Feed health by state
list_statesRead-only
Returns the status of each state feed, including last poll time, health, and incident count.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
DataQuoll
by io.dataquoll
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already covers the safety profile. The description adds useful context by specifying the returned status fields, but it does not discuss behavior such as data freshness, error conditions, or how to interpret 'health' values.
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?
A single sentence front-loads the action and resource, and every clause adds a specific output field. There is no redundant or filler content.
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?
For a zero-parameter, read-only operation, the description names the action, resource, and output fields, so nothing is missing to invoke the tool. It is slightly light on output semantics, such as what 'health' values look like, but this is a minor gap.
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 has zero parameters with 100% coverage, so there is no parameter ambiguity. The description correctly implies an unfiltered aggregate listing across all state feeds.
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'), a specific resource ('each state feed'), and enumerates the returned fields: last poll time, health, and incident count. This clearly distinguishes it from sibling tools like list_incidents or list_gauges, which target different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives, and no sibling tools are named. The use case is implied by the title and description, but there is no explicit selection criteria or exclusionary context.
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.