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Nevadaautoquotes

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 provide generic hints, but the description discloses meaningful behavior: nothing leaves without consent, up to 4 agents receive contacts under default scope, marketing partners are possible under sell_identity, deliveries/refusals are receipted, and POST /forget revokes. This goes well beyond the structured fields and gives the agent important side-effect awareness.

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 every sentence adds value: purpose, prerequisites, scope behavior, consent boundary, and lifecycle/revocation. It is front-loaded with the core action and does not repeat schema field names mechanically.

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 9-parameter tool with nested consent and no output schema, the description covers the most important operational context: prerequisites, consent, scope, receipts, and revocation. It does not elaborate on optional contact fields or the exact response shape, but the supplied context is sufficient for safe invocation in most agent flows.

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 44%, so the description must compensate. It explains quote_id, name, phone, and consent, but leaves parameters like best_time, email_address, and preferred_channel without additional meaning in the description or schema. The consent object is clarified well, but several parameters remain undocumented.

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 uses a specific verb and resource: "Connect the consumer with licensed insurance agents who can quote firm and bind." It clearly separates this tool from siblings by anchoring it to quotable/bindable agent contact and explicitly requiring consent. The title reinforces the consent requirement without relying on it.

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 names the required prerequisite (quote_id from get_quotes), the required consumer approvals, and how consent can be obtained. It explains the two scope variants and their different sharing targets, but does not explicitly name sibling tools to exclude or state when not to use this tool.

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