Ocm MCP Server

Name: Ocm MCP Server
Author: ocm-mcp-server

by ocm-mcp-serverversion 0.6.0i★ –iRuns on your computer

Registry nameio.github.ocm-mcp-server/ocm-mcp-server

Install

Overview

Description from the maker (original English)

Guardrailed fleet ops for AI agents: multi-cluster Kubernetes via OCM with policy, approval, audit.

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: stdio
Category: Developer tools
You need to fill in: KUBECONFIG

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).
You fill in yourself: KUBECONFIG, OCM_MCP_HUB_CONTEXT, OCM_MCP_READ_ONLY

You need

PyPIPython’s uv installed (for the uvx command)

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": {
    "ocm": {
      "command": "uvx",
      "args": [
        "ocm-mcp-server"
      ],
      "env": {
        "KUBECONFIG": "your-value",
        "OCM_MCP_HUB_CONTEXT": "your-value",
        "OCM_MCP_READ_ONLY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add ocm --env KUBECONFIG=your-value --env OCM_MCP_HUB_CONTEXT=your-value --env OCM_MCP_READ_ONLY=your-value -- uvx ocm-mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ocm": {
      "command": "uvx",
      "args": [
        "ocm-mcp-server"
      ],
      "env": {
        "KUBECONFIG": "your-value",
        "OCM_MCP_HUB_CONTEXT": "your-value",
        "OCM_MCP_READ_ONLY": "your-value"
      }
    }
  }
}

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": {
    "ocm": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ocm-mcp-server"
      ],
      "env": {
        "KUBECONFIG": "your-value",
        "OCM_MCP_HUB_CONTEXT": "your-value",
        "OCM_MCP_READ_ONLY": "your-value"
      }
    }
  }
}

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.

Values you fill in

KUBECONFIG required env
Path to the kubeconfig with the OCM hub context (and optional spoke contexts).
OCM_MCP_HUB_CONTEXT env
Kubeconfig context name of the OCM hub cluster (defaults to the current context).
OCM_MCP_READ_ONLY env
Set to 'true' to disable the gated write path entirely (read-only fleet access).

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account ocm-mcp-server, 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 1391 servers in this category
Published to the registry more recently than 46% of the other 1391 servers in this category
Basic checks equal to or better than 100% of the other 1391 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 Ocm 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?

You need to fill in: KUBECONFIG

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 (0.6.0) reached the registry 2 months ago.

Which apps can use it?

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

What can I use instead?

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

Alternatives

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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.

ocm-mcp-server

[📖 Read the docs site → ocm-mcp-server.github.io](https://ocm-mcp-server.github.io/)

Star us ❤️ →

AgentOps for Kubernetes fleets, done safely.

An MCP server that lets AI agents operate a multi-cluster Kubernetes fleet through an [Open Cluster Management](https://open-cluster-management.io/) hub, with policy, approval, and audit between the model and your clusters.

*The agent never holds a kubeconfig. Every write is policy-checked, human-approved, and traced.*

[📦 Get it](#where-to-get-it-and-how-its-vetted) · [✨ Why](#why-this-exists) · [🔌 Connect your agent](#connect-your-agent---any-mcp-client-works) · [🧭 Architecture](#architecture) · [🧰 Toolsets](#toolsets) · [🛠️ Tools](#tools) · [💬 Prompts](#prompts) · [🔭 Observability](#observability---audit-tracing-opentelemetryjaeger-metrics) · [🚀 Quickstart](#quickstart-laptop-15-minutes) · [📖 Wiki](https://github.com/ocm-mcp-server/ocm-mcp-server/wiki) · [📚 Docs](#documentation)

The whole safe-remediation loop: investigate with free reads, propose a change, get rejected by the guardrails and correct it, wait for a human-signed token, apply, verify, and report from the audit log.

Where to get it, and how it's vetted

📦 [PyPI - `ocm-mcp-server`](https://pypi.org/project/ocm-mcp-server/) - pip install ocm-mcp-server (or run directly with uvx ocm-mcp-server). Every release is published straight from CI via OIDC trusted publishing - no long-lived tokens anywhere.
🗂️ [Official MCP Registry](https://registry.modelcontextprotocol.io/?q=ocm-mcp-server) - listed as io.github.ocm-mcp-server/ocm-mcp-server, so any MCP client or platform that browses the registry can discover and auto-configure this server (package, transport, and required env vars are all in the listing); the registry validates the listing against this repo and the PyPI package.
🐳 [Container image on GHCR](https://github.com/ocm-mcp-server/ocm-mcp-server/pkgs/container/ocm-mcp-server) - docker run ghcr.io/ocm-mcp-server/ocm-mcp-server (kubeconfig mount shown in the deployment guide); built in CI with an SBOM and SLSA provenance attached, vulnerability-gated with Trivy, and signed keyless with Cosign so you can verify what you run.
🛡️ [OpenSSF Scorecard](https://scorecard.dev/viewer/?uri=github.com/ocm-mcp-server/ocm-mcp-server) - the repo's supply-chain security posture (pinned dependencies, branch protection, signed releases, ...) is scored automatically every week and published for anyone to inspect.

Why this exists

Your team runs many Kubernetes clusters. Sooner or later somebody asks the question: can an AI agent take the 2 a.m. page?

The quickest way to find out is to hand a model kubectl with cluster-admin and watch. In production that experiment ends badly, for three separate reasons:

The model is non-deterministic. The same alert can produce a careful diagnosis one run and a kubectl delete the next.
The credentials are real. There is no dry run between the model's decision and your production cluster.
There is no record. When something breaks, you cannot reconstruct what the agent did, in what order, or on whose authority.

This project starts from a different observation: fleets already have a control point that humans trust every day, the multi-cluster hub. Open Cluster Management (a CNCF project) gives every fleet an inventory (ManagedCluster), a scheduler (Placement), and a delivery channel (ManifestWork). ocm-mcp-server exposes that hub to agents as a small set of typed MCP tools, and puts four independent layers between the model and your clusters:

#	Layer	Enforced by	What it stops
1	Static checks	this server, before anything else	privileged pods, host access, system namespaces, unpinned images, disallowed kinds
2	Policy admission	Kyverno dry-run on the hub	anything your org's policies reject, evaluated inside the `ManifestWork` envelope
3	Human approval	Ed25519 token signed by `ocm-mcp approve` on a trusted terminal; the server needs only the public verifier key	any change reaching a cluster without a person consenting to that exact content and operation (one-time token, bound to content + operation + issuer/audience + expiry)
4	Least-privilege RBAC	Kubernetes	everything else; no Secrets, no exec, no deletes outside its own ManifestWorks

None of these layers live in the system prompt, so none of them can be talked out of.

A write is two calls with a person between them. The token is bound to one content hash and one operation, it expires on its own, and offered a second time it is refused.

Two writes, the same four gates. The privileged, unpinned one dies at Layer 1 and never reaches a cluster; the compliant one waits for a person to sign the exact content, then lands and is verified.

A fleet operator's day with Claude , live from a cold start: install from PyPI, claude mcp add , inventory the fleet, reason about placement — then ship a new service the gated way: the privileged :latest shortcut is refused , the pinned proposal is signed by a human , applied with the token, verified, and the whole day is read back from the audit trail . — narrated MP4 · terminal cast .

The same day, driven by four different agents. Identical ten chapters, identical server - only the agent asking changes, which is the whole point of speaking MCP rather than shipping a client. Codex · Gemini (Antigravity CLI) . Re-record any

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

Ocm MCP Server

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

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

ocm-mcp-server

[📖 Read the docs site → ocm-mcp-server.github.io](https://ocm-mcp-server.github.io/)

AgentOps for Kubernetes fleets, done safely.

Where to get it, and how it's vetted

Why this exists