Validate an EU VAT ID against the live VIES registry (format check first — an invalid format still costs the full price, it never reaches VIES but you got an answer). 5 credits, requires an API key.
| Name | Required | Description | Default |
|---|---|---|---|
| vatId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure. It reveals important traits: 5 credits, API key requirement, and that invalid formats still incur cost without reaching VIES. This goes beyond minimal expectations, though it does not detail the return format or possible errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, opening with the core purpose and then adding caveats and requirements. Every sentence provides value, with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter validation tool, the description covers purpose, cost, prerequisites, and invalid-format behavior. It lacks an explicit mention of the return value structure, but the 'got an answer' phrase hints at it, and the lack of output schema makes this acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description identifies 'vatId' as an EU VAT ID, providing some meaning beyond the raw schema. However, it does not specify the expected format (e.g., country code + digits) or provide examples, leaving a gap since schema description coverage is 0%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates EU VAT IDs against the live VIES registry, using a specific verb and resource. It distinguishes itself from the sibling tool 'validate_vatid_format' by emphasizing the live registry check, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool: for live VIES validation. It also implies that format-only checks may not need this tool, but it does not explicitly name alternatives or exclusions. The mention of cost and API key adds practical usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct, but validate_vatid and validate_vatid_format could be confused at a glance, and verify_company overlaps with validate_iban and validate_vatid in scope. However, detailed descriptions and explicit mentions of differences help an agent choose correctly.
All tools follow a consistent verb_noun pattern with lowercase and underscores: screen_sanctions, validate_email, validate_iban, validate_vatid, validate_vatid_format, verify_company. The verb varies but the structure is uniform.
Six tools is a well-scoped number for a validation API covering sanctions, email, IBAN, VAT, and a combined company verification. Each tool serves a clear purpose, and the count feels neither sparse nor bloated.
The set covers core validation areas (sanctions, email, IBAN, VAT, company verification) but intentionally excludes PEP screening and commercial-register checks, as noted in the descriptions. These minor gaps do not prevent core workflows, though they limit full KYC coverage.