k8s-aiops

Disclaimer: This is a community-maintained open-source project and is not affiliated with, endorsed by, or sponsored by the Cloud Native Computing Foundation, the Kubernetes project, or k3s/Rancher. "Kubernetes" and "k3s" are trademarks of their respective owners. Source code is publicly auditable at github.com/AIops-tools/K8s-AIops under the MIT license.

Governed Kubernetes operations for AI agents — 55 MCP tools, every one wrapped with the bundled @governed_tool harness: a local unified audit log under ~/.k8s-aiops/, a token/runaway budget guard, undo-token recording, and a descriptive risk-tier label on every audit row. Coverage spans pods, deployments, statefulsets, daemonsets, replicasets, jobs/cronjobs, services, ingresses, endpoints, configmaps, secrets (names/keys only), PVCs/PVs/storageclasses, nodes, namespaces, events, rollouts (status/history/undo/pause/resume/set-image), pod/node describe, pod/node top, a cluster health summary, and read-only diagnostics / RCA (pod-health and workload-readiness) that flag the root cause worst-first.

Standalone: the governance harness is bundled in the package (k8s_aiops.governance) — k8s-aiops has no external skill-family dependency. Coverage focuses on common cluster operations and is not yet exhaustive.

Verification status: exercised end-to-end against a live kind cluster (v1.36); the diagnostics/RCA tools added in this release are mock-tested only. See docs/VERIFICATION.md.

What works

Any cluster a kubeconfig can reach: standard Kubernetes, k3s, EKS, GKE, AKS, kind, minikube. Authentication (client certs, tokens, EKS/GKE/AKS exec plugins) is delegated entirely to the kubeconfig.

Related MCP server: k8s-mcp-go

What this tool does, and does not, decide

It delivers Kubernetes operations — reads and writes — accurately and efficiently, and records every one of them. It does not decide whether a write is allowed to happen. That is the agent's judgement, or the permission of the kubeconfig context / ServiceAccount you connect it with: point it at a context bound to a read-only RBAC role and the writes fail at the apiserver — the place that actually owns the permission.

So there is no read-only switch, no policy file, no approval gate to configure. The one thing the tool guarantees is that nothing is silent: every call, over MCP and over the CLI alike, lands an audit row in ~/.k8s-aiops/audit.db, and destructive writes still capture their before-state and record an inverse where one exists. The runaway budget guard is a safety backstop, not authorization.

Each tool declares a risk_level, kept in agreement with its [READ]/[WRITE] documentation tag by a test, and carried into the audit row as a descriptive tier — so a reviewer can see at a glance that a row was a high-risk delete. It is a label, not a gate.

Running a smaller / local model? See agent-guardrails.md — it lists the guardrails this tool now enforces for you (so you don't spend prompt budget restating them) and gives a ready-made system prompt for what's left.

Quick Start

uv tool install k8s-aiops

# Friendly onboarding wizard — registers your kube contexts as named targets:
k8s-aiops init

# Or skip it — uses your current kube-context out of the box:
k8s-aiops doctor
k8s-aiops pod list
k8s-aiops deployment list -n default

# Read-only RCA — worst-first root-cause findings, no changes made:
k8s-aiops diagnose pod-health -n prod
k8s-aiops diagnose workload-readiness -n prod

To define named targets (multiple clusters/contexts), create ~/.k8s-aiops/config.yaml:

targets:
  - name: prod          # used as -t prod
    context: prod-eks   # a context in your kubeconfig (omit for current-context)
    namespace: default  # optional default namespace
    # kubeconfig: /path/to/alt/kubeconfig   # optional explicit path
  - name: lab
    context: k3s-lab

No secrets live in this file — credentials come from the kubeconfig.

MCP

{
  "command": "k8s-aiops",
  "args": ["mcp"],
  "env": { "K8S_AIOPS_CONFIG": "~/.k8s-aiops/config.yaml" }
}

Note — MCP servers get a clean environment: most MCP clients spawn the server without your shell's exports, so variables like K8S_AIOPS_HOME, K8S_AUDIT_APPROVED_BY, K8S_AUDIT_RATIONALE (and KUBECONFIG, if your kubeconfig is not at ~/.kube/config) must be set in the MCP server config's env block above — values exported only in your terminal may never reach the server.

Audit & Safety

• Every tool call is logged to ~/.k8s-aiops/audit.db (local SQLite; relocate with K8S_AIOPS_HOME).

• Reversible writes record an inverse undo descriptor (scale_deployment → scale-back to previous; cordon_node ↔ uncordon_node).

• Every MCP write tool takes dry_run=True and returns a {"dryRun": true, ...} preview without touching the cluster (no undo recorded for a preview).

• delete_deployment is risk_level=high; destructive CLI commands require double confirmation, medium-risk ones (deployment scale/restart) a single confirmation, and all write commands support --dry-run.

• All API text passes through sanitize() (output hygiene: control/format-char stripping + truncation).

See skills/k8s-aiops/SKILL.md and SECURITY.md for details.

Secrets

k8s-aiops deliberately has no encrypted secret store (no secrets.enc, no secret CLI): authentication is delegated entirely to your kubeconfig — client certificates, bearer tokens, or exec plugins (EKS/GKE/AKS) — and the tool never handles or stores cluster credentials itself. This is a documented exception to the AIops-tools line-wide encrypted-secret-store pattern.

Companion Skills

Contributing & feature requests

Coverage is intentionally focused. Missing a device, action, or feature you need? Open an issue or pull request at github.com/AIops-tools/K8s-AIops — feature requests, contributions, and comments are all welcome.

License

MIT — github.com/AIops-tools/K8s-AIops