Kubernetes MCP server with RBAC-style, context-scoped guardrails for AI agents.
MCPpedia last refreshed this data
io.github.kubeleash/kubeleash is an MCP server that kubernetes MCP server with RBAC-style, context-scoped guardrails for AI agents. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 34/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-kubeleash-kubeleash": {
"command": "<see-readme>",
"args": []
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
Guardrails for AI agents on your cluster. kubeleash is a local MCP server for Kubernetes whose differentiator is RBAC-style, context-scoped access control. Point it at a kubeconfig — even a cluster-admin one — and a local policy file constrains what the agent can actually do, per kube context, with destructive actions gated before any call reaches the cluster.
No automated test available for this server. Check the GitHub README for setup instructions.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
No package registry to scan.
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in ai-ml / cloud
MCP client bridge: connects to MCP servers and registers their tools on ctx.tools
An autonomous agent that conducts deep research on any data using any LLM providers
The official MCP server implementation for the Perplexity API Platform
1000+ scientific tools for AI scientists: life science, reserach, literature, and more.
MCP Security Weekly
Get CVE alerts and security updates for io.github.kubeleash/kubeleash and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
Point it at your over-privileged kubeconfig — it still can't nuke prod.
Guardrails for AI agents on your cluster. kubeleash is a local MCP server for Kubernetes whose differentiator is RBAC-style, context-scoped access control. Point it at a kubeconfig — even a cluster-admin one — and a local policy file constrains what the agent can actually do, per kube context, with destructive actions gated before any call reaches the cluster.
Install launches the local kubeleash binary — get it via brew, go install, or the container (see Install). VS Code installs in one click; Cursor & Claude open the setup steps (GitHub strips the cursor:// one-click link, so it lives there as copy-paste).
Most Kubernetes MCP servers inherit the kubeconfig's permissions wholesale — whatever the credentials grant, the agent can do. kubeleash adds three things native RBAC can't express for this use case:
kubeconfig-grants ∩ policy-allows — kubeleash only ever subtracts.delete/exec/…) as a safety net against agent
mistakes and prompt injection.kubeleash governs only the calls routed through it. It provides a hard
guarantee when it is the agent's sole path to the cluster. An agent that also
has shell access (raw kubectl, oc, the API) can step around it, so in a
shell-enabled assistant kubeleash is advisory — pair it with the
using-kubeleash skill, which instructs the agent to stop (not reach for
kubectl) when the leash is unavailable.
policies:
- contexts: ".*prod.*" # regex over the active context name
allow:
resources: ["*"]
verbs: [get, list, watch] # read-only in prod
deny:
verbs: [exec] # never, regardless of credentials
Deny wins. Default deny. A broken policy refuses to start — it never fails open.
See [examples/policy.yaml](examples/polic