Ocm MCP Server

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

作者：ocm-mcp-server版本 0.6.0i★ –i在你的电脑上运行

注册名称io.github.ocm-mcp-server/ocm-mcp-server

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 开发者工具
你需要填写：KUBECONFIG

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： KUBECONFIG, OCM_MCP_HUB_CONTEXT, OCM_MCP_READ_ONLY

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 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"
      }
    }
  }
}

在终端中运行这条命令

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

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.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"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "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"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

KUBECONFIG 必填 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).

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 ocm-mcp-server 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“开发者工具”中的其他服务器比较i

星标数多于此分类中其他 1390 个服务器的 0%
发布到注册中心的时间比此分类中其他 1390 个服务器的 46% 更新
基础检查结果等于或优于此分类中其他 1390 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Ocm MCP Server 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：KUBECONFIG

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.6.0）于2个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv), Docker。

有什么可以替代它？

同一分类中有：GitHub, Desktop Commander, Cloudflare MCP

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

GitHub

作者：github

Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.

电脑或云端★ 34,245MIT3天前更新

安装

Desktop Commander

作者：wonderwhy-er

MCP server for terminal commands, file operations, and process management

在你的电脑上运行★ 10,225MIT15天前更新

安装

Cloudflare MCP

作者：cloudflare

Cloudflare MCP servers

在云端运行★ 4,446Apache-2.014个月前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

只显示了开头部分；其余内容请在仓库中阅读