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au-nsw-hazard

NSW Environmental Planning Instrument hazard layers: flood planning areas and landslide risk land, queried by point or suburb. Coverage is sparse — some LGAs have no hazard polygons mapped; responses flag coverage honestly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude within NSW (-38.5..-27.5). Provide with lng instead of suburb.
lngNoLongitude within NSW (140.5..154.5). Provide with lat instead of suburb.
hazardNoWhich hazard layer: "flood", "landslide", or "all" (default)
suburbNoNSW suburb name, e.g. "Wollongong". Provide either suburb OR lat+lng.

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It adds a genuine behavioral trait with 'responses flag coverage honestly' and implies a read-only query through 'queried'. However, it does not describe the response shape, error or ambiguity handling, or concretely explain what 'flag coverage' means in the returned data.

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 sentences with no filler: the first names the resource, layers, and query modes, and the second provides the single most important caveat about coverage. Every clause carries operational value.

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?

Parameter selection is well covered by the schema, and the sparse-coverage caveat is a useful addition. However, there is no output schema, so the description should at least hint at the return format or content; it only says responses 'flag coverage honestly', leaving the response model vague.

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

Parameters3/5

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

The input schema already documents all four parameters at 100% coverage, including the either-or relationship between lat/lng and suburb and the 'flood'/'landslide'/'all' hazard values. The description only restates the point-or-suburb query mode generally, adding no semantic detail beyond the schema.

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

Purpose5/5

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

The description opens with the specific resource, 'NSW Environmental Planning Instrument hazard layers', then enumerates the exact layers ('flood planning areas and landslide risk land') and query modes ('queried by point or suburb'). This clearly distinguishes it from all sibling tools, none of which target hazard data.

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

Usage Guidelines4/5

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

The description establishes when it applies ('queried by point or suburb') and includes the sparse-coverage caveat ('some LGAs have no hazard polygons mapped'), which is important for interpreting absent results. It does not explicitly name an alternative or state when not to use it, but the sibling list contains no comparable hazard tool, so the context is 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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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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