Dutch municipality demographics — $0.005 per call (x402, USDC on Base). CBS key figures for a Dutch municipality (population, men/women, households, average household size, population density), by gemeente name or CBS code.
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | CBS municipality code (e.g. GM0363). | |
| gemeente | No | Municipality name (CBS spelling, e.g. 's-Gravenhage). |
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 discloses the $0.005 per-call cost and USDC on Base payment method, along with the specific metrics returned. It does not mention rate limits or error handling, but for a read-only data query this is reasonable coverage.
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 sentence that front-loads the tool's purpose, includes essential cost information, and lists key output fields without any waste. Every part earns its place, making it highly concise and well-structured.
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?
The description enumerates the output metrics, which serves as implicit output documentation since no output schema exists, and clarifies the two lookup methods. It lacks details on required parameters (neither is required) and potential error states, but is otherwise complete for a simple data-retrieval tool.
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?
Both parameters are fully described in the input schema (CBS code and municipality name), providing 100% coverage. The description reinforces how they are used ('by gemeente name or CBS code') and lists key figures, but adds no additional syntax or format details beyond what the schema already provides.
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 the resource (Dutch municipality demographics) and specific data points (population, households, density), distinguishing it from sibling tools that cover other Dutch data types. However, it lacks an explicit verb like 'get' or 'retrieve', relying on a noun phrase to convey the action.
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 usage when demographic data for a Dutch municipality is needed, by name or CBS code. It does not explicitly state when to prefer this tool over siblings or provide usage scenarios or exclusions, but the content makes the intended use clear.
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.