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apc-agent

apc-agent

by J8k3
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

generate_card_validation_data

Generate CVV, CVV2, iCVV, or ARQC validation values for card testing. Use CVK or EMV keys to produce accurate test vectors for card personalization and CVV-based validation.

Instructions

Call this when implementing card personalization, CVV/CVV2/iCVV generation, or validating card data for testing. Also use to generate test vectors for CVK-based validation before writing production code.

Generates CVV, CVV2, iCVV, or dynamic card verification values. Supported key types: C0 (CVK), E4/E6 (EMV).

generation_attributes examples: CVV: {"CardVerificationValue1": {"CardExpiryDate": "0128", "ServiceCode": "101"}} CVV2: {"CardVerificationValue2": {"CardExpiryDate": "0128"}} iCVV: {"CardVerificationValue1": {"CardExpiryDate": "0128", "ServiceCode": "999"}} ARQC: {"DynamicCardVerificationCode": {"ApplicationTransactionCounter": "0001", "PanSequenceNumber": "01", "TrackData": "..."}}

Args: key_identifier: ARN or alias of CVK (C0) or EMV key primary_account_number: 12-19 digit PAN generation_attributes: Algorithm and card data parameters validation_data_length: Optional output length override

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": {
      "key_identifier": "<key_identifier>",
      "primary_account_number": "<primary_account_number>",
      "generation_attributes": "<generation_attributes>"
    },
    "name": "generate_card_validation_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
key_identifierYes
generation_attributesYes
primary_account_numberYes
validation_data_lengthNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains supported algorithms, key types, and optional output-length override, but it does not state whether the operation is non-mutating, whether it consumes key usage, or what permissions are required. This is adequate but leaves side-effect profile implicit.

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-organized and front-loaded with usage scenarios, then supported types, examples, and parameter definitions. The examples are necessary because generation_attributes is an untyped nested object. It is slightly longer than strictly necessary, with some redundancy between the opening scenarios and the generation sentence, but every section earns its place.

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?

For a complex tool with nested parameters, no annotations, and no output schema, the description covers input semantics thoroughly and gives realistic invocation examples. It does not explicitly describe the returned value shape or the precise effect of validation_data_length, which are minor gaps given the clear generate-oriented purpose.

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?

The input schema has 0% description coverage, so the description must compensate, and it does excellently. It defines key_identifier as ARN or alias, constrains PAN length to 12-19 digits, explains generation_attributes, and provides concrete JSON examples for CVV, CVV2, iCVV, and ARQC. This adds meaning far beyond the bare schema.

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 names the operation as generating CVV, CVV2, iCVV, or dynamic card verification values, with specific supported key types. This distinguishes it from verification and MAC tools in the sibling list and leaves no doubt about the tool's primary purpose.

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 gives explicit trigger scenarios: implementing card personalization, generating CVV variants, producing test vectors before production code, and validating card data for testing. It does not explicitly name alternatives like verify_card_validation_data or state when not to use this tool, so it stops short of a 5.

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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