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Ct Dmv Ev Registrations

ct_dmv_ev_registrations
Read-onlyIdempotent

Count electric vehicles registered in Connecticut from the Connecticut DMV's vehicle-level registration file, broken down by town, make, model, model year, body style, fuel code, colour, plate type or registration usage. Answers "how many electric vehicles are registered in Connecticut", "how many Teslas are registered in Connecticut", "which Connecticut town has the most EVs", "most popular EV models in Connecticut", and "how many EVs registered in Stamford". Built from 60,489 individual registration records, a snapshot covering electric vehicles registered as of February 2025, so every response reports that vintage. For the whole Connecticut vehicle fleet including petrol and diesel, ca_dmv_vehicle_registrations covers California's full fuel mix by ZIP.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoConnecticut town of the registered owner, e.g. "Stamford", "West Hartford".
makeNoVehicle make, matched as a substring, e.g. "Tesla", "Toyota", "Jeep".
limitNoMax rows to return (default 25, max 200).
modelNoVehicle model, matched as a substring, e.g. "Model 3", "Wrangler".
group_byNoBreakdown dimension: city (default), make, model, model_year, vehicle_type, body, fuel, color, plate_type, usage, category.
model_yearNoFour-digit model year, e.g. "2024".
vehicle_typeNoVehicle type as Connecticut records it, matched as a substring, e.g. "Passenger", "Truck".

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate the tool is read-only and non-destructive. The description adds valuable context about the data source (60,489 records, snapshot as of February 2025), ensuring the agent understands the temporal scope. 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 concise, with no redundant information. It front-loads the main purpose and includes examples, data source context, and sibling distinction. A slight improvement could be clearer sentence breaks, but overall efficient.

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?

For a read-only counting tool with no output schema, the description adequately covers purpose, parameters, data vintage, and alternatives. It could mention behavior for no results or empty filters, but not essential.

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?

Schema description coverage is 100%, so baseline is 3. The description does not add significant new parameter semantics but provides context about the grouping dimension and example usage. It meets the minimum for a well-described 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 electric vehicles registered in Connecticut, broken down by various dimensions. It provides specific example questions and distinguishes from the sibling tool for California vehicles.

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 when to use this tool (for Connecticut EV registrations) and when not to (for California or full fleet), naming the alternative tool ca_dmv_vehicle_registrations.

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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