DataQuoll

River gauge stations with live flood status

list_gauges

Read-only

Returns river gauge stations with their latest water level, the Bureau of Meteorology flood classification thresholds for each gauge, and a derived floodClass (below_minor, minor, moderate, major, or unclassified) computed from the latest reading against the published thresholds. Water-level readings are in metres relative to each gauge's local datum (each gauge carries its own unit, since some report in AHD). readingStatus flags stale data (bom-kiwis sourced stations are a 1-2 day archive, stale horizon 72 hours; bcc-telemetry are event-driven council ALERT loggers, stale horizon 48 hours; telemetry sources 24 hours). Defaults to water-level gauges; pass variable=rainfall for observed rainfall gauges, which report incremental millimetres and carry no flood classification. Status reporting, not flood prediction.

Input Schema

TableJSON Schema

Name	Required	Description
`lga`	No	ABS LGA code (the same LGA_2022 vocabulary as the declarations API)
`bbox`	No	Bounding box minLng,minLat,maxLng,maxLat (within Australia)
`limit`	No	Max results per page (1-500, default 100)
`state`	No	Lowercase state code (nsw, vic, qld, sa, wa, tas, nt, act)
`cursor`	No	Pagination cursor from meta.nextCursor
`source`	No	Polling source (hydstra-nsw, hydstra-vic, hydstra-qld, aquarius-sa, aquarius-tas, aquarius-nt, bom-kiwis, bcc-telemetry)
`variable`	No	Measured variable. Defaults to water_level; rainfall returns observed rain gauges (millimetres, incremental, no flood class).
`floodClass`	No	Filter to gauges at or above this class (>= semantics: minor matches moderate and major too). Water-level only; cannot combine with variable=rainfall.

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description adds substantial behavioral detail beyond the readOnlyHint annotation: readingStatus stale horizons by source, local datum units, AHD caveat, and the fact that rainfall gauges carry no flood classification. This gives the agent a clear model of what the tool actually reports and how to interpret its 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 dense but every sentence earns its place: main return payload, units/datum, stale-data semantics, rainfall mode, and status-not-prediction caveat. It is front-loaded with the core purpose and structured logically for agent consumption.

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?

Despite the tool's complexity (8 parameters, no output schema), the description covers primary return content, classification logic, unit semantics, stale-data thresholds, mode switching, and a clear scope boundary. The remaining parameter semantics are fully covered by the schema, so nothing essential is missing.

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?

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining that water-level readings are in metres relative to local datum, that rainfall values are incremental millimetres with no flood class, and that variable defaults to water_level. This meaningfully clarifies parameter behavior beyond raw schema descriptions.

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 states a specific verb ('Returns') and resource ('river gauge stations') with a clear scope: latest water level, BoM flood thresholds, and a derived floodClass. It also differentiates itself from siblings by covering list-level station data with live flood status, not a single gauge or readings time series.

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?

Clear context is given about defaults (water-level gauges) and the variable=rainfall alternative, plus a negative boundary ('Status reporting, not flood prediction'). However, it does not explicitly route the agent away from sibling tools like get_gauge or list_gauge_readings for single-station or time-series needs.

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.

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