Quotor

check_late_arrivals

Read-only

Save-the-sale tool — call when the customer signals dissatisfaction with the current options ("too expensive", "let me think", "what else do you have", "is this the best you can do", "I'll check elsewhere", "can you do better"). Some carriers (Foremost STAR, etc.) run slower than the initial set and may have come back with a better rate while the customer was deliberating. This tool:

Pulls a fresh read of all rated carriers.
Compares the current cheapest bundle/LOB price to the original first-quoted best.
Returns has_improvement=true ONLY if a late carrier beats the original by >= $100/yr OR >= 5% — a threshold meaningful enough to justify interrupting the customer.

CALL THIS ONLY WHEN:

check_quote_status has returned 'quoted' at least once (there's a baseline to compare).
The customer just expressed price resistance or hesitation (not a closed positive).
You have NOT called check_late_arrivals in the last 2 turns (don't spam).

DO NOT CALL WHEN:

The customer is actively committing ("yeah let's go with A") — that's a close moment, not a wavering moment.
The customer hasn't seen the initial options yet.

WHAT TO DO WITH THE RESULT:

has_improvement=true → narrate the improvement naturally ("Quick update — one more carrier just came in $X/yr cheaper. Want me to refresh your options?") and the iframe auto-updates with the new state.
has_improvement=false → acknowledge the customer's concern, pivot to value positioning against the options on the table. Don't bluff a "better one is coming" if it isn't. Use the carriers_still_pending count if relevant ("a couple more carriers are still finalizing in the background — I'll flag if anything comes in lower").

Input Schema

TableJSON Schema

Name	Required	Description	Default
`quote_id`	Yes	The quote session id to check for late-arriving carrier rates.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`new_options`	No

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds detailed behavioral context: the tool only returns improvement if the threshold is met (>= $100/yr or >= 5%), and it explains the internal logic (pulls fresh reads, compares cheapest). No contradictions.

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 well-structured with numbered steps and bullet points, making it easy to scan. It is front-loaded with the key purpose and conditions, though slightly longer than necessary. However, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the presence of annotations and output schema, the description is fully complete. It covers the purpose, triggers, thresholds, result handling, and fallback actions, leaving no ambiguity for an AI agent.

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 100%, and the single parameter quote_id is adequately described in the schema. The description does not add significant new information beyond what the schema provides, so a baseline score of 3 is appropriate.

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: 'Save-the-sale tool' for detecting late-arriving better rates when a customer shows dissatisfaction. It distinguishes itself from siblings like check_quote_status by focusing on late carriers and specific triggering conditions.

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

Usage Guidelines5/5

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

Provides explicit when-to-call conditions (e.g., after check_quote_status returns 'quoted', customer hesitation, not spammed) and when-not-to-call conditions (customer committing, hasn't seen options). Also includes guidance on what to do with the result, making it highly actionable.

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

A4.4/5.0

Disambiguation4/5

Each tool targets a distinct step in the quoting workflow (eligibility, start, update, status, options, details, binding, resume, lead capture). Some overlap exists between get_bind_link and request_bind_inline, but descriptions clarify usage. Overall clear.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern using snake_case (e.g., check_eligibility, get_quote_options, request_bind_inline). No mixing of conventions.

Tool Count5/5

11 tools cover the insurance quoting lifecycle well. The number is appropriate for the domain without being sparse or overwhelming.

Completeness4/5

The tool set covers the major steps: eligibility, quoting, status, options, updates, binding, and lead capture. Missing a quote cancellation or decline tool, but core workflows are complete.

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