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Data To Agents

ar-timezone

Argentina time zone (America/Argentina/Buenos_Aires, UTC-3). No DST since 2009.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full transparency burden. It adds useful behavioral context by stating 'No DST since 2009,' implying the offset is fixed, but it does not disclose what the tool actually returns (e.g., current local time, offset, or a timezone string) or how that output is formatted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences with no filler; the essential identifying information is front-loaded and the DST caveat adds value. Every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only lookup the description is sufficient for selection and invocation, but because there is no output schema the description should have clarified what the tool returns. The core timezone facts are present, yet the response semantics remain ambiguous.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema is empty with 0 parameters, so there is nothing for the description to explain. The rule for zero-parameter tools gives a baseline of 4, and the description appropriately adds no parameter noise.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as providing Argentina's timezone, including the IANA zone and UTC offset, which separates it from ar-cpi, ar-weather, and the other country-specific timezone tools. However, it is stated as a noun phrase rather than a verbed action like 'Returns' or 'Provides', so it stops short of the strongest framing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: an agent can infer it should call this tool when Argentina timezone information is needed, especially alongside sibling timezone tools for other countries. There is no explicit when-to-use/when-not-to-use guidance or mention of alternatives, and the DST note only loosely signals applicability for post-2009 dates.

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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TDQS

A3.6/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

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