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InsurTech MCP Server

InsurTech MCP Server

by David-IABB
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

assess_insurance_risk

Evaluate risk factors to calculate personalized insurance premium estimates for auto, home, or health policies across multiple jurisdictions.

Instructions

Assess insurance risks and provide premium estimates

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": {
      "risk_data": "<risk_data>"
    },
    "name": "assess_insurance_risk"
  }
}

Input Schema

NameRequiredDescriptionDefault
risk_dataYesDictionary with risk factors (age, value, location, etc.)
jurisdictionNoLegal jurisdiction (belgium, france, germany, etc.)belgium
insurance_typeNoType of insurance (auto, home, health, etc.)auto

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It only states the general purpose ('assess risks, provide premium estimates') without describing side effects, required permissions, whether the operation is read-only, or any output details beyond what the schema might imply.

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 a single concise sentence that communicates the tool's core function without extraneous words. It is front-loaded and easy to parse.

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

Completeness3/5

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

While the schema and output schema provide technical details, the description lacks contextual completeness. It doesn't mention when to use this tool in an insurance workflow, what data is expected beyond the schema, or how it compares to sibling tools. For a tool with moderate complexity, this is adequate but has clear gaps.

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?

The input schema already provides descriptive text for all three parameters (risk_data, jurisdiction, insurance_type), covering 100% of parameters. The description adds no additional parameter-level detail, so the baseline of 3 applies because the schema does the heavy lifting.

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 function: 'Assess insurance risks and provide premium estimates.' This distinguishes it from sibling tools like analyze_policy_clauses or generate_claim_report, which focus on different aspects of insurance processing.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or exclusions. For example, it doesn't explain that this is for risk/price evaluation rather than clause analysis or claim generation.

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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