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Glama

Texas DMV

by io.github.pipeworx-io
OverviewAdmin

Tx Dmv Vehicle Registrations

tx_dmv_vehicle_registrations
Read-onlyIdempotent

Count vehicles registered in Texas from the Texas DMV (TxDMV) registration series: total vehicles registered statewide in a fiscal year, split into passenger cars, pickup trucks of one ton or less, and motorcycles, each with its share of the fleet. Answers "how many vehicles are registered in Texas", "how many motorcycles are registered in Texas", "how many pickup trucks are registered in Texas", and growth questions across years such as how the Texas fleet changed from 2001 to 2021. TxDMV publishes this series as one statewide row per fiscal year, covering fiscal years 2001 through 2021, so every response reports its fiscal year and vintage. For a ZIP-code or county breakdown of a registered fleet, ca_dmv_vehicle_registrations covers California at ZIP × make × model-year × fuel grain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax fiscal years to return, newest first (default 10, max 21).
fiscal_yearNoTexas state fiscal year, 2001–2021, e.g. "2021". Omit for the most recent years, newest first.

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations declare read-only, open world, idempotent, non-destructive. Description adds that data is one row per fiscal year, covers 2001–2021, and responses include vintage, providing behavioral context beyond annotations.

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 purpose, includes examples and alternatives, and every sentence adds value without unnecessary fluff.

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 no output schema, the description adequately covers data source, time range, granularity, response contents, and alternative tool, making it fully complete for a simple count 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?

Schema coverage is 100% with clear parameter descriptions. The description adds context about response structure (fiscal year, vintage, shares) but does not significantly enhance parameter understanding beyond 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 counts vehicles registered in Texas with breakdowns by type and provides example questions. It distinguishes itself from the California breakdown tool mentioned in the description.

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 states the tool answers specific questions about Texas registrations and directs users to ca_dmv_vehicle_registrations for granular data, providing clear when-to-use and when-not-to-use guidance.

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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