VIES Smoother

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

With no annotations provided, the description carries the full behavioral burden. It discloses the credit cost, the fallback behavior when upstream is unavailable and no cache exists, and notes that the tool is 'honest' about free results. This is rich, non-obvious behavioral context that an agent needs for selection and expectation-setting.

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 compact and front-loaded. The primary purpose and the most important behavioral detail (cost) appear in the first sentence, followed by clear parameter documentation. Every sentence earns its place with no filler.

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 validation tool, the description covers purpose, cost, fallback behavior, and both parameters. However, there is no output schema and the description does not state the return format or what a valid versus invalid result looks like. An agent might still need to infer the response shape, so a small completeness gap remains.

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 provides zero description coverage, so the description is the only source of parameter meaning. It clearly defines country_code as a 2-letter EU member state code with examples, and vat_number as the VAT number without the country prefix. This fully compensates for the schema's lack of detail.

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 opens with an explicit action—'Validate'—a specific resource, 'a single EU VAT number', and explicitly contrasts with the batch sibling by stating 'single'. This makes the tool's scope immediately clear and differentiates it from validate_vat_batch without 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 the appropriate use case: validating one VAT number at a time. The sibling name validate_vat_batch further reinforces the batch alternative, but the description never explicitly states 'use this for a single number, use validate_vat_batch for multiple'. This is a slight gap, though the intent is strongly implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the transparency burden. It discloses non-obvious behaviors: the 50-item maximum, the per-item credit cost, and the fact that 'unavailable_upstream items are free.' This adds real behavioral detail beyond a generic 'validate' statement, though it does not cover error handling or response shape.

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 compact and well-structured: a clear first sentence states the core purpose, the second covers cost behavior, and the Args block precisely defines the input. Every sentence adds value with no redundancy.

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 single-parameter tool, the description is nearly complete: purpose, batch limit, item structure, and cost are all covered. The main gap is the absence of any description of the return format or possible response values, which would help because there is no output schema, but invocation itself is fully specified.

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 schema provides almost no parameter detail (items array with untyped objects, 0% schema coverage). The description fully compensates by specifying the exact item shape: 'items: list of {"country_code": str, "vat_number": str}, max 50.' This gives an agent everything needed to construct valid arguments.

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 opens with a specific verb and resource: 'Validate up to 50 EU VAT numbers in one call.' This clearly identifies the batch nature of the tool and distinguishes it from the sibling validate_vat by emphasizing the multi-item, single-call scope.

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 batch usage with 'up to 50... in one call' and contextualizes credit costs per item, but it never explicitly mentions the sibling validate_vat or gives criteria for choosing between batch and single validation. The usage context is present but not fully articulated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.