Validate an EU VAT number against the official European Commission VIES service and return the registered company name and address. All 27 member states.
| Name | Required | Description | Default |
|---|---|---|---|
| vat | Yes | VAT number without country prefix | |
| country | Yes | EU member state code, e.g. LT, DE, FR |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does disclose the external service, the return payload, and the supported member states. However, it does not mention behavior for invalid VAT numbers, VIES unavailability, latency, rate limits, or whether the tool returns a structured result versus throwing an error.
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?
Two concise, information-dense sentences. The primary action and resource are front-loaded, followed by the return value and scope. There is no fluff, and every sentence contributes useful information.
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 two-parameter tool with full schema coverage, the description is mostly complete: it states purpose, external source, return values, and geographic scope. The main gap is the lack of a precise return shape or error behavior, made slightly more relevant by the absence of an output schema, but this is not a severe omission for this tool's simplicity.
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?
Schema description coverage is 100% and both parameters already have clear explanations: 'VAT number without country prefix' and 'EU member state code, e.g. LT, DE, FR'. The description adds no further parameter-level meaning, so the baseline score of 3 is appropriate.
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 states a specific verb ('Validate'), a precise resource ('EU VAT number'), and the authoritative source ('official European Commission VIES service'). It also names the expected output, the registered company name and address, and clarifies scope ('All 27 member states'). This clearly distinguishes it from siblings like iban_validate or kyb_verify_business.
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 context is implied: use this when you need EU VAT validation against VIES. However, there is no explicit guidance about when not to use it or which sibling tool might be a better alternative for related but different checks, such as IBAN validation or broader business verification.
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 by resource or function, but fx_rates vs us_treasury_fx and entity_resolve vs lei_lookup could both be plausible choices for the same request. Descriptions generally make the boundaries clear, so the overlap is minor rather than systematic.
All names use lowercase_snake_case, but there is no single naming pattern: tools like btc_network, crypto_price, and weather_forecast are noun phrases, while eu_vat_validate, lei_lookup, and sanctions_screen follow an object+operation style. This mixed convention is readable but noticeably inconsistent.
14 tools is within a reasonable range for a multi-provider data server, and most tools cover a distinct data domain. However, kyb_verify_business heavily overlaps the individual VAT, LEI, sanctions, IBAN, and entity tools, and having two FX rate tools makes the set feel slightly broader than necessary.
The main KYB/entity workflow is well covered, but the set has notable gaps: sanctions screening covers only OFAC and UK OFSi, IBAN validation is structural only, and there are no UBO or adverse-media checks. For the broader data-lookup domain, the coverage is decent but not exhaustive.