "BRONX".
group_byNoBreakdown dimension: county, zip, city, fuel, make, model_year, body_type, registration_class, record_type, state. Defaults to county.
body_typeNoBody-type code, e.g. "SUBN" (SUV/station wagon), "4DSD" (4-door sedan), "PICK" (pickup), "MCY" (motorcycle), "VAN".
fuel_typeNoFuel; plain words are mapped to New York's codes, e.g. "gas" → GAS, "diesel" → DIESEL, "electric" → ELECTRIC, "cng" → COMP N/G. The published codes are GAS, DIESEL, ELECTRIC, NONE, FLEX, COMP N/G, OTHER, PROPANE.
model_yearNoFour-digit model year, e.g. "2024".
record_typeNoWhich register to read: VEH, TRL, BOAT, SNOW. Defaults to VEH (road vehicles); TRL is trailers, BOAT is vessels, SNOW is snowmobiles.
registration_classNoRegistration class code, e.g. "PAS" (passenger), "COM" (commercial), "MOT" (motorcycle), "SRF" (semi-trailer).
scofflaw_indicatorNoSet "Y" to count only registrations flagged as scofflaw (unpaid tickets), "N" for the rest.
revocation_indicatorNoSet "Y" to count only revoked registrations, "N" for the rest.
suspension_indicatorNoSet "Y" to count only registrations under suspension, "N" for the rest.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds valuable context: source file type, 12.6 million rows, county name quirks (Brooklyn as KINGS). No contradictions.

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?

Description is front-loaded with purpose and examples. While somewhat long, every sentence adds value. 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?

With 14 parameters and no output schema, the description covers usage, examples, county quirks, and sibling references adequately. Provides sufficient context for correct invocation.

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?

Schema coverage is 100%, but description adds meaning beyond schema for many parameters: county name mappings, make truncation, fuel type mapping, record_type defaults. This compensates beyond baseline 3.

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 states it counts vehicles registered in NYS broken down by multiple dimensions, with specific examples like 'how many cars are registered in Kings County'. It also distinguishes itself from sibling tools like ny_dmv_ev_adoption and ny_dmv_offices.

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 explicitly tells when to use alternatives: 'For electric-vehicle share use ny_dmv_ev_adoption; for a DMV office address use ny_dmv_offices.' It also explains the data source and that each row is a registration.

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

All New York State DMV toolsInspect
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