get_schemaReturns all valid enum values for event types, categories, states, warning levels, and other filterable fields. Use this to discover what values are accepted by filter parameters. No authentication required.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already marks this as read-only, and the description adds a useful behavioral fact: 'No authentication required.' It also discloses that the returned enum set is exhaustive for the listed fields. The output format is not specified, but this is a simple zero-parameter metadata endpoint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short, focused sentences with no filler. The core purpose and the practical usage instruction are both front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only metadata tool, the description is complete. It explains what the caller will get (valid enum values), why to call it (to discover filter values), and the auth expectation. No output schema exists, but the description sufficiently covers the return value semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema already reflects that, so there is no parameter burden for the description to carry. The baseline of 4 applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns all valid enum values for event types, categories, states, warning levels, and other filterable fields. This makes it immediately distinguishable from sibling data-retrieval tools like list_events or list_states.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to use this tool to discover what values are accepted by filter parameters. It does not name alternatives, but no sibling tool appears to provide schema/enum discovery, so the usage guidance is clear and sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.