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get_top_consumers

Identify the highest resource consumers in VMware Aria Operations by querying top metrics like CPU, memory, disk, or network usage across virtual machines or other resources.

Instructions

Query resources with highest consumption of a given metric.

Args: metric_key: The metric to rank by. Common values: cpu|usage_average, mem|usage_average, disk|usage_average, net|usage_average. resource_kind: Resource kind to scope the query. Default VirtualMachine. top_n: Number of top consumers to return (max 50). Default 10. target: Optional Aria Operations target name from config. Uses default if omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metric_keyNocpu|usage_average
resource_kindNoVirtualMachine
top_nNo
targetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses important constraints like 'max 50' for top_n and default behaviors, but omits safety classification (read-only vs destructive), rate limits, or error handling behavior. The term 'Query' implies read-only operation but doesn't explicitly confirm it.

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?

Well-structured with a clear one-sentence purpose statement followed by an Args section. No redundant text. The information is front-loaded and every sentence earns its place by conveying specific constraints or examples.

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?

Given the existence of an output schema (not shown), the description appropriately focuses on input parameters and constraints rather than return values. It adequately covers the 4 parameters and mentions the Aria Operations domain context, though it could briefly mention the return type (ranked list).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Excellent compensation for 0% schema description coverage. The Args section documents all 4 parameters with meaningful context: metric_key includes common values (cpu|usage_average, etc.), resource_kind notes the default VirtualMachine, top_n specifies the max constraint, and target explains the Aria Operations connection.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool queries resources with highest consumption using the verb 'Query' and specifies ranking by metric. However, it doesn't explicitly differentiate from similar metrics tools like get_resource_metrics or get_capacity_overview in the sibling list.

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 explicit guidance on when to use this versus alternatives like get_resource_metrics (which retrieves metrics for specific resources) or get_capacity_overview. The description documents parameters but lacks contextual 'when-to-use' or 'when-not-to-use' guidance.

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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