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Californiacarquotes

Connect with licensed agents (consent required)

request_agent_contact

Connect the consumer with licensed insurance agents who can quote firm and bind. Needs the quote_id from get_quotes, the consumer's name and phone, and the consumer's explicit consent to be contacted — granted by the human, presented by you, or confirmed by the human directly at the consent URL the elicitation returns. Under scope contact_consumer up to 4 licensed agents receive the request and contact details; sell_identity additionally permits sharing with marketing partners. Nothing leaves without the consent. Every delivery and refusal is receipted, and POST /forget revokes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
consentYes
quote_idYesFrom get_quotes
best_timeNo
full_nameYes
phone_numberYesUS mobile or landline
verify_tokenNoOptional. From POST /v1/verify/check after the consumer enters the code texted to them. A verified number sells at the verified price and is contacted first.
email_addressNo
street_addressNoOptional. Lets a licensed agent answer firm.
preferred_channelNo

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?

Annotations only convey openWorld=true, non-idempotent, non-read-only, and non-destructive. The description adds significant behavioral disclosure: consent gating, scope-dependent sharing with up to 4 agents, optional marketing-partner sharing under sell_identity, receipting of deliveries/refusals, and a POST /forget revocation path. This is valuable context beyond what annotations provide.

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?

The description is dense but efficient: purpose, prerequisites, scope behavior, consent rule, and revocation are all covered in three tight sentences. There is no filler or repetition of schema content.

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?

Given the absence of an output schema and the tool's sensitive consent and sharing behavior, the description does well by explaining prerequisites, scope effects, and revocation. It does not describe the response/return shape or how the consent URL is obtained, which leaves a small but notable gap.

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?

With only 44% schema coverage, the description compensates for the most important parameters: quote_id, full_name, phone_number, and the consent object, including how consent can be granted. However, it does not explain optional parameters like best_time, email_address, street_address, preferred_channel, or verify_token, so the compensation is incomplete for a 9-parameter tool.

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: connect the consumer with licensed insurance agents, and clarifies the agents can 'quote firm and bind.' It also ties the tool to the quote_id prerequisite from get_quotes, distinguishing it from sibling tools like check_eligibility and market_data.

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 states preconditions: quote_id must come from get_quotes, and explicit consumer consent is required. It also explains how scope changes behavior. However, it does not name alternative tools or explicitly state when not to use this tool, so it stops short of full when/when-not guidance.

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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