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saketh12e

GrabInsurance MCP

by saketh12e

get_insurance_quote

Calculate a premium quote for an insurance product using product ID, deal value, and risk tier. Returns premium, coverage, validity, and policy type.

Instructions

Calculate premium quote for an insurance product.

Returns a premium quote based on the product's base rate, deal value, and user's risk tier. Premium is floored at Rs 19 and capped at Rs 499.

Args: product_id: Insurance product ID from catalog (e.g., TRVL_CANCEL, ELEC_SCREEN) deal_value: Deal amount in INR risk_tier: User risk tier (low, medium, high)

Returns: PremiumQuote with premium_inr, coverage_inr, validity_days, policy_type

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
risk_tierNomedium
deal_valueYes
product_idYes
Behavior5/5

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

Despite no annotations, the description fully discloses key behaviors: premium floor (Rs 19) and cap (Rs 499), calculation basis (base rate, deal value, risk tier), and default risk tier. No side effects are mentioned, but the tool appears read-only.

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 concise and well-structured with a clear first sentence, followed by bullet-like details. The Args/Returns format adds clarity, though it could be slightly more streamlined.

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 3 parameters, no output schema, and no annotations, the description is remarkably complete. It specifies return fields (premium_inr, coverage_inr, validity_days, policy_type), calculation logic, and constraints. No gaps for an agent to interpret.

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

Parameters5/5

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

Schema description coverage is 0%, but the description adds full semantic meaning: product_id examples, deal_value as INR amount, risk_tier with possible values. This compensates completely for the missing schema descriptions.

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 'Calculate premium quote for an insurance product' with specific verb and resource. It provides examples of product IDs, making the purpose unambiguous. The sibling tool classify_deal_intent is distinctly different, so no confusion.

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 description explains what the tool does and its inputs, but lacks explicit guidance on when to use it vs. alternatives or when not to use it. There is no mention of prerequisites or context, though the purpose is clear enough for an agent to infer.

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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