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Glama

Newyorkautoquotes

Get indicative quotes

get_quotes
Read-only

Return indicative auto insurance prices from multiple carriers, where this entity is licensed to show them. Takes rating facts only — no name, phone, email, SSN or licence number is required for an indicative price. Where we are not licensed to show prices, the request is still registered and a quote_id returned so licensed agents can quote it. If facts are missing the server asks for exactly what it needs and nothing more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoOptional; derived from the ZIP when absent
coverageYes
garaging_zipYesFive-digit ZIP where the vehicle is kept
vehicle_yearYes
date_of_birthYes
annual_mileageNo
violations_3yrNo
years_licensedNo
prior_continuousNo
vehicle_make_modelYes

Schema Changelog

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

  1. First observed

TDQS

A3.6/5.0
Behavior1/5

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

The description says that for unlicensed territories "the request is still registered and a quote_id returned," which describes a state-changing side effect, yet the annotations declare readOnlyHint=true. This is a direct contradiction: registering a request and returning a quote_id implies creating state, not a read-only operation.

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?

Three sentences, each earning its place: the first states the core purpose, the second clarifies the minimal data requirement, and the third explains fallback behavior and error handling. No redundancy and the most important information is front-loaded.

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?

With no output schema, the description adequately conveys the key return values: prices and a quote_id when unlicensed. It also covers missing-facts behavior. It could be more explicit about the exact response shape, but the essential behavioral path is complete for an agent.

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 only 20%, so the description must compensate for most parameters. It usefully frames all inputs as "rating facts" and excludes PII, but it does not explain the meaning of several parameters like prior_continuous, violations_3yr, or annual_mileage beyond their self-explanatory names. This is partial compensation, not complete.

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 opens with a specific verb and resource: "Return indicative auto insurance prices from multiple carriers." It also clarifies the licensing condition, which distinguishes this from siblings like market_data or check_eligibility by stating exactly what kind of output is produced.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly explains when prices will be returned and what happens when the entity is not licensed, plus it explicitly states that only rating facts are needed and that PII is not required. It does not explicitly name alternative tools, but the context is clear enough for an agent to decide when to use it.

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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TDQS

A3.9/5.0
Disambiguation4/5

The tools mostly partition the workflow: eligibility, quotes, agent contact, agency registration/status, and data pulls each have distinct roles. Check_eligibility and agency_status both surface licensing state, and market_data/pull_requests both involve retrieving records, so a couple pairs need careful reading, but the descriptions disambiguate them.

Naming Consistency3/5

Most tools follow a verb_noun pattern (check_eligibility, get_quotes, register_agency, request_agent_contact, pull_requests), but agency_status, data_use_terms, and market_data are noun phrases with the action implied. The mixed style is readable but not fully consistent.

Tool Count5/5

Eight tools cover the main consumer, agency, and data workflows without redundancy or bloat. Each tool has a clear place in the domain.

Completeness4/5

The surface supports the main loop from eligibility to quote to consented agent contact, plus agency registration, queue draining, status, terms, and market data. Minor gaps exist, such as no tool for updating agency registration or explicitly managing credits, but they do not create dead ends.

Resources