A
license-
qualityC
maintenanceSLA'd EU VAT number validation for AI agents via VIES, with caching and circuit-breaking to handle flaky upstream, returning valid/invalid/unavailable responses.
MIT
VIES Smoother
Server Details
SLA'd EU VAT validation on VIES. Honest 3-state result, never guesses. 100 free lookups.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
A4.1/5.0
Tool DescriptionsA
Average 4.1/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools are clearly distinct: one validates a single VAT number, the other validates up to 50 in batch. There is no overlap in purpose.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern: 'validate_vat' and 'validate_vat_batch'. The naming is predictable and clear.
Tool Count4/5
With only two tools, the server is minimal but appropriate for the focused domain of VAT validation. It covers the essential operations without bloat.
Completeness4/5
The server provides single and batch validation, covering the primary use cases. However, it lacks tools for checking service status or managing credits, which are minor gaps.
Available Tools
2 toolsvalidate_vatAInspect
Validate a single EU VAT number. Costs 1 credit unless upstream is unavailable and there's no cache (then it's free and honest about it).
Args:
country_code: 2-letter EU member state code (e.g. DE, FR, EL, XI).
vat_number: The VAT number without the country prefix.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| vat_number | Yes | ||
| country_code | Yes |
Tool Definition Quality
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses credit cost and cache fallback behavior, providing useful behavioral context beyond simple validation.
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?
Two sentences plus args list, front-loaded with primary purpose. No unnecessary words.
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?
For a simple 2-param, no-output-schema tool, description covers purpose, cost, cache behavior, and parameter details completely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description fully documents parameters: country_code as 2-letter EU code with examples, vat_number without prefix. Adds meaning beyond bare schema.
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?
Description clearly states 'Validate a single EU VAT number' with specific verb and resource. Distinguishes from sibling 'validate_vat_batch' by specifying 'single'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Mentions cost and cache behavior, implying use for single validation, but does not explicitly state when to use this over validate_vat_batch or exclude certain scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_vat_batchAInspect
Validate up to 50 EU VAT numbers in one call. 1 credit per item that gets a real answer (unavailable_upstream items are free).
Args:
items: list of {"country_code": str, "vat_number": str}, max 50.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes |
Tool Definition Quality
A3.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral aspects. It includes credit cost and batch size, but omits critical details such as idempotency, authentication requirements, error handling on exceeding 50 items, and read-only nature. This is insufficient for a mutation-like validation tool.
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 extremely concise: two sentences plus an Args block. The purpose is front-loaded, and every sentence adds value. No unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description explains the parameter structure and credit model but lacks information about return format, error handling, authentication, and any other usage constraints. It meets minimal viability but has gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage and defines 'items' loosely with additionalProperties true. The description compensates by specifying the expected structure (list of objects with country_code and vat_number), providing meaning beyond the generic schema.
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' and resource 'EU VAT numbers', and specifies the batch limit of 50, which distinguishes it from the sibling tool 'validate_vat' for single VAT numbers.
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 explicitly mentions the batch size limit and credit cost behavior, implying when to use it (for multiple VAT numbers) and providing cost guidance. However, it lacks explicit guidance on when not to use it or detailed alternatives beyond the sibling name.
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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