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Latam Validate

by io.github.pipeworx-io
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Validate Clabe

validate_clabe
Read-onlyIdempotent

Validate a Mexican CLABE (Clave Bancaria Estandarizada, the 18-digit interbank account number used for SPEI transfers). Checks the control-digit checksum and decodes the bank code, plaza (branch city) code, and account number. Resolves the bank name for major Mexican banks (BBVA, Banorte, Santander, Banamex, Banco Azteca, STP, Nu, Mercado Pago, ...). Pure computation — accepts spaces or dashes in the input.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clabeYesThe 18-digit CLABE, e.g. "002010077777777771". Spaces and dashes are allowed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "clabe": "002010077777777771"
+  },
+  {
+    "clabe": "002 0100 7777 7777 71"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds value by detailing the decoding behavior (bank code, plaza, account number) and bank name resolution. 'Pure computation' aligns with annotations, and no contradictions are present.

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 three concise sentences, front-loaded with the core purpose. Every sentence adds value with no redundant or filler content.

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?

The description covers validation behavior and input flexibility but omits any mention of the output format or return value. Given the lack of an output schema, this gap reduces completeness for an AI agent that needs to interpret results.

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%, so baseline is 3. The description adds behavioral context about what the tool does with the parameter (checksum, decoding) but does not significantly enhance parameter semantics beyond the schema's format description.

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 validates a Mexican CLABE, checks the control-digit checksum, decodes bank code, plaza, account number, and resolves bank names. It is specific and distinguishes itself from sibling validation tools like validate_cpf and validate_cnpj.

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 provides clear context for when to use this tool (for Mexican CLABE validation) and notes that it is a pure computation accepting spaces/dashes. It does not explicitly mention alternatives or exclusions, but the specificity makes usage intuitive.

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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