Florida 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.

First observedSep 6, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Beyond annotations (readOnlyHint=false, openWorldHint=true), the description discloses the email confirmation flow, that nothing is delivered before confirmation, the founding-period free leads, and prepaid credits. This meaningfully supplements 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 a bit long but each sentence carries useful information: purpose, required fields, confirmation mechanism, delivery gating, and billing terms. It is front-loaded with the core action.

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 an 11-parameter registration tool with no output schema, the description covers the essential workflow, prerequisites, and post-registration behavior. It could mention response format or verification token, but what is included is sufficient for correct invocation.

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?

With schema coverage at 64%, the description explicitly enumerates the key fields: agency name, NPN and state, appointed states, licensed contact details, and lead delivery method. This compensates for uncovered schema entries, though it omits specifics like verify_token and webhook_url.

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: 'register the agency to receive consented consumer requests in its states.' It clearly differentiates from siblings by focusing on agency onboarding rather than quote retrieval or contact requests.

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?

It provides context by targeting 'an AI working for a licensed insurance agency' and lists prerequisites like NPN and licensed contact. However, it does not explicitly compare to sibling tools like request_agent_contact 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.

All Florida Insurance tools Inspect

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TDQS

A3.9/5.0

Disambiguation5/5

Each tool maps to a distinct step or resource: eligibility pre-check, quotes, consent/contact, agency registration, agency status, lead queue, market data, and data-use terms. Even the two quote-related tools are clearly sequenced with check_eligibility described as 'call this first'.

Naming Consistency3/5

Five tools use a clear verb_noun pattern (check_eligibility, get_quotes, pull_requests, register_agency, request_agent_contact), but three are bare noun phrases (agency_status, data_use_terms, market_data). All names are readable and consistently snake_case, but the verb-led convention is not uniform.

Tool Count5/5

Eight tools cover the main stages of an insurance marketplace: eligibility, quotes, consumer consent, agency onboarding, lead delivery, market data, and terms. The count is well-scoped and each tool earns its place.

Completeness4/5

The core consumer journey (eligibility → quotes → consented agent contact → lead delivery) and agency workflow (register → status → pull requests) are well covered, along with market data and terms. Minor gaps exist, such as no explicit update/removal for agency registration and consent revocation only mentioned as POST /forget rather than a tool.

Resources

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