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Glama

Californiacarquotes

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 'the request is still registered and a quote_id returned,' which implies creating a persistent record/ID, while annotations declare readOnlyHint=true. This is an annotation contradiction; the tool cannot be read-only if it registers requests and returns a new quote_id.

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?

Four focused sentences, each earning its place: main purpose, PII exclusion, unlicensed fallback, and server behavior. Information is front-loaded and there is no redundant phrasing.

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?

Covers the main output, licensing edge case, and error/request behavior, which is strong for a 10-parameter tool with no output schema. It still leaves the license-status success response format somewhat implicit, but the essential call and fallback semantics are present.

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. It usefully clarifies that only rating facts are needed and enumerates excluded PII, but it does not explain the optional fields (annual_mileage, violations_3yr, prior_continuous, etc.) or their roles, so the gap is only partially filled.

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?

States a specific verb ('Return'), a concrete resource ('indicative auto insurance prices'), and scope ('from multiple carriers, where this entity is licensed to show them'). This clearly distinguishes it from eligibility or agency-status tools.

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?

Conveys when to use it: when an indicative price is needed and only rating facts are available. It also explains the unlicensed-carrier fallback, but it does not explicitly name alternatives or state when not to use it, leaving some inference to the agent.

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 has a distinct purpose: eligibility checks, quotes, agency registration, market data, and lead retrieval are clearly separated. Even the potentially overlapping get_quotes and market_data are well-differentiated by their descriptions (individual quotes vs. de-identified dataset).

Naming Consistency4/5

Tool names use snake_case and are mostly descriptive, but the pattern is not uniform: some are verb_noun (check_eligibility, get_quotes, pull_requests, register_agency) while others are noun_noun (agency_status, data_use_terms, market_data). This is a minor inconsistency that does not hinder readability.

Tool Count5/5

With 8 tools, the set is well within the optimal 3-15 range for a focused service. Each tool addresses a necessary function for the car insurance quote and agency workflow, and none feel redundant.

Completeness5/5

The tool surface covers the core lifecycle: eligibility, quoting, agent contact, agency registration and status, data usage terms, market data access, and lead retrieval. No obvious missing operations for the stated domain are apparent.

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