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DataQuoll

by io.dataquoll

Disaster declaration status for a postcode

declarations_by_postcode
Read-only

Returns the declaration STATUS for a postcode: declared, partial, activation_only, not_declared, uncertain, or unknown_postcode, with per-LGA detail and official source links. Reports declared-area status only; the billing/eligibility decision rests with the practitioner. Built for Medicare disaster telehealth checks.

Requires the "declarations" entitlement on the API key. Accounts without it get a clear error rather than an empty result, so an empty response never means "not entitled".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_atNoPoint-in-time check (YYYY-MM-DD). Defaults to today.
suburbNoOptional suburb/locality name (ABS SAL) to narrow the result to the local government areas that suburb falls in, within this postcode only. Matching is exact after normalisation; there is no fuzzy matching. An unmatched suburb never fails the lookup: the full-postcode result is returned with narrowing.applied=false, a reason, and the postcode's known suburbs. Narrowing can legitimately change matchStatus (for example partial to declared); a suburb that itself spans two LGAs stays partial, and a bare name that denotes suburbs on both sides of a border (Mingoola in 4380) matches all of them.
postcodeYes
include_expiredNoInclude expired records in the per-LGA lists. Never changes matchStatus.
instrument_typeNoTrim the returned record lists to one instrument type. Never changes matchStatus.

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses the entitlement requirement, the error-versus-empty behavior for unauthorized accounts, and that the result is declared-area status only, which prevents an agent from over-interpreting a 'not_declared' result. It also flags that suburb narrowing can legitimately change matchStatus.

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 front-loaded with the core result and status list, then layers usage, entitlement, and parameter nuance in readable paragraphs. Every sentence adds decision-relevant information; there is no filler or repetition.

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

Completeness5/5

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

With no output schema, the description still tells the agent what the result contains (status, per-LGA detail, source links) and how each parameter behaves, including the important edge case of 'Mingoola in 4380'. It is complete enough for an agent to call this tool without guessing.

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

Parameters5/5

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

Despite 80% schema coverage, the description adds substantial semantics: exact matching after normalisation with no fuzzy matching, unmatched-suburb fallback behavior, narrowing can change matchStatus, and include_expired/instrument_type never change matchStatus. This is material guidance 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 a specific verb and resource: 'Returns the declaration STATUS for a postcode', enumerates the possible statuses, and adds per-LGA detail plus source links. This clearly distinguishes it from point- and AGRN-based sibling tools such as declarations_by_point and declaration_by_agrn.

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?

It gives a clear intended context ('Built for Medicare disaster telehealth checks') and explicit exclusions ('Reports declared-area status only... billing/eligibility decision rests with the practitioner'). It does not explicitly name sibling tools as alternatives or state when to prefer declarations_by_point, so it stops just short of a 5.

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

A3.9/5.0
Disambiguation4/5

Most tools target a distinct resource and action, and the descriptions are detailed enough to separate declaration lookups, hazard history, incidents, events, and gauges. A couple of pairs could still be confused at first glance, such as declarations_by_point vs hazard_history_by_point or list_events vs list_incidents, but the descriptions resolve the boundaries.

Naming Consistency4/5

The naming largely follows a clear get_/list_ convention for single resources versus collections, with lookup-style names like declarations_by_point and declarations_by_postcode. Minor deviations such as incident_snapshot, nearby_incidents, and the singular declaration_by_agrn prevent a perfect score.

Tool Count4/5

19 tools is on the heavier side, but the count is justified by the broad domain covering current incidents, historical incidents, clustered events, declarations, river gauges, hazard history, schema discovery, attribution, and feed health. Each tool appears to earn its place, though the set is larger than the ideal 3-15 range for a tightly scoped server.

Completeness5/5

The surface is remarkably complete for a read-only emergency/disaster data API: current and historical incidents, event clustering, incident snapshots, nearby queries, declaration lookups by multiple keys, gauge readings and summaries, hazard history, schema enums, attribution, and source feed status are all covered. There are no obvious dead ends or missing core operations for the stated domain.

Resources