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FabTally Valid

by io.github.zvmzaretsky

Validate an IBAN (mod-97) + extract bank code (paid $0.001)

validate_iban

PAID $0.001 (x402, USDC on Base). Validate an IBAN: give {iban} and get ISO 13616 length + mod-97 checksum validation, the country, the human-formatted grouping, and the bank identifier (e.g. German Bankleitzahl, UK bank code + sort code) extracted from the BBAN where derivable. Pure-offline. Without payment returns the x402 challenge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ibanYesThe IBAN (spaces/dashes tolerated), e.g. DE89 3704 0044 0532 0130 00.
x_paymentNox402 payment payload (base64) for this PAID validation. If supplied it is forwarded as the X-PAYMENT header to settle the call and return the real result instead of a 402 challenge. Omit to get the price challenge first.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
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 does well: it discloses the paid nature ('PAID $0.001 (x402, USDC on Base)'), the offline execution ('Pure-offline'), and the challenge/response behavior when payment is missing. It also previews the returned fields, providing useful behavioral context beyond the schema.

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 compact and information-dense. It front-loads the payment requirement, then gives a clear list of outputs, and ends with the no-payment behavior. Every sentence serves a purpose, and the title reinforces the key validation and extraction points.

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?

For a validation tool with no output schema, the description adequately explains the return data (length, checksum, country, grouping, bank identifier) and the payment flow. It lacks explicit error-handling/edge-case details, but the core use case is fully covered given the tool's modest complexity.

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% (both 'iban' and 'x_payment' have detailed descriptions), so the baseline is 3. The description adds minimal extra semantics beyond the schema: it mentions '{iban}' and 'x402 challenge' but does not materially enhance parameter understanding.

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 ('Validate an IBAN') and specifies the resource and expected outputs: ISO 13616 length, mod-97 checksum validation, country, human-formatted grouping, and bank identifier extraction. This distinguishes it from sibling tools like validate_email or validate_vat because it is IBAN-specific and mentions concrete derived data (e.g., Bankleitzahl, UK sort code).

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 when to use it: whenever an IBAN needs validation/parsing. It does not explicitly list alternatives or exclusions, but sibling tools are for different entities (VIN, email, VAT, geo), so the use case is clear. The payment caveat ('Without payment returns the x402 challenge') is a practical guideline for invoking correctly.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct input type (VIN, place name, email, IBAN, VAT) with no overlap in purpose. An agent can easily distinguish decode_vin from validate_email based on the resource being processed.

Naming Consistency3/5

Three tools use the validate_* prefix (validate_email, validate_iban, validate_vat), but decode_vin and geo_lookup break the pattern with different verb/noun structures. The names are readable but not uniformly consistent.

Tool Count5/5

Five tools form a well-scoped validation utility set, covering common data types (VIN, email, IBAN, VAT, location) without redundancy or bloat. This is an ideal size for a focused server.

Completeness4/5

The set covers a solid range of common validation/lookup tasks, but typical additions like phone or credit-card validation are absent. These are minor gaps that do not hinder the core purpose.

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