Dutch identifier validation — $0.001 per call (x402, USDC on Base). Offline validation of a Dutch identifier: BSN (elfproef), IBAN (structure + mod-97 + bank), or kenteken (sidecode format).
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Identifier type: bsn, iban or kenteken. | |
| value | Yes | The value to validate. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals that validation is offline (performing local checks without external calls), specifies the validation algorithms for each type, and transparently discloses the cost and payment method. This exceeds basic expectations, though it does not state the return format or behavior on invalid input.
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 a single, well-structured sentence that packs in the tool's scope, examples, validation specifics, and pricing without any fluff. Parentheticals efficiently list the identifier types and their validation methods, making it easy to scan.
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 the tool's low complexity (two simple parameters) and lack of output schema, the description provides sufficient context: what it does, for which identifiers, and how much it costs. It could mention what the validation result looks like, but the essential information for selecting and invoking the tool is present.
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 coverage is 100%, so parameters are already described. The description adds valuable meaning by explaining what each type enum actually validates (e.g., elfproef for BSN, mod-97 for IBAN), going beyond the schema to clarify the purpose and behavior of each parameter value.
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 uses a specific verb 'validation' with a clear resource 'Dutch identifier' and explicitly enumerates the three supported types (BSN, IBAN, kenteken). This distinguishes it from sibling tools like nl_address or nl_vehicle, making its 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 clearly implies when to use the tool: for validating Dutch identifiers of the listed types. It does not explicitly exclude other types or name alternatives, but the context of sibling tools and the clear scope make the appropriate usage evident.
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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Each tool targets a distinct data source or function: address lookup, building info, demographics, elevation, school holidays, vehicle lookup, validation, currency conversion, plus meta-tools for catalog and sample responses. There is no meaningful overlap between any pair of tools.
The Dutch-specific tools all follow a consistent 'nl_<subject>' pattern (nl_address, nl_building, etc.), and all names use snake_case. However, three tools (catalog, global_fx, sample) break the prefix pattern, creating a minor inconsistency but remaining clear and readable.
With 11 tools, the server is well-scoped for a Dutch open data API. Each tool covers a distinct data domain, and the count is within the ideal range without feeling excessive or sparse.
The tool surface covers a broad range of Dutch open data lookups, including addresses, buildings, demographics, elevation, holidays, and vehicles, along with validation and a sample mechanism. Minor gaps exist (e.g., no weather or property value data), but the core domain is well represented.