Get monitor anomalies
get_monitor_anomaliesAnomaly-detection output for a single monitor (flapping, latency spikes, etc.).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | Monitor UUID (format "mon_..."). |
hyperping
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.5/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose whether the operation is read-only, any authorization requirements, rate limits, or response format, leaving the agent with minimal behavioral context.
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 a single concise sentence that front-loads the purpose with specific examples, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description provides basic context with examples but lacks details on the output structure or additional behavioral information, leaving some gaps.
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 coverage is 100% with the 'uuid' parameter already described. The description adds no new parameter information beyond the schema, so baseline of 3 is appropriate.
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 clearly states the tool returns anomaly-detection output for a single monitor, with examples like flapping and latency spikes, distinguishing it from sibling tools that return other monitor 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 description implies usage for getting anomaly data for a specific monitor, but does not explicitly state when to use it over alternatives like 'get_monitor' or 'get_monitor_uptime', nor does it provide exclusions.
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
A3.5/5.0
Disambiguation5/5
Each tool targets a specific resource or action (monitor, outage, escalation policy, etc.) with clear naming. No two tools could be easily confused; even similar ones like get_monitor_outages and get_monitor_anomalies serve distinct purposes.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern (create_, get_, list_, pause_, resume_, search_, update_) and use snake_case uniformly. This makes the tool set predictable and easy to navigate.
Tool Count4/5
With 26 tools, the set is slightly above the typical well-scoped range of 3-15, but each tool serves a distinct monitoring function (monitors, outages, escalations, integrations, schedules, alerts, team members). The count is reasonable for the domain.
Completeness2/5
The tool surface is heavily read-oriented, lacking create/update/delete for most resource types (escalation policies, integrations, on-call schedules, outages). Even monitors lack a delete tool. This leaves significant gaps for administrative workflows.