Skip to main content
Glama

New York State DMV

by io.github.pipeworx-io
OverviewAdmin

Ny Dmv Ev Adoption

ny_dmv_ev_adoption
Read-onlyIdempotent

Measure electric-vehicle adoption in New York State: how many registered vehicles carry the ELECTRIC fuel code in each county, ZIP code, city, make or model year, and what share of all registered vehicles in that scope they represent. Reads the NYS DMV record-level registration file, so it answers "how many EVs are registered in New York", "EV share in Westchester County", "which New York county has the most electric vehicles", "how many electric Teslas are registered in Brooklyn", and how EV registrations break down by model year. New York files every plug-in vehicle under one ELECTRIC code, so battery-electric and plug-in hybrid vehicles arrive combined; Washington separates them, in wa_dmv_ev_population.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipNoFive-digit New York ZIP code, e.g. "10583".
cityNoCity or town of the registrant, e.g. "BROOKLYN", "ROCHESTER".
makeNoVehicle make; pass the everyday name, e.g. "TESLA", "RIVIAN", "CHEVROLET". New York stores makes cut to five characters (CHEVROLET is filed as CHEVR), and the full name is cut to match automatically.
limitNoMax rows to return (default 25, max 200). Raise it to 62 to cover every New York county.
countyNoNew York county name, unabbreviated and upper case in the source. Brooklyn is "KINGS", Manhattan is "NEW YORK", Staten Island is "RICHMOND".
group_byNoBreakdown dimension: county, zip, city, fuel, make, model_year, body_type, registration_class, record_type, state. Defaults to county.
model_yearNoFour-digit model year, e.g. "2025".

Schema Changelog

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

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds value by explaining data source (NYS DMV record-level file) and the combined ELECTRIC code limitation.

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 well-structured with examples and contrast, but slightly verbose. Front-loads the main action and efficiently adds important caveats.

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?

No output schema, but description explains return values (count, share, breakdowns). For a read-only query tool with good annotations, this is sufficiently complete.

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?

Input schema has 100% coverage with descriptions. Description adds context beyond schema (e.g., make truncation to 5 chars, ELECTRIC code meaning, group_by options), which compensates for no output 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 measures EV adoption in NYS with various breakdowns. It distinguishes itself from sibling tools like ny_dmv_vehicle_registrations by focusing on EV-specific metrics, but does not explicitly name alternatives.

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?

Provides example queries and contrasts with Washington's tool, but does not explicitly specify when to use this tool versus siblings like ny_dmv_vehicle_registrations.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources