Argentina 4-digit postal code → postal region/provinces/cities via the Correo Argentino leading-digit scheme.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | Argentina 4-digit numeric postal code (e.g. 1000 = CABA) |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden of behavioral disclosure. It discloses a meaningful trait: the lookup uses the Correo Argentino leading-digit scheme, implying approximate region/province/city mapping rather than a full street-level address. However, it does not mention output format, invalid code handling, or other constraints.
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, compact sentence that front-loads the country and input format, and uses an arrow to convey the operation without wasted words. Every phrase contributes either scope or method detail.
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 one-parameter read-only lookup, the description adequately covers the input, output categories, and lookup method, which is enough for an agent to understand the tool's basic function. It omits exact response structure and error behavior, but given the low complexity, this is a minor gap.
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 only parameter, postcode, is already fully documented in the input schema with its format and an example. The description adds no new parameter-level meaning beyond restating that it is an Argentine 4-digit postal code. With 100% schema coverage, the baseline of 3 is appropriate.
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 an arrow to clearly express a mapping from an Argentine 4-digit postal code to postal region/provinces/cities, identifying the resource and expected output. The 'Correo Argentino leading-digit scheme' detail further differentiates it from other country-specific postcode lookup siblings, though the verb is implied rather than explicit.
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?
There is no explicit guidance on when to use this tool versus alternatives, and no named sibling tool. However, the description and tool name clearly scope it to Argentina, so an agent can infer it is the right choice for Argentine postal codes among the many country-specific lookup siblings. This is implied usage, not explicit direction.
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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Every tool maps to a clearly distinct dataset or lookup, with country prefixes and topic names separating overlapping domains. Even similar tools like au-abs-building-activity and au-abs-building-approvals are unambiguously differentiated by their descriptions.
The data tools follow a consistent country/topic hyphenated pattern (au-*, nz-*), making resource selection predictable. The meta tools (get_catalog, list_services, health) break this pattern with imperative/underscore names, but this is a minor and understandable deviation.
At 26 tools, the set is on the heavy side and slightly exceeds the typical comfortable range. However, each tool represents a genuinely distinct data service, and the clear grouping by country and topic keeps the surface navigable.
The server covers a broad range of common agent data needs for Australia and New Zealand: demographics, income, building, labour, weather, time, holidays, school terms, and place resolution. Minor gaps exist, such as no NZ building data or broader international coverage, but core workflows are well supported.