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MaxMind minFraud MCP by usefulapi

minFraud Factors (risk reasons + subscores)

minfraud_factors
Read-only

The most detailed assessment: everything in Insights PLUS risk_score_reasons and per-factor subscores explaining WHY the score is what it is. Same request body as minfraud_score. API: POST /minfraud/v2.0/factors.

Input Schema

NameRequiredDescriptionDefault
emailNo
eventNo
orderNo
deviceNoInfo about the device/connection. device.ip_address is the most important input.
accountNo
billingNo
paymentNo
shippingNo
credit_cardNo
custom_inputsNoCustom inputs configured in your minFraud account.
shopping_cartNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / properties / custom_inputs / additionalProperties / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "number"
      -  },
      -  {
      -    "type": "boolean"
      -  }
      -]
    • addedInput schema / properties / custom_inputs / additionalProperties / type
      Added value: +[
      +  "string",
      +  "number",
      +  "boolean"
      +]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

readOnlyHint=true already covers the safety profile, and the description adds genuinely new behavioral context: the HTTP endpoint (POST /minfraud/v2.0/factors) and, notably, the extra response content (risk_score_reasons, per-factor subscores) that no output schema documents. It omits auth requirements, rate limits, and pricing/billing tier for these calls, which keeps it out of the top band.

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?

Three short sentences, front-loaded with the differentiating claim, then the request-body equivalence, then the raw endpoint. No filler or restatement of the title, and the final sentence earns its place as an implementation detail.

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?

With 11 nested parameters, 0 required fields, low schema coverage and no output schema, an agent needs more than this to call the tool confidently — it cannot tell which fields are actually needed for a score. The description partially compensates by describing what comes back, but the input contract remains largely undisclosed.

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

Parameters2/5

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

Schema description coverage is only 18% across 11 top-level parameters, so the description carries the burden — and it explains no parameters at all, deferring entirely with 'Same request body as minfraud_score'. That delegation only helps an agent that already knows the sibling's contract; nothing clarifies the nested device, order, or custom_inputs fields.

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?

States a specific verb-and-resource framing — 'the most detailed assessment' — and defines it as a strict superset of a named sibling ('everything in Insights PLUS risk_score_reasons and per-factor subscores'). This lets an agent rank it against minfraud_insights and minfraud_score without opening any schema.

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 tiering language ('most detailed', 'PLUS... explaining WHY the score is') and 'Same request body as minfraud_score' tell the agent when this is the right choice versus the cheaper siblings. It stops short of an explicit exclusion ('do not use when you only need the score'), so it is context rather than a full decision rule.

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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