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Glama

DataQuoll

by io.dataquoll

Flood status counts by state or LGA

get_gauge_summary
Read-only

Counts of gauges at or above minor/moderate/major flood classification, grouped by state or LGA, plus the full list of at-or-above-minor stations (capped at 500). STALE GUARD: only readings within the staleness horizon enter class counts — a days-old reading above minor is counted as stale, never as a current flood signal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNo
groupByNostate (default) or lga (requires the state parameter)
variableNoMeasured variable. Defaults to water_level (rain gauges carry no flood class, so they are excluded from the counts by default).

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

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

The readOnlyHint annotation already signals a safe read, but the description adds meaningful behavioral detail beyond that: the 500-station cap and, more importantly, the STALE GUARD rule that stale readings are excluded from class counts. This materially changes how an agent should interpret the results.

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?

Two sentences, both information-dense. The core output is stated first, and the critical stale-readings caveat is highlighted and separated clearly. No filler or repetition.

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

Completeness4/5

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

Given the absence of an output schema, the description does a good job outlining both the count summary and the list output, including the 500 cap. The main gap is that the 'staleness horizon' is not quantified, so an agent must still discover that boundary elsewhere.

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?

Schema descriptions already cover groupBy and variable, including the default behavior and rain-gauge exclusion. The tool description mostly restates the grouping concept and does not add parameter-level meaning for the undocumented state parameter, though the title and context make its purpose fairly evident. With 67% schema coverage this is adequate but not exceptional.

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 specifies exactly what the tool returns: counts of gauges at each flood classification threshold, grouped by state or LGA, plus a capped list of stations. This clearly differentiates it from sibling tools like list_gauges or list_gauge_readings, which return different kinds of data.

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?

The intended use case is implied by the description: get flood-status counts and at-or-above-minor stations, grouped by geography. However, there is no explicit guidance on when to prefer this over siblings, nor any stated exclusions or alternatives.

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