VIES Smoother
Server Details
SLA'd EU VAT validation on VIES. Honest 3-state result, never guesses. 100 free lookups.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.2/5 across 2 of 2 tools scored.
The two tools are clearly distinguished: one for single VAT validation and one for batch validation. There is no overlap in functionality because they serve different input cardinalities.
Both tools follow a consistent 'validate_vat' base name, with '_batch' appended for the multi-item variant. The naming pattern is fully predictable and adheres to a verb_noun style.
With only two tools, the surface is minimal but appropriate for a focused VAT validation service. A third tool for checking credit or cache status might enhance completeness, but the current count is not unreasonable.
The server covers the core operation of VAT validation for both single and batch requests. Missing auxiliary operations like credit balance or cache control, but these are not essential for the primary purpose.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| vat_number | Yes | ||
| country_code | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the cost (1 credit) and a special case where it might be free, adding valuable behavioral context about caching and upstream availability.
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 relatively concise: a purpose sentence, a cost sentence, and an Args section. It focuses on essential information without redundancy, though the cost detail could be integrated more smoothly.
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?
Given no output schema and no annotations, the description covers purpose, parameters, and cost. However, it lacks explanation of return values, error handling, or validation results, leaving gaps for an AI agent to understand the tool's full behavior.
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 input schema has 0% description coverage, so the description must compensate. It explicitly defines both parameters: country_code as a 2-letter EU code (with examples) and vat_number as the number without prefix, providing clear format and usage beyond the schema.
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 'Validate a single EU VAT number,' specifying the verb and resource. It distinguishes from the sibling tool 'validate_vat_batch' by emphasizing 'single,' leaving no ambiguity.
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 implies singular use by saying 'single EU VAT number,' contrasting with the sibling 'validate_vat_batch.' However, it does not provide explicit guidance on when to use this tool versus the batch alternative, nor any prerequisites or limitations.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the credit model and the limit of 50 items, but omits potential errors, rate limits, or response format. Adequate but not comprehensive.
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?
Extremely concise: two sentences front-load purpose and cost, then a clear argument definition. Every sentence adds value, no fluff.
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?
Covers purpose, limit, cost, and parameter structure well. Lacks return value description or error handling, but given the tool's simplicity and no output schema, it is mostly complete.
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 input schema only defines items as an array of objects with no structure description. The tool's description compensates fully by specifying the required fields (country_code, vat_number) and their types, and the maximum of 50 items. This adds critical meaning beyond the schema.
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 it validates up to 50 EU VAT numbers in one call, distinguishing it from the sibling tool validate_vat which likely handles single numbers. The verb 'validate' and resource 'EU VAT numbers' are specific.
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?
Provides credit cost information ('1 credit per item...') which guides usage decisions. Implicitly suggests batch use for multiple numbers via sibling tool name, but lacks explicit 'when to use' or 'when not to use' guidance.
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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