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Glama
ay-garg

OpenShift 4 MCP Server

create_hpa

Create a Horizontal Pod Autoscaler to automatically scale Deployments or DeploymentConfigs based on target CPU utilization, with configurable min and max replicas.

Instructions

Create an HPA using 'oc autoscale'. target_ref format: 'Deployment/my-app' or 'DeploymentConfig/my-dc'. cpu_percent: target CPU utilisation percentage (default 80).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
clusterNo
namespaceYes
target_refYes
cpu_percentNo
max_replicasYes
min_replicasYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It only reveals that it uses 'oc autoscale' and gives parameter formatting, but does not mention side effects (e.g., whether it overwrites existing HPAs), required permissions, reversibility, or failure behavior. For a mutating tool, this is a significant gap.

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 extremely concise—three short lines—and front-loads the core action. There is no redundant information, and the essential parameter clarifications are included directly after the action. It is well-structured and easy to parse.

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 7 parameters, no schema descriptions, and no annotations, the description is incomplete. It omits explanations for most required parameters and provides no context about expected outcomes, error conditions, or relationship to sibling tools. The existence of an output schema is noted, but without its contents, the description remains insufficient for correct invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain all parameters. It explains target_ref format and the cpu_percent default, but leaves name, namespace, cluster, min_replicas, and max_replicas unexplained. These may be self-evident by name, but the description does not compensate for the missing schema descriptions beyond two parameters.

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 action ('Create an HPA') and the underlying command ('oc autoscale'), which identifies the resource type and differentiates from list/delete HPA tools. It doesn't explicitly contrast with other scaling tools like scale_deployment or create_machine_autoscaler, but the resource focus is specific enough for an agent to distinguish.

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?

The description provides no guidance on when to use this tool versus alternatives (e.g., scale_deployment, create_machine_autoscaler). It doesn't mention prerequisites, typical scenarios, or conditions that would route an agent here. Usage is only implied by the tool name and the action, leaving the agent to infer appropriateness.

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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