K8S Blast Radius

Kubernetes Blast Radius

Answers hypothetical and real node-failure questions with data. Categorizes every deployment touching the node into full-outage / degraded / unaffected, totals up memory at risk, and checks whether the remaining cluster has capacity to reschedule.

Prerequisites

If the user is not running Kubernetes, this tool does not apply. But a Kubernetes-only customer (no APM) still gets the full pod-level impact assessment and rescheduling feasibility — the majority of the value.

Tools

How to call k8s-blast-radius

{
  "node": "gke-prod-pool-1-abc123",
  "cluster": "prod-us-east",
  "layout": "summary"
}

Parameter-filling guidance:

• node: must be exact. Matched literally against kubernetes.node.name. If the user describes a node ambiguously ("the noisy node", "the one running frontend"), ask them to confirm the exact node name before calling. Do not guess.
• cluster: required when the same node name might exist in multiple clusters — auto-generated cloud node names (GKE / EKS) sometimes collide. Resolves fuzzily against k8s.cluster.name (OTel) / orchestrator.cluster.name (ECS); on miss the response includes cluster_candidates. Omit for single-cluster deployments.
• layout: default summary (compact, collapsible sections). Use radial when the user wants a visual "impact-by-proximity" diagram.

After the tool returns

Response shape:

• status: AT RISK (full outage), PARTIAL RISK (degraded only), or SAFE (no impact).
• data_coverage: which backends contributed (always kubernetes: true; apm: true|false).
• pods_at_risk: count of pods on the node.
• full_outage[]: deployments losing all replicas — lead with these.
• degraded[]: deployments losing partial capacity.
• unaffected / unaffected_count: deployments not touching the node.
• rescheduling: memory required vs available, and whether it's feasible.
• downstream_services[] (only if APM present): user-facing services whose namespace is affected.
• downstream_services_note (only if APM absent): explains the gap.
• investigation_actions: next-step prompts surfaced as click-to-send buttons in the view (includes a SPOF callout when a single-replica deployment is implicated).
• render_instructions: HTML render spec — let the inline MCP App view handle visualization (floating summary card, radial affected-deployment sweep, safe-zone arc, hover tooltips).

Ignore _setup_notice if present — it's view-side chrome (welcome banner) that the UI handles. Don't echo or summarize it in chat.

Narrate in this order:

1. Headline status: "AT RISK — 3 deployments lose all replicas if gke-prod-pool-1-abc123 goes offline."
2. Full outage list: name the deployments. These are the critical ones.
3. Degraded list: name them, note surviving replica counts.
4. Rescheduling feasibility: "Cluster has X GB available across N nodes to absorb Y GB required — safe / not safe / marginal."
5. Downstream services (if APM present): name the services in affected namespaces that might be user-visible.
6. Recommend action: for AT RISK + infeasible reschedule, "don't drain this node without scaling up." For PARTIAL RISK + feasible, "safe to drain with PodDisruptionBudgets in place."

Key principles

• Hypothetical framing. Unless the node is actually down, always present results as "if X goes offline, then Y" — not as current reality.
• Rescheduling feasibility is a heuristic. It compares memory only — doesn't account for CPU, storage, affinity rules, taints, or PodDisruptionBudgets. Note this caveat.
• Full-outage >> degraded. A deployment with 1 replica on the node is a full outage; a deployment with 3 replicas losing 1 is degraded. Treat them very differently in recommendations.
• Downstream services matter. Even if a deployment is degraded not down, user-facing services might see tail latency. Mention the downstream APM services.
• Don't conflate "at risk" with "broken." The status reflects potential impact. The node may be fine.