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create_horizontal_pod_autoscaler

Configure automatic scaling for a Kubernetes Deployment by creating a Horizontal Pod Autoscaler with CPU target, min/max replicas, or custom YAML manifest. Supports dry-run for validation.

Instructions

Creates an HPA for a Deployment. Use either basic configuration or provide a raw YAML manifest.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dryRunNoIf true, simulates the action without making changes
clusterNoTarget cluster name (default: 'default')
hpaNameNoThe name of the HPA to create
namespaceYesThe Kubernetes namespace
maxReplicasNoMaximum number of replicas
minReplicasNoMinimum number of replicas
yamlManifestNoFull YAML manifest for advanced HPA creation
targetDeploymentNoThe name of the target Deployment to scale
targetCPUUtilizationPercentageNoTarget CPU utilization percentage
Behavior2/5

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

No annotations provided, and the description does not disclose behavioral traits such as permissions required, whether it overwrites existing HPAs, or error conditions. For a mutation action, more transparency is needed.

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?

Two sentences, direct and efficient. Could be slightly improved by separating the modes more clearly, but overall concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 9 parameters and no output schema or annotations, the description is too minimal. It does not explain prerequisites, return behavior, or error scenarios.

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?

Schema coverage is 100% with good parameter descriptions. The description adds value by clarifying the two usage modes (basic vs YAML), which is not captured in the 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?

Clearly states it creates an HPA for a Deployment, and specifies two configuration methods (basic or YAML). Distinguishes from siblings like get_hpa_status.

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?

Mentions two modes (basic config vs raw YAML) but does not explicitly state when to use this tool versus alternatives or when to choose one mode over the other.

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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