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eu-verify

by io.github.contentfactory

validate_email

One-call email verification for lead qualification and list hygiene: RFC 5322 syntax check, then a live MX lookup over DNS-over-HTTPS (Cloudflare, Google fallback). Detects non-existent domains (NXDOMAIN), null-MX domains that refuse mail (RFC 7505), and returns prioritized MX hosts. Query: ?email=someone@example.com. Verdict: deliverable | risky | undeliverable. 1h cache per address. Price: $0.005 USDC per call (x402).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYes

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses the verification steps (syntax check, live MX lookup via DNS-over-HTTPS with fallback), caching behavior (1h), pricing ($0.005 USDC), and expected verdicts. This is comprehensive and transparent.

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 (4-5 sentences) with front-loaded purpose, followed by technical details, output, caching, and pricing. Every sentence adds value, no fluff.

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 1 parameter, no output schema, and no annotations, the description provides all necessary context: input format, verification process, output classification, caching, and pricing. It 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?

With 0% schema coverage, the description compensates fully by providing an example query format ('?email=someone@example.com') and specifying the email parameter is required. It adds meaning beyond the schema by explaining the format and purpose.

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 explicitly states the tool's purpose: 'One-call email verification for lead qualification and list hygiene' and details the specific checks (RFC 5322 syntax, MX lookup, NXDOMAIN detection) and outputs (verdict: deliverable|risky|undeliverable). It clearly distinguishes this tool from sibling tools which focus on business lookups and validation.

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 provides clear context for use ('lead qualification and list hygiene') and explains the process. However, it does not explicitly mention when not to use this tool or suggest alternatives, which would be beneficial for an AI agent.

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 serves a distinct verification or lookup purpose (e.g., company lookup, sanctions screening, address geocoding, IBAN validation) with no overlap. Even tools targeting the same source (e.g., check_eori vs. validate_vat_eu) have clearly different inputs and outputs.

Naming Consistency4/5

Almost all tools follow a verb_noun pattern (verify, validate, lookup, check, find, screen) with clear nouns. The only exception is 'catalog' (a noun-only name), but it's a minor deviation that doesn't cause confusion.

Tool Count5/5

19 tools is appropriate for a data verification service covering company data, sanctions, VAT, addresses, emails, IBANs, and financials. Each tool addresses a specific need without being excessive or sparse.

Completeness5/5

The tool set covers the full lifecycle of EU company verification: existence, status, financials, ownership, VAT, sanctions, EORI, invoices, tenders, and address validation. No obvious gaps for its stated domain.

Resources