Radar Kubernetes MCP Server

Name: Radar Kubernetes MCP Server
Author: skyhook-io

by skyhook-ioversion 1.19.0i★ –iRuns on your computer

Registry nameio.github.skyhook-io/radar

Install

Overview

Description from the maker (original English)

Kubernetes MCP server for diagnosis, resource management, GitOps, and RBAC-enforced operations.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: Streamable HTTP
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "radar": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/skyhook-io/radar:1.19.0"
      ]
    }
  }
}

Run this in your terminal

claude mcp add radar -- docker run -i --rm ghcr.io/skyhook-io/radar:1.19.0

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "radar": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/skyhook-io/radar:1.19.0"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "radar": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/skyhook-io/radar:1.19.0"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account skyhook-io, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 0% of the other 1392 servers in this category
Published to the registry more recently than 82% of the other 1392 servers in this category
Basic checks equal to or better than 100% of the other 1392 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Radar Kubernetes MCP Server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.19.0) reached the registry 8 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Radar

The missing open-source Kubernetes UI, with a built-in MCP server for your AI agent.

Single binary. No account required. Free forever.

🌐 [radarhq.io](https://radarhq.io) · Docs · Benchmarks · Releases

Radar shows you what's broken in a Kubernetes cluster, why, and what changed: issues, topology, a timeline of events and resource changes, Helm, GitOps, traffic, audit, and upgrade impact. Your AI agent reads the same cluster data and relationships through Radar's MCP server, and Radar can run an AI investigation for you with the agent CLI you already use. It runs on your laptop as a kubectl plugin or desktop app, or in-cluster with Helm.

Table of contents

Why Radar? · Debugging in the UI
AI agents: MCP server and AI investigations — Connect your agent · AI investigations · Benchmarks
Installation
Usage
Views — Issues · Topology · Resources · Image Filesystem · Timeline · Helm · Compare · TLS · GitOps · Traffic · Metrics · Capacity · Cost · Audit · Network path · Upgrade impact · RBAC · Auth
Supported Resources & Integrations
Keyboard Shortcuts
Security
Development · Contributing

Install and run in 30 seconds:

curl -fsSL https://get.radarhq.io | sh && kubectl radar

Or brew install kubectl-radar. Windows, Krew, desktop app and Helm ↓

Then, with Radar running, connect your AI agent (read-only):

claude mcp add --transport http radar http://localhost:9280/mcp-readonly

Codex, Cursor, GitHub Copilot, other clients, and the desktop app ↓

Why Radar?

Nothing to install in your cluster — a single binary on your laptop that talks to the Kubernetes API with your kubeconfig. No agents, no CRDs, no dependencies
Fast on big clusters — tested on tens of thousands of pods; watches the cluster through informers and pushes live updates to the browser over SSE
Private by design — your cluster data stays on your machine. No account, no agents, no cloud sync. Usage reporting is off unless you opt in, and then sends only anonymous usage counts and coarse cluster facts: Kubernetes version, platform, node-count range, known integrations (what Radar sends). An AI agent you connect sends what it reads to its own model provider
Airgapped-friendly — runs as a single binary against the Kubernetes API and works in locked-down environments with outbound egress blocked
Works everywhere — GKE, EKS, AKS, minikube, kind, k3s, or any conformant cluster
One model of the cluster, for you and your agent — the UI, the MCP server, and AI investigations read the same live data: relationships, health, events, and changes
Ready for teams — deploy in-cluster with Helm for shared access, with SSO (OIDC or an auth proxy) and per-user Kubernetes RBAC in the open-source build

"Have Radar deployed at work. As far as Kubernetes dashboards go, this is one of the best." — u/TheRealNetroxen

Debugging in the UI

Start from [Issues](#issues), a ranked queue of what's broken right now. Open a failing resource to see it in context: its owners, Pods, Services, and config on the [Topology](#topology), what changed and when on the [Timeline](#timeline) (with diffs), and its events, logs, and YAML in the detail drawer. [Helm](#helm), [GitOps](#gitops), and [Traffic](#traffic) cover how it got deployed and who talks to it. When you want help, Investigate hands the same evidence to your agent (AI investigations). Radar records resource changes while it watches the cluster; history is in memory by default, and --timeline-storage=sqlite or postgres keeps it across restarts.

Issues for the incident the agent diagnoses below: a crash loop on checkout and a Service with no endpoints

AI agents: MCP server and AI investigations

Pointed at raw kubectl, an agent spends its context window on YAML and its time on round trips. Radar's built-in Model Context Protocol (MCP) server gives it what Radar already knows instead: how resources connect, what is unhealthy, deduplicated events, recent changes with diffs, and filtered logs. In our benchmarks, the same agent and model reached a diagnosis about 3× faster through Radar than through kubectl (details).

Claude Code on Radar's read-only endpoint, finding why a rollout failed (replay of a real session)

Connect your agent

MCP is on by default and served on the same port as the UI: 9280 unless you set --port. The desktop app picks its own port: copy the URL from Radar's MCP dialog and change the final /mcp to /mcp-readonly. Start with the read-only endpoint:

# Claude Code

claude mcp add --transport http radar http://localhost:9280/mcp-readonly

Codex

codex mcp add radar --url http://localhost:9280/mcp-readonly

Setup for Cursor, VS Code (GitHub Copilot), Copilot CLI, OpenCode, Google Antigravity, Kiro, JetBrains, Cline, Devin, Claude Desktop, Zed, Gemini CLI, and any other MCP client is in the [MCP guide](docs/mcp.md#setup-instructions). Its examples use /mcp; use /mcp-readonly instead to keep Radar's tools read-only. Your agent's other tools follow its own configuration.

Context, not raw YAML — topology, health assessments, events, changes, and logs, minified so they don't burn the context window. The tools cover resources and topology, issues, events and changes, logs, Helm, audit, upgrade readiness, RBAC, cost, and Prometheus
You pick the endpoint — /mcp-readonly has no write tools. The full /mcp endpoint, also on by default, adds write tools (restart, scale, apply, rollback, GitOps sync, node drain) marked destructive so clients can ask before running them. On /mcp, diagnose can also run an in-cluster reachability probe that creates short-lived pods. Calls use Radar's Kubernetes access: your kubeconfig locally, or each user's own RBAC when authentication is on. --mcp-read-only turns /mcp off (and with it, applying a fix from an AI investigation); --no-mcp turns MCP and AI investigations off. Neither blocks ordinary UI writes
Secret values are withheld — Secret reads never include .data or .stringData. Environment variables, Helm values, and logs are scrubbed for known secret patterns, which can't catch everything, and the opt-in exec_pod tool returns raw command output. See MCP security

AI investigations

Radar can also run the investigation for you, with the agent CLI you already have — Claude Code, Codex, Cursor, or OpenCode — on your machine and signed in with your own account. Radar needs no API key of its own. Start one from a resource's Investigate action, from an issue, by describing what you're seeing on the AI investigations page (where to start, since when, and optionally the resource it's about), or from a terminal against a running Radar:

radar diagnose deploy/checkout -n shop

radar diagnose deploy/checkout -n shop --json > conclusion.json

Every investigation ends on the same Findings page, with the evidence behind each claim. Radar's investigation tools can only read the cluster, and Radar applies a fix only when you approve it, from an investigation of the resource it changes. What else the agent can do depends on how Radar runs it. Radar safeguards (Claude Code and Codex) shuts off the agent's other MCP servers and its ability to write. Your agent setup (the only option for Cursor and OpenCode) runs the agent with its normal configuration, tools, and MCP servers, which Radar cannot constrain and which may be able to change your cluster. Cursor and OpenCode approve tool requests automatically in these runs, without asking you. See agents and isolation.

Investigations need a supported agent CLI on the machine running Radar, MCP on, and authentication off. In-cluster installs don't run them. See the AI investigations guide and agent CLI setup.

Benchmarks

Three studies of an AI agent diagnosing live faults from SREGym (Microsoft and UIUC) on a 3-node EKS cluster, with the same judge scoring every tool. Times are averages to a submitted answer, right or wrong. These are our own benchmarks; compare numbers within a study, not across studies.

Study	Faults	Result
Radar MCP vs raw kubectl — same agent and model	50	64s vs 191s to a diagnosis (about 3× faster); 86% vs 60% diagnosed correctly within two minutes
Kubernetes MCP servers — Radar, containers/kubernetes-mcp-server, Flux159/mcp-server-kubernetes, Amazon EKS MCP Server, k8sgpt, raw kubectl	25	Radar took 57s, 2.3× faster than the next-fastest MCP server; on the 14 faults every tool got right, 33s vs 58s for the next-fastest

Only the beginning is shown; read the rest in the repo

Radar Kubernetes MCP Server

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

Radar

Why Radar?

Debugging in the UI

AI agents: MCP server and AI investigations

Connect your agent

Codex

AI investigations

Benchmarks