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Ca Dmv Vehicle Registrations

ca_dmv_vehicle_registrations
Read-onlyIdempotent

Count vehicles registered in California from official DMV data, broken down by ZIP code, make, model year, fuel type or light/heavy duty. Each row is a count of registered vehicles from the California DMV "Vehicle Fuel Type Count by Zip Code" file, with annual snapshots back to 2019, so it answers "how many vehicles are registered in ZIP 90210", "how many Toyotas are registered in California", or "how many 2024-model-year vehicles are on California roads". For electric-vehicle share of the fleet use ca_dmv_ev_adoption; for a DMV office address use ca_dmv_offices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipNoFive-digit California ZIP code, e.g. "90210".
dutyNoVehicle weight class: "Light", "Heavy" or "Unk".
makeNoVehicle make, matched as a substring, e.g. "TESLA", "FORD".
yearNoSnapshot year, 2019–2026. Defaults to 2026 (the most recent).
limitNoMax rows to return (default 25, max 200).
group_byNoBreakdown dimension: fuel (default), zip, make, model_year, duty.
fuel_typeNoFuel/drivetrain; plain words are mapped to California's labels, e.g. "electric", "diesel", "gas", "hybrid", "plug-in hybrid", "hydrogen".
model_yearNoFour-digit model year, e.g. "2024".

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 safe read operation. Description adds detail about data source, annual snapshots from 2019, and default year (2026), enhancing transparency beyond annotations without contradiction.

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?

Description is three sentences, front-loaded with the main function, and contains no redundant information. Every sentence serves a purpose.

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?

Despite no output schema, the description explains each row's content, data source, and available snapshots. It also covers typical use cases and sibling tool boundaries, making it complete for agent understanding.

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?

Input schema has 100% coverage with full descriptions. Description mentions key parameters but does not add significant new meaning beyond the schema. Baseline score of 3 applies.

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 explicitly states the tool counts registered vehicles by various dimensions (ZIP, make, etc.) and provides concrete example queries. It also distinguishes sibling tools like ca_dmv_ev_adoption and ca_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 specifies when to use (count queries) and explicitly advises using ca_dmv_ev_adoption for EV share and ca_dmv_offices for office addresses, providing clear alternatives.

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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