Built-in HTTP MCP server for Radar, the local-first Kubernetes UI that gives Claude token-optimized cluster topology, health, events, logs, audit, RBAC, GitOps, workload, and resource-management tools.
Radar MCP can inspect live Kubernetes resources, topology, warning events, logs, metrics, package inventory, audit findings, RBAC permissions, Helm releases, ArgoCD resources, and FluxCD resources., The MCP implementation includes mutating tools for applying YAML, patching resources, restarting or scaling workloads, triggering or suspending CronJobs, managing GitOps resources, and cordoning, uncordoning, or draining nodes., Mutating tools are RBAC-enforced and annotated as destructive-capable, but clients and operators should still require human review before running them., `apply_resource` with forced ownership can take server-side-apply fields from other managers such as Helm or GitOps controllers., `manage_node drain` can evict pods and affect live workloads., Radar provides flags and environment variables that disable MCP, disable exec/local terminal paths, or loosen localhost/origin protections; only relax those protections in trusted development contexts.
Privacy notes
Kubeconfig paths, cluster names, namespaces, resource names, manifests, labels, annotations, topology edges, metrics, events, logs, RBAC rules, Helm release data, GitOps metadata, and audit findings may be exposed to the MCP client and model provider., Radar's MCP docs state that Secret data is not exposed and environment/log values are redacted for known sensitive patterns, but metadata, key names, errors, and operational context can still be sensitive., Workload logs can contain credentials, customer data, request payloads, internal hostnames, incident details, or proprietary deployment context., In-cluster deployments without authentication make all MCP callers share the pod service account view, so restrict network access, enable auth, and scope RBAC before exposing shared endpoints.
Author
Skyhook
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06
Decision playbook
Review trust signals before you adopt
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
Compare context
Selected
0
Current score
63
Baseline
—
Delta
No baseline selected
No major trust-signal divergence detected in the current selection.
Source and provenance checks
Needs review
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Done
Source provenance statusRequired
Marked as source-backed.
Done
Metadata reviewed
No reviewed flag detected in metadata.
Pending
Safety and privacy checks
Complete
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Done
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Done
Trust level risk gateRequired
Trust level does not block evaluation.
Done
Package and install checks
Needs review
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Done
Package verification flag
No package verification flag provided.
Pending
Checksum metadata
No checksum provided for downloaded artifact.
Pending
Compare-driven decision checks
Needs review
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
6 safety and 4 privacy notes across 6 risk areas. Review closely: credentials & tokens, permissions & scopes, third-party handling.
6 areas
SafetyPermissions & scopesRadar MCP can inspect live Kubernetes resources, topology, warning events, logs, metrics, package inventory, audit findings, RBAC permissions, Helm releases, ArgoCD resources, and FluxCD resources.
SafetyGeneralThe MCP implementation includes mutating tools for applying YAML, patching resources, restarting or scaling workloads, triggering or suspending CronJobs, managing GitOps resources, and cordoning, uncordoning, or draining nodes.
SafetyExecution & processesMutating tools are RBAC-enforced and annotated as destructive-capable, but clients and operators should still require human review before running them.
SafetyGeneral`apply_resource` with forced ownership can take server-side-apply fields from other managers such as Helm or GitOps controllers.
SafetyGeneral`manage_node drain` can evict pods and affect live workloads.
SafetyLocal filesRadar provides flags and environment variables that disable MCP, disable exec/local terminal paths, or loosen localhost/origin protections; only relax those protections in trusted development contexts.
PrivacyThird-party handlingKubeconfig paths, cluster names, namespaces, resource names, manifests, labels, annotations, topology edges, metrics, events, logs, RBAC rules, Helm release data, GitOps metadata, and audit findings may be exposed to the MCP client and model provider.
PrivacyCredentials & tokensRadar's MCP docs state that Secret data is not exposed and environment/log values are redacted for known sensitive patterns, but metadata, key names, errors, and operational context can still be sensitive.
PrivacyCredentials & tokensWorkload logs can contain credentials, customer data, request payloads, internal hostnames, incident details, or proprietary deployment context.
PrivacyPermissions & scopesIn-cluster deployments without authentication make all MCP callers share the pod service account view, so restrict network access, enable auth, and scope RBAC before exposing shared endpoints.
Disclosure: Apache-2.0 open-source Kubernetes UI with a built-in MCP server. The upstream project also mentions Radar Cloud for hosted team use.
Safety notes
Radar MCP can inspect live Kubernetes resources, topology, warning events, logs, metrics, package inventory, audit findings, RBAC permissions, Helm releases, ArgoCD resources, and FluxCD resources.
The MCP implementation includes mutating tools for applying YAML, patching resources, restarting or scaling workloads, triggering or suspending CronJobs, managing GitOps resources, and cordoning, uncordoning, or draining nodes.
Mutating tools are RBAC-enforced and annotated as destructive-capable, but clients and operators should still require human review before running them.
`apply_resource` with forced ownership can take server-side-apply fields from other managers such as Helm or GitOps controllers.
`manage_node drain` can evict pods and affect live workloads.
Radar provides flags and environment variables that disable MCP, disable exec/local terminal paths, or loosen localhost/origin protections; only relax those protections in trusted development contexts.
Privacy notes
Kubeconfig paths, cluster names, namespaces, resource names, manifests, labels, annotations, topology edges, metrics, events, logs, RBAC rules, Helm release data, GitOps metadata, and audit findings may be exposed to the MCP client and model provider.
Radar's MCP docs state that Secret data is not exposed and environment/log values are redacted for known sensitive patterns, but metadata, key names, errors, and operational context can still be sensitive.
Workload logs can contain credentials, customer data, request payloads, internal hostnames, incident details, or proprietary deployment context.
In-cluster deployments without authentication make all MCP callers share the pod service account view, so restrict network access, enable auth, and scope RBAC before exposing shared endpoints.
Prerequisites
Radar installed from Homebrew, Krew, GitHub Releases, Scoop, or another upstream-supported path.
Running Radar server or `kubectl radar` session with the MCP endpoint enabled.
MCP client that supports Streamable HTTP server configuration.
Kubernetes kubeconfig, context, namespace scope, or in-cluster service account reviewed before connecting an AI client.
Human approval policy for apply, patch, workload, CronJob, GitOps, and node-management tools.
Apache-2.0 open-source Kubernetes UI with a built-in MCP server. The upstream project also mentions Radar Cloud for hosted team use.
Full copyable content
claude mcp add radar --transport http RADAR_MCP_URL
About this resource
Content
Radar is a local-first Kubernetes UI from Skyhook. Its built-in MCP server lets
Claude and other MCP clients query a running Radar session through Streamable
HTTP, using curated tools rather than raw kubectl output.
The MCP surface is built for cluster troubleshooting and operations. It exposes
token-optimized tools for dashboards, topology, neighborhoods, issues, events,
logs, resource search, resource details, package inventory, Helm release
inspection, RBAC review, and cluster audit findings. It also includes guarded
write tools for Kubernetes resource changes, workload actions, GitOps actions,
CronJob actions, and node management.
These sources were reviewed on 2026-06-06. Prefer the live repository,
Radar website, releases page, MCP guide, license, MCP handler source, tool
registration source, and apply-tool source for current setup, transport,
security controls, and tool behavior.
Features
Built into the Radar binary and enabled by default unless Radar starts with
MCP disabled.
Streamable HTTP transport mounted on the running Radar server.
Client setup examples for Claude Code, Claude Desktop, Cursor, Windsurf, VS
Code Copilot, Cline, JetBrains AI, OpenAI Codex, and Gemini CLI.
Read tools for dashboards, top resource usage, resource lists, resource
detail, topology, neighborhoods, events, changes, pod logs, workload logs,
cluster audit, package inventory, Helm releases, namespaces, search, and RBAC
subject permissions.
Diagnostic tool that bundles resource context, logs, events, and startup
blockers for a workload.
Write tools for applying YAML, patching resources, restarting, scaling, or
rolling back workloads, managing CronJobs, managing ArgoCD/FluxCD resources,
and cordoning, uncordoning, or draining nodes.
Secret-data suppression, environment/log redaction, RBAC-aware access, and
destructive-tool annotations in the MCP implementation.
Local binary flow for personal cluster access and in-cluster deployment path
for shared team access with authentication and RBAC planning.
Installation
Install Radar through an upstream-supported path such as Homebrew, Krew,
GitHub Releases, Scoop, or the desktop installers. For Homebrew:
brew install skyhook-io/tap/radar
Start Radar with access to the intended Kubernetes context:
kubectl radar
Then configure an HTTP-capable MCP client with the Radar MCP endpoint for your
chosen host and port:
Use a local endpoint for personal development. For shared or in-cluster
deployments, review Radar authentication, Kubernetes RBAC, namespace scoping,
and network exposure before connecting AI clients.
Use Cases
Ask Claude what is broken in a Kubernetes namespace before drilling into a
failing workload.
Summarize topology relationships around a Service, Deployment, Pod, or
GitOps-managed application.
Pull focused pod or workload logs after Radar has narrowed the affected
resource.
Review audit findings, RBAC permissions, package inventory, and warning
events from one MCP surface.
Investigate recent changes that may explain an incident.
Trigger a reviewed restart, scale, rollback, CronJob run, GitOps action, or
node operation only after confirming the exact scope.
Use Radar's minified resource output when raw Kubernetes YAML would be too
noisy for an LLM context window.
Safety and Privacy
Radar MCP can operate against real Kubernetes clusters. Start with the smallest
namespace and RBAC scope that supports the workflow, and require human approval
before any apply, patch, workload action, CronJob action, GitOps action, or
node-management call. Be especially careful with server-side apply ownership,
node drains, production rollbacks, and actions that interact with Helm,
ArgoCD, or FluxCD state.
Treat cluster metadata as sensitive. Even with Secret data suppression and
redaction controls, manifests, labels, annotations, logs, events, metrics,
repository references, namespace names, RBAC rules, and topology edges can
reveal customer data, security posture, incident details, or internal
architecture. Keep the MCP endpoint local or authenticated, and do not expose
unauthenticated in-cluster deployments to untrusted clients.
Duplicate Check
No skyhook-io/radar, Radar MCP Server, radarhq.io, or kubectl radar entry
was found in content/mcp or README.md. Existing Kubernetes and Argo CD MCP
entries cover different direct server implementations; the TrendRadar entry is
an unrelated news and RSS monitoring project.
Show that Radar MCP Server is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/mcp/radar-mcp-server)
How it compares
Radar MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
Built-in HTTP MCP server for Radar, the local-first Kubernetes UI that gives Claude token-optimized cluster topology, health, events, logs, audit, RBAC, GitOps, workload, and resource-management tools.
Manage Docker and Kubernetes infrastructure from Claude — list environments, inspect containers, manage GitOps workflows, troubleshoot resources, and proxy Docker/K8s API calls — with the official Portainer MCP server generated from the Portainer OpenAPI spec.
Google Cloud gcloud MCP server from googleapis that lets Claude run approved gcloud CLI commands with allowlist and denylist controls for cloud resource inspection, automation, and operations.
✓Radar MCP can inspect live Kubernetes resources, topology, warning events, logs, metrics, package inventory, audit findings, RBAC permissions, Helm releases, ArgoCD resources, and FluxCD resources.
The MCP implementation includes mutating tools for applying YAML, patching resources, restarting or scaling workloads, triggering or suspending CronJobs, managing GitOps resources, and cordoning, uncordoning, or draining nodes.
Mutating tools are RBAC-enforced and annotated as destructive-capable, but clients and operators should still require human review before running them.
`apply_resource` with forced ownership can take server-side-apply fields from other managers such as Helm or GitOps controllers.
`manage_node drain` can evict pods and affect live workloads.
Radar provides flags and environment variables that disable MCP, disable exec/local terminal paths, or loosen localhost/origin protections; only relax those protections in trusted development contexts.
✓The server exposes the full Portainer REST API — including container start/stop/delete, stack deployment, and Kubernetes resource management.
The API proxy tools forward requests directly to your Docker/Kubernetes API — review before executing any destructive operations.
For team deployments over HTTP, the container requires TLS (either BYO certificates or a TLS-terminated reverse proxy) — do not expose plaintext on the public internet.
✓Coolify MCP Server can start, stop, restart, redeploy, cancel deployments, update env vars, create or delete projects, environments, applications, databases, services, backups, storages, scheduled tasks, private keys, GitHub apps, and cloud tokens depending on API permissions.
Batch tools such as `restart_project_apps`, `bulk_env_update`, `stop_all_apps`, and `redeploy_project` can affect multiple production services at once.
Deployment, control, backup, storage, private key, cloud token, GitHub app, and scheduled-task operations should require explicit confirmation.
Custom `--header` values may carry auth-proxy secrets; never let an agent invent, log, or modify them casually.
Test on non-production projects or staging resources before allowing Claude to operate live Coolify infrastructure.
✓gcloud MCP Server executes gcloud CLI commands with the permissions of the active gcloud account.
Allowed commands can create, update, delete, deploy, scale, list, export, or configure Google Cloud resources depending on IAM permissions and selected services.
The server blocks command substitution, pipes, redirection, SSH-style commands, interactive commands, and a default set of sensitive command prefixes, but allowed gcloud commands can still have real infrastructure, billing, IAM, and data impact.
Use allowlists for narrow workflows and service account impersonation with limited roles when possible.
Require human approval for IAM, billing, networking, firewall, storage, database, secret, deployment, delete, and production-impacting commands.
Privacy notes
✓Kubeconfig paths, cluster names, namespaces, resource names, manifests, labels, annotations, topology edges, metrics, events, logs, RBAC rules, Helm release data, GitOps metadata, and audit findings may be exposed to the MCP client and model provider.
Radar's MCP docs state that Secret data is not exposed and environment/log values are redacted for known sensitive patterns, but metadata, key names, errors, and operational context can still be sensitive.
Workload logs can contain credentials, customer data, request payloads, internal hostnames, incident details, or proprietary deployment context.
In-cluster deployments without authentication make all MCP callers share the pod service account view, so restrict network access, enable auth, and scope RBAC before exposing shared endpoints.
✓Container names, environment configurations, Docker/Kubernetes resource details, and GitOps credentials from your Portainer instance are surfaced in Claude's context.
Your Portainer API key grants the same access as your Portainer user account — treat it as a secret.
✓Coolify access tokens, base URLs, custom headers, application UUIDs, server IPs, domains, logs, env vars, deployment logs, private keys, cloud-provider tokens, GitHub app data, team membership, backups, database metadata, and service configuration can be exposed to the MCP client.
Application and deployment logs may contain secrets, customer data, build output, container metadata, private repository details, and runtime errors.
Environment variable and cloud-token tools can reveal or mutate sensitive infrastructure credentials.
Documentation search is local to the server, but Coolify API calls contact the configured Coolify instance.
Keep tokens and auth-proxy headers in local MCP client configuration only, and avoid sharing transcripts that include infrastructure identifiers or logs.
✓gcloud output can reveal project IDs, resource names, regions, IAM bindings, service accounts, logs, errors, labels, metadata, URLs, secrets references, billing context, and infrastructure topology.
Authentication state, ADC files, service account impersonation details, access tokens, project IDs, and local gcloud configuration should stay out of prompts and repository files.
Command output may be retained by the MCP client, model provider, terminal logs, shell history, and chat transcripts.
Avoid broad listing or export commands against production projects unless data handling and retention have been reviewed.
Prerequisites
Radar installed from Homebrew, Krew, GitHub Releases, Scoop, or another upstream-supported path.
Running Radar server or `kubectl radar` session with the MCP endpoint enabled.
MCP client that supports Streamable HTTP server configuration.
Kubernetes kubeconfig, context, namespace scope, or in-cluster service account reviewed before connecting an AI client.
A running Portainer instance (CE or Business Edition).
A Portainer API key: My Account → Access tokens → Add access token.
Python with `uv` installed: `pip install uv` or `brew install uv`.
Match the MCP server minor version to your Portainer instance minor version (e.g., `mcp-portainer~=2.42.0` for Portainer 2.42.x).
Running Coolify instance with API access enabled.
Coolify API access token scoped to the resources Claude may inspect or manage.
Node.js 20 or newer for the published npm server.
Review of which servers, projects, applications, databases, services, deployments, env vars, private keys, teams, cloud tokens, and scheduled tasks the token can access.
Node.js 20 or newer with npm or another compatible package runner.
Google Cloud CLI installed and authenticated.
Active gcloud account, project, and configuration scoped to the intended environment.
Least-privilege user or service account impersonation for the allowed cloud actions.