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Glama

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

A4.4/5.0
Behavior5/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses valuable behavior: no PII is required, unlicensed requests still produce a quote_id for licensed agents, and the server asks only for missing facts. These details meaningfully shape agent expectations. No contradiction with annotations exists.

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 concise sentence, each adding necessary context: the main behavior, the PII limitation, and the missing-facts behavior. It is front-loaded with the core purpose and contains no filler.

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 quote tool with no output schema, the description covers the key operational context: licensing, required input type, and the fallback registration behavior. It could be slightly richer about the exact response shape beyond the quote_id, but the remaining ambiguity is minor.

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 should compensate. It does add useful high-level meaning by explaining that only rating facts are accepted and PII must be excluded. However, it does not elaborate on ambiguous parameters like prior_continuous or the coverage enum values, leaving some semantic gaps.

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 states a specific verb and resource: it returns indicative auto insurance prices from multiple carriers. It also clarifies the licensing condition, which distinguishes this from related tools like check_eligibility or request_agent_contact. The purpose is unmistakable.

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 clearly establishes when this tool is appropriate: when indicative pricing is needed and only rating facts are available. It also explains the unlicensed-carrier fallback behavior, which helps an agent decide what to do with the result. It does not explicitly name sibling alternatives, but the context is strong enough.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct resource/action: eligibility check, quotes, consent terms, contact request, agency registration, agency status, queue pull, and market data. Even pull_requests and market_data are clearly separated as private consented queue vs. de-identified public dataset. No two tools appear to do the same thing.

Naming Consistency3/5

Five tools follow a verb_noun pattern (check_eligibility, get_quotes, pull_requests, register_agency, request_agent_contact), but three are noun phrases (agency_status, data_use_terms, market_data). The names are readable and underscore-consistent, yet the mixed verb/noun convention is noticeable.

Tool Count5/5

Eight tools cover the consumer quote/contact flow, agency lifecycle, queue delivery, market data, and privacy terms without bloat. Each tool earns a place and the set is within the ideal 3-15 range.

Completeness4/5

The core lifecycle is covered: eligibility, quotes, consent, contact request, agency registration, status, and pulling routed requests. Minor gaps exist—no in-MCP update/delete for agencies and no receipt/revocation tool beyond the mentioned POST /forget—but agents can work around them.

Resources