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validate_clabe_mx

Read-onlyIdempotent

Validates Mexican CLABE bank account numbers using the official weighted modulo-10 checksum algorithm to confirm accuracy for wire transfers and payment flows.

Instructions

Validates a Mexican CLABE (Clave Bancaria Estandarizada) — the 18-digit standardized bank account number used for electronic transfers in Mexico, as defined by BANXICO (Banco de México). Applies the official weighted modulo-10 checksum algorithm (weights: 3,7,1 repeating). Returns { valid: boolean, clabe: string, bank_code: string, city_code: string } or { valid: false, reason: string }. Use when processing Mexican wire transfers, validating supplier bank accounts, or building Mexican payment flows in AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clabeYes18-digit Mexican CLABE bank account number. Example: '032180000118359719'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
validYes
clabeNo
bank_codeNo
city_codeNo
reasonNo
Behavior5/5

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

Beyond the readOnly and idempotent hints, the description discloses the weighted modulo-10 checksum algorithm with specific weights (3,7,1 repeating) and the exact return structure including valid, clabe, bank_code, city_code, or reason. No contradictions with 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?

Two sentences: first defines the tool's purpose and background, second gives algorithm and return format plus usage context. No filler, front-loaded with essential info.

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?

Given the presence of an output schema and the description covering validation logic, return structure, and use cases, the definition is complete for an AI agent to correctly select and invoke the tool.

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

Parameters4/5

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

Schema already describes the 'clabe' parameter with format and example. The description adds context about the algorithmic behavior and return values, providing more meaning beyond the schema alone. With 100% schema coverage, baseline 3, but description adds significant extra value.

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, specifies it's an 18-digit bank account number for electronic transfers, mentions BANXICO, and distinguishes from sibling tools that validate other identifiers (CBU, CC, CNPJ, etc.).

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?

Explicitly states when to use: processing Mexican wire transfers, validating supplier bank accounts, building Mexican payment flows. While it doesn't list when not to use, the positive guidance is clear and the sibling list provides alternatives.

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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