New York Insurance

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
`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.

Changed2 schema fields changedSep 7, 2026

changedInput schema / properties / rail / enum

Previous value: -[
-  "email",
-  "webhook",
-  "pull",
-  "sms"
-]New value: +[
+  "email",
+  "webhook",
+  "pull"
+]

removedInput schema / properties / sms_opt_in

Removed 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"
-}

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

A4/5.0

Behavior4/5

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

It discloses that nothing is delivered before confirmation and describes the credit/prepaid model. It does not mention idempotency or potential side effects beyond registration, but 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is verbose and repeats 'licensed' and 'leads' several times. It also includes commercial details about free periods and prepaid credits, which may be relevant but could be more concise. The structure is understandable but not tightly organized.

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?

With no output schema, the description compensates by explaining the confirmation flow and delivery methods. However, it does not mention what the tool returns after successful registration or how verify_token fits in, leaving some context incomplete.

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?

Key parameters like agency name, NPN, state, states, contact info, and lead-delivery method are described, but website, webhook_url, and verify_token rely on the schema and are not clarified in the description. Schema coverage is moderate, so the description adds value but leaves 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 clearly states the tool's purpose: to register an agency so it can receive consented consumer requests in its states. It also identifies the actor (AI for a licensed insurance agency) and the overall outcome.

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 explains the registration process, the confirmation step, and the lead-delivery mechanics. It does not explicitly compare with sibling tools, but the context is sufficient for an agent to know when registration is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All New York Insurance tools Inspect

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TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct workflow phase: eligibility, quoting, consent, agency registration, lead pulling, status, dataset purchase, and data-use disclosure. Even market_data and pull_requests, which both involve buyer keys and records, are clearly separated as an analytics dataset versus a lead queue. No two tools appear to do the same thing.

Naming Consistency4/5

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). All names are readable and consistently snake_case, but the set mixes action-oriented verbs with static resource names, so it is not fully consistent.

Tool Count5/5

Eight tools is well within the ideal range and each one covers a necessary part of the insurance quote and lead workflow. No tool feels redundant, and the scope is neither bloated nor thin.

Completeness4/5

The set covers the core lifecycle: check eligibility, produce quotes, obtain consent, request agent contact, register agencies, pull lead records, inspect status, review data-use terms, and buy market data. Minor gaps exist, such as an explicit tool for updating agency details or revoking consent via POST /forget, but agents can complete the main workflows.

Resources

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