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get_resource_metrics

Read-onlyIdempotent

Fetch historical metric data for a resource, including CPU, memory, disk, and network usage, with rollup aggregation (AVG, MAX, MIN) and adjustable time window.

Instructions

[READ] Fetch time-series metric statistics for a resource.

Args: resource_id: The resource UUID. metric_keys: List of metric keys to fetch, e.g. ["cpu|usage_average", "mem|usage_average"]. Common keys: cpu|usage_average, mem|usage_average, disk|usage_average, net|usage_average, cpu|demand_average, mem|workload. hours: Number of hours of history to retrieve. Default 1. rollup_type: Aggregation type: AVG, MAX, MIN, SUM, COUNT, LATEST. Default AVG. target: Optional Aria Operations target name from config. Uses default if omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resource_idYes
metric_keysYes
hoursNo
rollup_typeNoAVG
targetNo
Behavior4/5

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

Annotations already indicate safe read-only behavior. The description adds value by detailing parameter formatting (e.g., metric_keys examples, rollup_type options) and default values, but does not disclose other behaviors like pagination or limits. This goes beyond annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively concise but uses a verbose docstring format with labeled arguments. It front-loads the purpose in the first line and then details parameters efficiently. Could be slightly more compact, but overall well-structured.

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?

The tool has 5 parameters and no output schema. The description thoroughly documents input parameters but omits information about the return format or any error conditions. While not critical for a read tool, some additional context on output structure would improve completeness.

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?

With 0% schema description coverage, the description compensates by explaining all five parameters: resource_id as UUID, metric_keys with examples, hours as history duration, rollup_type with valid values, and target with optional usage. This provides meaning beyond the raw schema.

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 'Fetch time-series metric statistics for a resource', specifying a concrete verb and resource. This distinguishes it from siblings like alerts, reports, and health checks, which operate on different entities or actions.

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 explains the tool's function and parameters but does not provide explicit guidance on when to use it versus alternatives. Given the sibling tools, it is unique for metrics, so no exclusion or context is given for other scenarios.

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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