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FabTally Valid

by io.github.zvmzaretsky

Validate an EU VAT number via VIES (paid $0.003)

validate_vat

PAID $0.003 (x402, USDC on Base). FLAGSHIP. Validate an EU/EEA VAT number: give {country_code, vat_number} and get a per-country format + checksum pre-check AND a LIVE lookup against the official VIES service — returning valid/registered plus the registered company name & address when VIES provides them (cached ~1h to shield the flaky VIES). Without payment returns the x402 challenge; pass x_payment to settle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
vat_numberYesThe VAT number without the country prefix (prefix is tolerated and stripped).
country_codeYes2-letter EU VAT country code (e.g. IE, DE, FR; GB/XI for Northern Ireland; GR accepted as EL).

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 to rely on, the description carries the full burden. It discloses the paid nature ($0.003, x402 USDC), the live lookup against VIES, the caching behavior (~1h), and the return content (company name/address when available). This goes beyond a simple 'validates' statement, though it doesn't detail error scenarios or exact response format.

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 concise and front-loaded with the most critical information (paid, flagship). Each sentence adds value: payment mechanic, validation steps, live lookup, caching, and challenge behavior. No fluff.

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 3-parameter tool with no output schema or annotations, the description explains the core behavior: what is validated, what is returned, payment challenge handling, and caching. It lacks an explicit description of the response structure on failure, but overall covers the essential contextual information for a paid VAT validation API.

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 descriptions cover 100% of parameters, including x_payment's purpose and country_code synonyms. The tool description adds no new parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.

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's purpose: 'Validate an EU/EEA VAT number' with specific actions (format + checksum pre-check and live VIES lookup). It distinguishes itself from sibling tools by focusing on VAT validation and mentions the specific VIES service, making it unambiguous.

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 clearly conveys when to use this tool (for VAT validation) and the condition for payment (without x_payment you get a challenge, with it you get real results). It does not explicitly compare with alternative tools, but the domain is so specific that exclusions are unnecessary, giving clear context.

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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