k8s_update_resources

THE RISK

Medium Risk

Why k8s_update_resources needs a policy

This tool modifies resource allocations (CPU/memory requests and limits) for containers in Kubernetes deployments. It is a Write operation as it changes existing configuration. Severity is high because misconfiguring resource limits can cause containers to OOM-kill, starve other workloads, or make deployments unschedulable, potentially disrupting production services at scale across multiple clusters.

From the tool's definition Update CPU/memory requests and limits for a specific container in a deployment

Documented attack patterns abuse exactly the kind of access k8s_update_resources gives an agent:

POLICY

How to control k8s_update_resources

PolicyLayer is an MCP gateway — it sits between your AI agents and Multi Cluster Kubernetes MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for k8s_update_resources:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "k8s_update_resources": {
      "limits": [
        {
          "counter": "k8s_update_resources_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

k8s_update_resources stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

Create a free account and register Multi Cluster Kubernetes MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

LIMIT THIS TOOL →

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EXPLORE

Related tools and policies

More Multi Cluster Kubernetes MCP Server tools

Destructive k8s_delete_ns Delete a Kubernetes namespace and all resources within it. Destructive k8s_delete_resource Delete a Kubernetes resource. Supports all common resource types Destructive k8s_drain_node Drain a node by evicting all pods, preparing it for maintenance. Execute k8s_pod_exec Execute a command inside a running container. Supports quoted arguments Execute k8s_rollout_restart Trigger a rolling restart of a deployment, daemonset, or statefulset. Execute k8s_run_pod Create and run a pod with the specified container image. Execute k8s_scale_resource Scale a deployment, statefulset, or replicaset to the specified number of replicas. Execute k8s_rollout_pause Pause an in-progress rollout for a deployment, daemonset, or statefulset.

All 57 Multi Cluster Kubernetes MCP Server tools →

Write tools on other servers

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Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Argument validation → Checking tool call arguments against schema and policy before they run.
Scoped token → Per-person credentials that grant a defined subset of servers and tools.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about k8s_update_resources

What does the k8s_update_resources tool do? +

Update CPU/memory requests and limits for a specific container in a deployment,. It is categorised as a Write tool in the Multi Cluster Kubernetes MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on k8s_update_resources? +

Register the Multi Cluster Kubernetes MCP Server MCP server in PolicyLayer and add a rule for k8s_update_resources: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Multi Cluster Kubernetes MCP Server. Nothing to install.

What risk level is k8s_update_resources? +

k8s_update_resources is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit k8s_update_resources? +

Yes. Add a rate_limit block to the k8s_update_resources rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block k8s_update_resources completely? +

Set action: deny in the PolicyLayer policy for k8s_update_resources. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides k8s_update_resources? +

k8s_update_resources is provided by the Multi Cluster Kubernetes MCP Server MCP server (razvanmacovei/k8s-multicluster-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Multi Cluster Kubernetes MCP Server tool call.

Start from Multi Cluster Kubernetes MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →