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Glama

Nevadaautoquotes

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

A3.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds meaningful behavior: no PII is collected for indicative pricing, unlicensed requests are still registered and yield a quote_id, and the server asks for only the missing facts. These are non-obvious behaviors an agent needs to know and are not present in the schema or annotations.

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 reasonably concise and front-loaded with the core purpose. All four sentences carry useful information, though the unlicensed-agent explanation could be tightened slightly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 10 parameters, 20% schema coverage, and no output schema, the description is not fully complete. It does mention the key quote_id outcome and the licensing caveat, but it leaves many parameter meanings and the overall response shape unspecified. It is adequate but has clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 undocumented parameters. It groups inputs as 'rating facts only' and clarifies that PII is not needed, but it does not explain most parameters such as annual_mileage, violations_3yr, years_licensed, or prior_continuous. The server-asks-for-missing-facts note does not help an agent supply correct values up front.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns indicative auto insurance prices from multiple carriers, with a licensing condition. This is a specific verb+resource, but it does not explicitly differentiate from siblings like market_data or check_eligibility, so it misses the highest bar.

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?

The description gives clear usage context: it takes rating facts only, does not require PII, and handles unlicensed situations by returning a quote_id for licensed agents. However, it does not name alternative tools or state when not to use this one, so exclusion guidance is implied rather than explicit.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a clearly distinct operation: eligibility preflight, quote generation, consumer contact, agency registration/status, queue draining, market data, and terms. Potential proximity between check_eligibility and get_quotes is resolved by explicit sequencing and different outputs.

Naming Consistency3/5

Most tools follow a verb_noun pattern such as check_eligibility, get_quotes, register_agency, pull_requests, and request_agent_contact. However, agency_status, market_data, and data_use_terms are noun-style resource names, creating a noticeable but still readable mix.

Tool Count5/5

Eight tools is well within the ideal range for a domain-focused server. Each tool serves a distinct function across quoting, consent, agency operations, queue handling, and data access, with no obvious redundancy.

Completeness4/5

Core workflows are well covered: eligibility, indicative quotes, consumer-agent connection, agency onboarding/status, lead queue draining, and market data purchase. Minor gaps remain, such as no tool to update or remove an agency, and no tool to inspect an individual consumer request or consent status; revocation is only mentioned as an external POST endpoint.

Resources