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Oklahomaautoquotes

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

A4.3/5.0
Behavior4/5

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

The description adds meaningful behavior beyond annotations: up to 4 licensed agents receive request and contact details, sell_identity extends sharing to marketing partners, nothing leaves without consent, delivery/refusal is receipted, and POST /forget revokes. This is useful privacy and side-effect context. There is no contradiction with the 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 dense but efficient: purpose in the first sentence, prerequisites and consent mechanics in the second, and recipient/revocation behavior in the third. The repetition of the consent requirement is acceptable given the sensitivity of the operation. It errs slightly on length, but each clause contributes value.

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 complex tool with no output schema, the description covers consent flow, scoping, recipient limits, receipting, and revocation. The main gaps are the absence of a return-payload description and lack of detail on optional contact-channel parameters. Still, the agent has enough to select and invoke the tool correctly.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is low (44%), so the description compensates by explaining consent modes ('granted by the human, presented by you, or confirmed... at the consent URL'), the provenance of quote_id, and the meaning of the scope enum. It does not describe optional fields like best_time, email_address, or preferred_channel, but the required fields are well covered.

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 outcome: 'Connect the consumer with licensed insurance agents who can quote firm and bind.' It also identifies the required source of the quote_id ('from get_quotes'), which helps distinguish this tool from siblings like get_quotes or register_agency. The title reinforces the consent requirement without ambiguity.

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 prerequisites: quote_id, full_name, phone_number, and explicit consent. It also explains scope behavior for contact_consumer versus sell_identity, giving the agent actionable routing guidance. It does not explicitly name an alternative tool to use instead, but the dependency on get_quotes and the consent condition provide adequate usage context.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct part of the workflow: eligibility, quoting, consent, agency registration, status, queue draining, and market data. There is no meaningful overlap, and descriptions clearly separate consumer-facing from agency-facing operations.

Naming Consistency4/5

Most tools follow a verb_noun pattern like check_eligibility, get_quotes, register_agency, and pull_requests. A few are noun phrases such as agency_status, market_data, and data_use_terms, but all are lowercase snake_case and predictable.

Tool Count5/5

Eight tools fit the server's scope well: the consumer quote/contact flow, agency lead handling, terms disclosure, and market data each have dedicated tools. None feel redundant, and the count is neither thin nor bloated.

Completeness4/5

The core lifecycle is well covered: check eligibility, get quotes, request agent contact, register an agency, check agency status, and pull requests. Minor gaps exist around explicit revocation/forget and agency updating, though these are hinted at in descriptions.

Resources