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Creditcard

by io.github.pipeworx-io
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Check Luhn

check_luhn
Read-onlyIdempotent

Run a raw Luhn (mod-10) check-digit validation on any numeric string (cards, IMEI, national IDs, etc.). Returns whether the check digit is valid and what the correct check digit would be.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "number": "4111111111111111"
    },
    "name": "check_luhn"
  }
}

Input Schema

NameRequiredDescriptionDefault
numberYesA numeric string to Luhn-check.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "number": "4111111111111111"
+  },
+  {
+    "number": "356938035643809"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral context by stating it returns 'whether the check digit is valid and what the correct check digit would be', which provides value beyond the annotations.

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 consists of two concise sentences with no redundant information. It is front-loaded with the tool's action and context, and every part adds value.

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?

For a simple, single-parameter tool with no output schema, the description is complete: it explains the purpose, algorithm, example inputs, and what is returned. It covers all necessary information for an agent to use it correctly.

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?

Schema coverage is 100%, and the schema description already explains the parameter ('A numeric string to Luhn-check.'). The description reiterates 'numeric string' and adds examples but does not provide additional semantic meaning beyond the 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 states the verb 'Run a raw Luhn (mod-10) check-digit validation', specifically identifies the resource as 'any numeric string', and lists example use cases (cards, IMEI, IDs). It distinguishes from the sibling 'validate_card' by emphasizing this is a raw Luhn check, not full validation.

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 this tool is for quick Luhn validation without additional checks, differentiating it from more comprehensive tools like 'validate_card'. However, it does not explicitly state when to use or not use this tool, nor does it mention alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Creditcard toolsInspect
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