k8scortex-mcp

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "namespace": "<namespace>"
    },
    "name": "create_horizontal_pod_autoscaler"
  }
}

Input Schema

Name	Required	Description
`dryRun`	No	If true, simulates the action without making changes
`cluster`	No	Target cluster name (default: 'default')
`hpaName`	No	The name of the HPA to create
`namespace`	Yes	The Kubernetes namespace
`maxReplicas`	No	Maximum number of replicas
`minReplicas`	No	Minimum number of replicas
`yamlManifest`	No	Full YAML manifest for advanced HPA creation
`targetDeployment`	No	The name of the target Deployment to scale
`targetCPUUtilizationPercentage`	No	Target CPU utilization percentage

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.2Jul 28, 2026

TDQS

A3.5/5.0

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.

Deploy Server