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get_cluster_audit

Read-only

Audit Kubernetes cluster configuration for security, reliability, and efficiency issues. Get best-practice findings with remediation guidance to fix misconfigurations.

Instructions

Use when the agent's decision is 'is this cluster well-configured / compliant?' — STATIC CONFIG POSTURE, not live operational state. Returns best-practice findings: Security (runAsRoot, privileged containers, dangerous capabilities, hostPath/hostNetwork, secret-in-ConfigMap), Reliability (single replicas, missing PDB, missing TopologySpread, podHARisk, Service/Ingress without matching backends, stuckTerminating, deprecatedAPIVersion), and Efficiency (missing resource requests/limits, orphaned ConfigMaps/Secrets, under/over-utilization). Each finding has remediation guidance. INDEPENDENT of operational health: a healthy pod can have many audit findings (badly configured but working), a crashing pod can have zero (cleanly configured but failing). For 'what's broken right now?' use the issues tool. Respects user's audit settings (ignored namespaces, disabled checks). Filter by namespace, category, or severity. Resources absent from findings should NOT be reported as non-compliant — empty findings for a scope means no violations, not a failed check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNomax audit violation findings to return (default 30, max 100). This limits findings only; compliant resources are not returned.
categoryNofilter by category: Security, Reliability, or Efficiency
severityNofilter by severity: danger or warning
namespaceNofilter to a specific namespace
Behavior5/5

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

Annotations already indicate readOnlyHint=true, but the description adds crucial behavioral context: audit findings are independent of operational health, empty findings mean no violations (not a failed check), and it respects user audit settings. This goes beyond annotations and prevents misinterpretation.

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 detailed but every sentence earns its place, front-loading the primary use case and then layering critical caveats. It is well-structured with categories and explicit contrast to sibling tools, making it highly informative without redundancy.

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 audit semantics, the description fully covers interpretation guidance (empty findings = no violations, not non-compliant), independence from operational health, and respect for user settings. No output schema exists, so this level of behavioral detail is essential and provided comprehensively.

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?

Schema coverage is 100%, so the schema already documents all parameters with descriptions. The description only re-states filtering by namespace, category, severity, and doesn't add additional semantic value beyond what the schema provides. Baseline 3 is appropriate.

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 states the tool's purpose: assessing static config posture/compliance, and explicitly contrasts it with operational health. It names the specific categories (Security, Reliability, Efficiency) and provides concrete examples, distinguishing it from siblings like 'issues'.

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

Usage Guidelines5/5

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

It explicitly says when to use: when deciding 'is this cluster well-configured / compliant?', and when not: for 'what's broken right now?' use the issues tool. This gives clear alternatives and exclusions.

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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