Newyorkautoquotes

Register an insurance agency as a buyer

register_agency

For an AI working for a licensed insurance agency: register the agency to receive consented consumer requests in its states. Needs the agency name, the producer NPN (National Producer Number) and its state, the states it is appointed in, the licensed contact's name, email and mobile, and how leads should arrive (email, webhook, or pull by key). The licensed contact confirms by a link sent to their email; nothing is delivered before that. The first agencies in a state receive leads free for a founding period, then prepaid credits by card. Read /join for the terms.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`npn`	Yes	NPN (National Producer Number). Your NPN is on your licence and at nipr.com. Digits only.
`rail`	No	How leads arrive: email, an https webhook (HMAC-signed), or pull by key over MCP/REST	email
`states`	No	Two-letter states the agency is appointed in; defaults to npn_state
`website`	No
`npn_state`	Yes	Two-letter state that issued the licence
`sms_opt_in`	No	Optional. true only if the agency contact expressly agrees to this exact text: I agree to receive recurring operational text messages from CoverIntent by TheChattyAI about my agency account and consented lead deliveries. Message frequency varies. Message and data rates may apply. Reply HELP for help or STOP to cancel. Consent is not a condition of registering or buying.
`agency_name`	Yes
`webhook_url`	No
`contact_name`	Yes	The licensed contact, who confirms by email
`verify_token`	No	Optional: from POST /v1/verify/check after the contact enters the code texted to them
`contact_email`	Yes
`contact_phone`	Yes	US mobile

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 7, 2026

addedInput schema / properties / sms_opt_in

Added value: +{
+  "default": false,
+  "description": "Optional. true only if the agency contact expressly agrees to this exact text: I agree to receive recurring operational text messages from CoverIntent by TheChattyAI about my agency account and consented lead deliveries. Message frequency varies. Message and data rates may apply. Reply HELP for help or STOP to cancel. Consent is not a condition of registering or buying.",
+  "type": "boolean"
+}

First observedSep 6, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations only convey readOnlyHint=false and non-destructive. The description adds genuine behavioral context beyond these: the licensed contact must confirm via an emailed link, nothing is delivered before confirmation, and the billing model (founding free period then prepaid credits). It does not contradict the annotations; readOnlyHint=false aligns with the write operation of registering.

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 front-loads the core purpose before enumerating requirements, confirmation flow, and billing. It is dense at roughly 100 words but each sentence adds information given the 12-parameter surface. Slightly long but purposeful.

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 registration tool with 12 parameters and no output schema, the description covers the purpose, required data, lead-delivery rails, the email-confirmation prerequisite, and billing terms. Minor gaps remain around verify_token, sms_opt_in, and webhook HMAC details, but the core flow an agent must understand to act correctly is 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 coverage is 67%, so the description adds framing value: it defines NPN (National Producer Number), frames the 'rail' parameter as 'how leads should arrive (email, webhook, or pull by key)', and clarifies the contact is the licensed one who confirms. However, four parameters (website, webhook_url, sms_opt_in, verify_token) are undocumented in both schema and description, so it does not fully compensate for the coverage gap.

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 (register) and resource (the agency as a buyer to receive consented consumer requests in its states), which is unambiguous. It also clearly distinguishes from siblings: agency_status, pull_requests, get_quotes and check_eligibility are all operational tools, whereas this is the onboarding entry point.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The opening 'For an AI working for a licensed insurance agency' provides a clear audience gate, and the purpose (to become a buyer) implies the usage context. However, it does not explicitly name alternatives or when-not-to-use conditions, such as routing to check_eligibility for a pre-check or agency_status for post-registration status.

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

A3.9/5.0

Disambiguation5/5

Each tool targets a distinct part of the workflow: eligibility pre-check, quote generation, consent/terms, agency registration, queue draining, status, market data, and agent contact. Even related tools like check_eligibility and get_quotes are clearly separated by their role in the process.

Naming Consistency3/5

All names are snake_case and readable, but the pattern is mixed: five tools start with an imperative verb (check_, get_, pull_, register_, request_) while three are noun phrases (agency_status, data_use_terms, market_data). This is inconsistent enough to prevent a higher score, though not chaotic.

Tool Count5/5

Eight tools is well within the ideal range and each one maps to a meaningful operation in the platform: eligibility, quotes, consent, registration, queue management, status, terms, and market data. No tool feels redundant or unnecessary.

Completeness4/5

The core consumer and agency workflows are covered: check eligibility, get quotes, request agent contact, register agency, pull requests, and monitor status. Minor gaps exist, such as no MCP tool for consent revocation or agency profile updates, but they do not block the main use cases.

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