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koraykoylu

ibanchecker-mcp

Validate Multiple IBANs

validate_bulk_ibans
Read-onlyIdempotent

Validate a batch of up to 100 IBANs in a single call with ISO 13616 checks. Returns per-IBAN results and summary counts of valid and invalid entries.

Instructions

Validate a batch of up to 100 IBANs in one call, applying the same ISO 13616 checks as validate_iban (country, length, BBAN structure, MOD-97).

Returns a JSON array of per-IBAN results in the same order as the input, each with valid, countryCode, an optional reason for failures, and bank details when the code is recognized, plus a summary count of valid vs. invalid entries.

Use this instead of calling validate_iban in a loop when checking a list (e.g. a payment file or a column of supplier accounts). Split inputs larger than 100 into multiple calls. Account numbers are validated in memory and never stored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ibansYesArray of 1 to 100 IBAN strings to validate. Case-insensitive; spaces are tolerated. Order is preserved in the response.
Behavior5/5

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

Annotations indicate readOnly, not destructive, idempotent, and open world. The description adds behavioral details beyond annotations: validation happens in memory, no storage, preserves input order, returns a summary count, and produces per-IBAN results with optional reasons and bank details.

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?

Description is concise and well-structured: first sentence states core action and limit, second details return format, third provides usage scenario and additional notes. No redundant sentences.

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 no output schema, the description fully explains the return structure (array with valid, countryCode, reason, bank details, summary count). Combined with thorough annotations and parameter semantics, the description is complete for an agent to use correctly.

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 single parameter 'ibans' has 100% schema description coverage. The description adds semantic details: case-insensitivity, tolerance of spaces, and order preservation in the response, which are not in 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 identifies the tool as validating a batch of up to 100 IBANs using ISO 13616 checks, distinguishing it from the single-IBAN sibling `validate_iban` by explicitly mentioning batch processing and listing the same checks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: 'Use this instead of calling validate_iban in a loop' when checking a list, and advises splitting inputs larger than 100. Also notes that account numbers are never stored, indicating privacy considerations.

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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