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OpenShift 4 MCP Server

get_rhoai_component_status

Check RHOAI/OpenShift AI overall health: inspect pods in RHOAI namespaces, DSC/DSCI status, and CRD presence to detect component failures.

Instructions

Check RHOAI/OpenShift AI overall health: pods in RHOAI namespaces, DSC/DSCI status, CRD presence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clusterNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations present, the description carries the burden of explaining behavior. It clearly discloses what resources are inspected (pods, DSC/DSCI, CRDs) and 'Check' implies a read-only operation, but it does not explicitly state that the tool has no side effects, does not require special permissions, or how it aggregates the health status. This is adequate but not fully transparent.

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 one concise sentence with the main purpose front-loaded and a colon-delimited list of the health dimensions. Every word contributes meaning and there is no filler or repetition.

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 description covers the main subject matter and an output schema is present, so return values do not need explanation. However, the cluster parameter is completely undocumented, and the tool's relationship to narrower RHOAI-related sibling tools is not addressed. For a moderately complex health-check tool, this is a visible gap.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not mention the 'cluster' parameter at all. The input schema only provides a default empty string, so the agent is left without any explanation of whether cluster is required, what values are valid, or how the default is interpreted. The description adds zero value here.

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 uses a specific verb ('Check') and identifies a clear resource scope ('RHOAI/OpenShift AI overall health'), then enumerates concrete elements: pods in RHOAI namespaces, DSC/DSCI status, and CRD presence. This distinguishes it from narrower siblings like get_dsci, list_pods, and list_crds by framing it as an aggregate health check.

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 phrase 'overall health' implies this tool is for a high-level RHOAI health assessment, but the description never explicitly says when to use it versus alternatives like get_dsci or get_data_science_cluster. There is no when-not-to-use guidance or any mention of sibling tools, so the usage context is only implied.

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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