DataQuoll

Audit trail snapshot

incident_snapshot

Read-only

Returns the incidents that were active at a specific point in time. Recent datetimes are reconstructed from live and lifecycle data. Datetimes before 2026-04-08 are reconstructed from the historical archive (4.8M+ records) and are gated by the same tier lookback as /incidents/history (free has no archive access; Starter 1 year, Developer and Business 5 years, Pro and Enterprise unlimited). Archive (pre-2026-04-08) snapshots require a state filter, and meta.source indicates whether the result came from "live" or "archive".

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max results (default 500)
`state`	No	Filter by state code. Required for archive (pre-2026-04-08) snapshots.
`datetime`	Yes	ISO 8601 datetime to query (cannot be in the future)
`event_type`	No	Filter by event type

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations declare readOnlyHint: true, and the description adds substantial behavioral detail: data reconstruction from live/lifecycle versus historical archive, the 2026-04-08 cutoff, tier-based lookback limits, archive state-filter requirement, and meta.source output hint. This goes well beyond the annotations and gives the agent a solid mental model of behavior.

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 carries necessary information: purpose, data source behavior, tier restrictions, archive requirements, and result provenance. The main purpose is front-loaded, and the detail is organized logically without 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?

The description covers the most important operational nuances: archive cutoff, tier access, required filter, and source indicator. There is no output schema, so a little more detail about the result shape would improve completeness, but the description is sufficient for an agent to select and invoke the tool correctly.

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 covers all four parameters with descriptions, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the live/archive split, the exact cutoff date, why a state filter is required for archive snapshots, and how tier affects archive access. This helps the agent interpret datetime and state more effectively.

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 and resource: 'Returns the incidents that were active at a specific point in time.' This clearly distinguishes the snapshot operation from sibling tools like list_incidents or get_incident, and the temporal focus is immediately evident.

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 provides clear context about when archive vs live data applies, tier lookback restrictions, the requirement for a state filter on archive snapshots, and how to identify the data source via meta.source. It does not explicitly name alternative sibling tools, but the conditions for using this tool are well specified.

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

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