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Maryland MVA

by io.github.pipeworx-io
OverviewAdmin

Md Dmv Vehicle Registrations

md_dmv_vehicle_registrations
Read-onlyIdempotent

Count vehicles registered in Maryland by county for a given month, from Maryland Motor Vehicle Administration (MVA) data published monthly since 2020. Returns each county's registered-vehicle count plus the genuine statewide total for that month and each county's share of it, so it answers "how many vehicles are registered in Montgomery County Maryland", "which Maryland county has the most registered vehicles", "how many cars are registered in Maryland", and month-over-month trend questions via the month argument. Counts the whole registered fleet of every fuel type; for the electric and plug-in hybrid slice of it use md_dmv_ev_adoption.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax counties to return (default 30, max 200). Maryland publishes 25 rows per month, so the default returns them all.
monthNoMonth as YYYY/MM, e.g. "2026/06". Defaults to the most recent month Maryland has published. The series starts at 2020/07.
countyNoMaryland county or Baltimore City, e.g. "Montgomery", "Prince George's", "Baltimore City". Apostrophes and periods are optional.

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 already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Beyond these, the description adds behavioral context: it counts whole fleet, returns genuine statewide total and each county's share, and specifies the data is from MVA published monthly since 2020. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero waste. Front-loads purpose, then details outputs and usage with example questions, ends with sibling reference. Every sentence is essential and well-structured.

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 the tool's simplicity and no output schema, the description fully explains return values (counts, total, share), usage context, and alternatives. It is complete for an agent to select and invoke the tool correctly.

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% (baseline 3). The description adds practical meaning: month defaults to latest and series starts at 2020/07, limit default returns all 25 rows, county examples include apostrophe handling. This adds value beyond 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?

Description clearly states the verb 'count' and resource 'vehicles registered in Maryland by county for a given month'. It specifies the data source (MVA), temporal range (since 2020), and explicitly differentiates from sibling md_dmv_ev_adoption.

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 says to use this tool for total fleet counts and points to md_dmv_ev_adoption for electric/hybrid data, providing clear context with alternatives. It also implies usage for month-over-month trends via the month argument.

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

All Maryland MVA toolsInspect
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