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get_k8s_cluster_health

Read-onlyIdempotent

Computes a high-level health scorecard across nodes, workloads, system pods, and control-plane components (GKE cluster state, admission webhooks, and API Priority and Fairness).

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": {
      "parent": "<parent>"
    },
    "name": "get_k8s_cluster_health"
  }
}

Input Schema

NameRequiredDescriptionDefault
parentYesRequired. The cluster to inspect. Format: projects/{project}/locations/{location}/clusters/{cluster}

Output Schema

NameRequiredDescriptionDefault
errorsNoErrors encountered during health computation.
resultNoJSON string representing the cluster health scorecard.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral context by detailing exactly which subsystems contribute to the score (GKE cluster state, admission webhooks, API Priority and Fairness). This goes beyond the safety hints provided in the annotations and informs the agent about the depth of the operation.

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 definition is extremely efficient, utilizing a single sentence to communicate complex technical scope with zero wasted words or fluff.

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

Completeness5/5

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

Given the complexity of Kubernetes health checks and the strong supporting structure of annotations and schema, the description provides a complete and sufficient picture for the AI agent to use the tool effectively.

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

Parameters3/5

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

The schema provides 100% coverage for the single parameter, 'parent'. Therefore, the description provides no additional semantic meaning beyond defining the scope of the overall operation executed on that parameter.

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 clearly defines the purpose using strong verbs ('Computes'), specifies the output format ('health scorecard'), and lists all major components included in the calculation (nodes, workloads, system pods, control-plane components).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly guides usage by defining the scope of the health check, differentiating it from tools that might only list logs or resources. It clearly establishes this tool's role as a comprehensive diagnostic summary.

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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