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insurance-mcp-poc

insurance-mcp-poc

by saivarun1410
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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Look up product rules

lookup_product_rules

Retrieve a product's issue limits and underwriting rules. When applicant age, face amount, or state is given, evaluate eligibility and flag which rules would trigger.

Instructions

Return a product’s issue limits and underwriting rules. When applicant_age, face_amount or state are supplied, also evaluates hard eligibility and flags which rules would trigger.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "product_code": "<product_code>"
    },
    "name": "lookup_product_rules"
  }
}

Input Schema

NameRequiredDescriptionDefault
stateNoTwo-letter state code
face_amountNo
product_codeYesProduct code, e.g. UL-200
applicant_ageNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that supplying applicant_age, face_amount, or state triggers hard eligibility evaluation and rule flagging. This is valuable behavioral context beyond the schema. However, it does not explicitly state the read-only nature, though 'lookup' implies it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is two sentences, front-loaded with the primary function, and contains no filler. Every word contributes to understanding the tool's behavior.

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?

The description covers the core purpose and conditional behavior effectively. Given no output schema, it could have described the return format more explicitly, but for a lookup tool with moderate complexity, it is adequate. The absence of side effects or prerequisites is not a significant gap.

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?

Schema coverage is 50%, with descriptions for state and product_code. The description adds meaning by explaining that three optional parameters trigger eligibility evaluation, which is not evident from the schema alone. It stops short of detailing units or semantic constraints for applicant_age and face_amount, but the param names are self-explanatory.

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 returns a product's issue limits and underwriting rules, which is a specific verb+resource. It also distinguishes itself from siblings by focusing on product rules rather than policy documents or application status.

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 implies when to use the tool: when product rules or eligibility information is needed. It does not explicitly mention alternatives, but sibling names are sufficiently distinct to avoid confusion. Clear context without explicit exclusions.

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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