Dutch building lookup (BAG) — $0.010 per call (x402, USDC on Base). Construction year (bouwjaar), floor area (oppervlakte), use function (gebruiksdoel) and status for a Dutch address, from the BAG via PDOK.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | Dutch postcode, e.g. 1011AC. | |
| huisnummer | Yes | House number. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses per-call cost and data source, and the word 'lookup' implies a read-only operation. However, it does not mention error behavior, response format, or any limits/edge cases, leaving gaps.
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 country, source, cost, and key output fields. Every element earns its place; there is no fluff or repetition.
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 lookup with no output schema, the description covers purpose, cost, source, and returns a list of key fields. It lacks explicit usage alternatives and response structure, but the omitted details are unlikely to cause misselection or misuse.
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 already describes both parameters (postcode and huisnummer) with examples and constraints, achieving 100% schema description coverage. The accompanying description adds no additional parameter-level semantics beyond what the schema provides, so it meets the baseline.
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 identifies a Dutch building lookup (BAG) and lists specific data fields (construction year, floor area, use function, status). It distinguishes from sibling tools like nl_address by focusing on building attributes rather than address formatting or validation.
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?
Usage context is implied by mentioning 'for a Dutch address' and the BAG/PDOK source, but there is no explicit guidance on when to choose this over nl_address or nl_validate. No exclusions or alternative comparisons are stated.
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.