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Pa Dmv

by io.github.pipeworx-io
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Pa Dmv Ev Adoption By Zip

pa_dmv_ev_adoption_by_zip
Read-onlyIdempotent

Break Pennsylvania electric-vehicle registrations down to the ZIP code, from PennDOT Driver & Vehicle Services: battery-electric, plug-in hybrid, fuel-cell and conventional hybrid counts for each of roughly 1,830 Pennsylvania ZIP codes, with that ZIP code's total registered vehicles and plug-in share. Answers "how many EVs are registered in ZIP 19103", "which Pennsylvania ZIP code has the most electric vehicles", "EV share in ZIP 15213", and neighbourhood-level adoption questions that a county figure averages away. Supply zip for one ZIP code, or omit it to rank them. For county figures and the statewide Pennsylvania total use pa_dmv_ev_adoption.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "zip": "19103",
      "year": "2026"
    },
    "name": "pa_dmv_ev_adoption_by_zip"
  }
}

Input Schema

NameRequiredDescriptionDefault
zipNoFive-digit Pennsylvania ZIP code, e.g. "19103", "15213".
yearNoRegistration year, one of 2023, 2024, 2025, 2026. Defaults to 2026.
limitNoMax ZIP codes to return (default 30, max 500).
quarterNoQuarter of that registration year: "Q1", "Q2", "Q3" or "Q4". Defaults to the latest quarter PennDOT has actually filed for that year.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark it as read-only and idempotent; the description adds rich behavioral context: data source (PennDOT), types of vehicles counted, total vehicles, plug-in share, scope (1,830 ZIP codes). 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?

The description is one focused paragraph, front-loading the purpose and key details. Slightly lengthy but no wasted words; clear and efficient.

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?

For a 4-parameter tool with no output schema, the description fully explains functionality, parameter behavior, return data, and sibling relationship. Complete enough for an AI agent to use 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% with descriptions, baseline 3. The description adds value by explaining how parameters are used (e.g., omit zip to rank, default year, quarter), enhancing 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 breaks down EV registrations by ZIP code, gives specific examples (e.g., 'how many EVs in ZIP 19103'), and distinguishes from the sibling tool pa_dmv_ev_adoption for county/statewide data.

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?

Explicitly tells when to use this tool (ZIP-level queries) vs the sibling for county/statewide, and provides parameter usage guidance: supply 'zip' for one ZIP or omit to rank.

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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