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IBAN validate & decode

iban_check

Validate an IBAN (length + mod-97 check digits). view='decode' resolves it to country, bank and account parts; view='format' returns electronic and paper (grouped) forms. Paid: call without x_payment to receive this call's exact terms (amount, asset, network), sign them, then call again with x_payment. The free pricing tool lists every price at once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ibanYesThe IBAN, spaces optional.
viewNoWhich operation to run.validate
x_paymentNoOptional signed x402 payment payload (base64, what the X-PAYMENT header carries). Omit to receive the exact payment terms; sign them (e.g. @x402/fetch) and call again with this argument to settle and get the data.

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and discloses the paid nature and the two-step payment process, which is critical. It also explains what each view returns (country/bank/account parts vs electronic/paper forms). It omits output format and invalid-IBAN behavior, but the core behaviors are well covered.

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 sentences, front-loaded with the main purpose. Each sentence contributes: the validation and views, the payment workflow, and the pointer to the pricing tool. No wasted words.

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?

The paid flow is complex and thoroughly explained, including the two-step payment and the pricing alternative. The views are described with their outputs. However, there is no indication of what the 'validate' response looks like (e.g., boolean, error details), and no output schema exists to fill that gap. This prevents a perfect score.

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?

Even though schema coverage is 100%, the description adds substantial value beyond the schema. It explains the functional difference between 'decode' and 'format', and details the x_payment lifecycle (omit to get terms, sign, resubmit). This goes well beyond the baseline for full schema coverage.

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 IBANs using length and mod-97 check digits, and explains the decode and format views. This is a specific verb+resource description that distinguishes it from sibling tools like sanctions screening or phone checks.

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?

It provides explicit payment workflow guidance: call without x_payment to get terms, sign, then call again. It also directs users to the free 'pricing' tool for a complete price list. It does not explicitly compare with sibling tools, but the domain is distinct enough that this is not a significant gap.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct resource type (crypto address, domain, email, IBAN, phone, URL, vessel, entity name) or a distinct purpose (pricing, data discovery, data request). Overlapping sanctions tools are clearly differentiated by target: address_screen for addresses, sanctions_screen for names, vessel_screen for vessels, and sanctions_entity for detailed records after screening.

Naming Consistency3/5

Most data-check tools follow a consistent object_verb pattern (e.g., address_screen, email_check, phone_check). However, find_data and request_data invert the order, domain_intel uses a noun instead of a verb, and pricing stands alone as a gerund, creating mixed conventions.

Tool Count5/5

12 tools is well within the ideal range for a data-screening server. Each tool covers a distinct verification task, and the additional meta tools (pricing, find_data, request_data) are useful entry points without bloating the core purpose.

Completeness4/5

The server covers a comprehensive set of screening and validation tasks across sanctions, domain, email, phone, IBAN, and URL. It includes a discovery tool (find_data) and a suggestion tool (request_data) to fill gaps, though an IP checker or company registry lookup could be considered minor omissions.

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