Container Host AIops

Name: Container Host AIops
Author: AIops-tools

作者：AIops-tools版本 0.14.0i★ 1i在你的电脑上运行

注册名称io.github.AIops-tools/container-host-aiops

安装方法

What it does

Governed Docker + Portainer container-host ops: reads, RCA analyses, guarded writes. 38 tools.

Connects 在你的电脑上运行
Account or key No key needed
Category 开发者工具

安装

选择你使用的 AI 应用，然后复制设置。

通过 3/3 项检查 · 在你的电脑上运行 · ✓ 命令已检查 2026年11月13日

第一步：你用哪个 AI？

Claude网页 · 应用 · 手机ChatGPT网页Claude Code终端Cursor代码编辑器VS Code代码编辑器其他其他应用

选择你用的应用，步骤会显示在下方。

可连接 Claude，但需要先准备

需要先在电脑上安装 Python (uv)（只需一次） · 仅限电脑上的 Claude 应用（Windows 或 Mac），网页和手机不可用

安装 Python (uv)
只需一次，所有需要 Python (uv) 的服务器都能用
打开 Python (uv) 下载页 ↗
已完成
打开电脑版 Claude 应用的设置文件
SettingsDeveloperEdit Config
已完成

粘贴这些设置并保存文件

{
  "mcpServers": {
    "container-host-aiops": {
      "command": "uvx",
      "args": [
        "container-host-aiops"
      ]
    }
  }
}

已完成

退出 Claude 再重新打开
已完成
试一试
用 Container Host AIops 告诉我你能帮我做什么，然后做一件给我看
✓ 如果 Claude 说正在使用 Container Host AIops，或回答中出现 Container Host AIops 的工具名称，就表示连接成功

菜单名称已于 2026年11月11日对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同

ChatGPT 暂时无法连接 Container Host AIops

Container Host AIops 需要安装在你的电脑上，而 ChatGPT 只能连接在网上运行的服务器

你可以改用这些方式

A · 使用能连接 Container Host AIops 的应用

Claude 的步骤 →需要先在电脑上安装 Python (uv)（只需一次） · 仅限电脑上的 Claude 应用（Windows 或 Mac），网页和手机不可用
Claude Code 的步骤 →需要先在电脑上安装 Python (uv)（只需一次）
Cursor 的步骤 →需要先在电脑上安装 Python (uv)（只需一次）
VS Code 的步骤 →需要先在电脑上安装 Python (uv)（只需一次）

B · 开发者工具类中能连接 ChatGPT 的其他服务器

从同类中使用较多的里挑选，仅限用地址即可连接、无需密钥的

Sentry✓ ChatGPT
Compiler Explorer✓ ChatGPT
GitDiagram✓ ChatGPT

可连接 Claude Code，但需要先准备

需要先在电脑上安装 Python (uv)（只需一次）

安装 Python (uv)
只需一次，所有需要 Python (uv) 的服务器都能用
打开 Python (uv) 下载页 ↗
已完成
打开终端
Mac：按 Command+空格，输入 Terminal · Windows：打开 PowerShell
已完成

粘贴这条命令并按 Enter

claude mcp add --scope user container-host-aiops -- uvx container-host-aiops

已完成

在 Claude Code 中输入 /mcp
应能看到 Container Host AIops 在列表中并显示已连接
已完成
试一试
用 Container Host AIops 告诉我你能帮我做什么，然后做一件给我看
✓ 如果 Claude Code 说正在使用 Container Host AIops，或回答中出现 Container Host AIops 的工具名称，就表示连接成功

菜单名称已于 2026年11月11日对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同

可连接 Cursor，但需要先准备

需要先在电脑上安装 Python (uv)（只需一次）

安装 Python (uv)
只需一次，所有需要 Python (uv) 的服务器都能用
打开 Python (uv) 下载页 ↗
已完成
点击这个按钮打开 Cursor
在 Cursor 中打开
Cursor 会请你确认安装，点击 Install
已完成
在 Cursor 中以 Agent 模式打开 AI 聊天
已完成
试一试
用 Container Host AIops 告诉我你能帮我做什么，然后做一件给我看
✓ 如果 Cursor 说正在使用 Container Host AIops，或回答中出现 Container Host AIops 的工具名称，就表示连接成功

菜单名称已于 2026年11月11日对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同

可连接 VS Code，但需要先准备

需要先在电脑上安装 Python (uv)（只需一次）

安装 Python (uv)
只需一次，所有需要 Python (uv) 的服务器都能用
打开 Python (uv) 下载页 ↗
已完成
点击这个按钮打开 VS Code
在 VS Code 中打开
VS Code 会请你确认安装，点击 Install
已完成
在 VS Code 中以 Agent 模式打开 AI 聊天
已完成
试一试
用 Container Host AIops 告诉我你能帮我做什么，然后做一件给我看
✓ 如果 VS Code 说正在使用 Container Host AIops，或回答中出现 Container Host AIops 的工具名称，就表示连接成功

菜单名称已于 2026年11月11日对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同

其他支持 MCP 的应用（如 Windsurf、Zed、Gemini CLI）添加方式各不相同，请在应用列表中查看

查看所有支持 MCP 的应用 →

JSON 设置在下方“开发者专用”中

卡住了？问问正在用 Container Host AIops 的人💬 讨论 →

开发者专用：终端命令和 JSON 设置

安装在电脑上

Claude Code

claude mcp add --scope user container-host-aiops -- uvx container-host-aiops

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "container-host-aiops": {
      "command": "uvx",
      "args": [
        "container-host-aiops"
      ]
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "container-host-aiops": {
      "command": "uvx",
      "args": [
        "container-host-aiops"
      ]
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "container-host-aiops": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "container-host-aiops"
      ]
    }
  }
}

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。

一览

GitHub 星标 i: 1
每周下载量i: 61/周
许可证 i: MIT
连接方式: 在你的电脑上运行
传输方式: stdio
最新版本i: 0.14.0
分类i: 开发者工具
发布到注册中心i: 2026年10月30日
代码最后更新i: 2026年10月30日
作者: AIops-tools
软件包: pypi: container-host-aiops
仓库: AIops-tools/Container-Host-AIops
注册状态: 活跃

哪些应用可以使用它

依据这个服务器的连接方式和各应用自己的文档，检查于 2026年11月10日。

在你的电脑上运行，适用于

Claude Goose LM Studio Msty Studio Cherry Studio Cursor VS Code + GitHub Copilot Visual Studio Windsurf (Devin Desktop)Zed Kiro Cline Continue JetBrains AI Assistant Claude Code Codex CLI Gemini CLI OpenCode nodecode Warp LibreChat Raycast

全部应用及其优缺点 →

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14日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 AIops-tools 验证，该账户拥有此仓库
✓ 通过
仍在维护
代码最后更新于15天前
✓ 通过
有许可证
使用 MIT
– 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

讨论

提问、分享技巧或报告无法使用。按“有帮助”数量排序。

还没有帖子，欢迎第一个提问或分享。

此处内容由用户撰写，不代表 PopNx 团队。

使用 Google 登录后发帖

用户评价

还没有评价

用 Google 登录后写评价

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 5876 个服务器的 69%
发布到注册中心的时间比此分类中其他 5876 个服务器的 63% 更新
基础检查结果等于或优于此分类中其他 5876 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

Container Host AIops 安全吗？

它在 3 项适用的基础检查中通过了 3 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？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

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15天前，最新版本（0.14.0）于15天前进入注册中心。

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)。

有什么可以替代它？

同一分类中有：REA, GitHub, Serena MCP: the IDE for your agent

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。

Container Host AIops

Audit availability: Real writes require a committed pending audit row and stop before contacting the platform if storage is unavailable. Reads and previews may continue with a warning; new audit rows store outcome summaries rather than full responses.

Disclaimer: Community-maintained open-source project. Not affiliated with, endorsed by, or sponsored by Docker, Inc., Portainer.io, or any container-platform vendor. "Docker", "Portainer" and all product/trademark names belong to their respective owners. MIT licensed.

Governed AI-ops for non-orchestrator container hosts — the Docker Engine API (over a local unix socket or a TCP host), Portainer (its management API, which also proxies Docker), and Podman (a rootful/rootless service socket speaking the Docker-compatible API plus libpod-native endpoints) — with a built-in governance harness: a unified audit log over both MCP and CLI, a runaway/budget safety guard, and undo-token recording. It records every operation; it does not decide whether a write is permitted — that is the agent's or the account's call. Multi-platform by construction: a registry keyed by platform means a per-target platform field (docker / portainer / podman) selects the API shape, and another host family could be added later without touching the ops/CLI/MCP layers. Exercised against a live Docker Engine 27.5.1 daemon (doctor, overview, the three flagship analyses, and a governed stop_container with audit + undo recorded); the Portainer and Podman API paths are covered by the mock suite only.

What it does

Three flagship signature analyses, plus the guarded reads and writes around them:

Restart-loop RCA — inspect containers for restart count + exit code, flag the crash-looping ones (restartCount over threshold, or restarting/dead, or a non-zero exit), and map each to a likely cause + action from the exit code (137 OOM/SIGKILL, 143 SIGTERM, 139 segfault, 127 bad entrypoint, …), with a tail of logs. Every ranking carries its numbers, not a black-box verdict.
Resource-pressure analysis — a one-shot CPU%/memory% sample per running container vs its configured limits, flagging each "near" (≥ 80% of a threshold) or "over", with a recommendation (raise a limit, set a missing memory limit, scale out).
Image & volume bloat — dangling images + dangling volumes + build cache from system/df, totalled into prune candidates with reclaimable bytes.

What works

CLI (container-host-aiops ...): init, overview, container, image, volume, network, system, stack, pod, analyze, manage, secret, doctor, mcp.
MCP server (container-host-aiops mcp or container-host-aiops-mcp): 38 tools (29 read, 9 write), every one wrapped with the bundled @governed_tool harness.
Connection layer: Docker over a unix socket (httpx.HTTPTransport(uds=...)) or a TCP host; Portainer over HTTPS with an X-API-Key token that also proxies the Docker API of a managed endpoint; Podman over its rootful/rootless service socket (autodetected: $XDG_RUNTIME_DIR/podman/podman.sock first, then /run/podman/podman.sock) speaking the Docker-compat layer (paths reused wholesale) plus libpod-native endpoints. A local Docker/Podman socket needs no secret — the socket's file permissions are the boundary.
Encrypted credentials: the Portainer API token lives in an encrypted store ~/.container-host-aiops/secrets.enc (Fernet + scrypt) — never plaintext on disk. Unlock with a master password from CONTAINER_HOST_AIOPS_MASTER_PASSWORD (MCP/CI) or an interactive prompt (CLI).
Reversibility: mutating writes fetch the real before-state first and record a faithful inverse (stop↔start; update_container restores prior CPU/memory limits). Irreversible ops (remove_container, prune_images, prune_volumes, recreate_stack) capture the before-state for audit but declare no undo.
Safety: every state-changing CLI op supports --dry-run and requires double confirmation; every write MCP tool takes a dry_run preview — and prune previews list what would be removed + reclaimable bytes before doing it.

What this tool does, and does not, decide

It delivers container-host operations — reads and writes — accurately and efficiently, and records every one of them. It does not decide whether a write is allowed to happen. That is the agent's judgement, or the permission of the account you connect it with: point it at a Docker socket mounted read-only, or a Portainer account without write scope, and the writes fail at the server — the place that actually owns the permission.

So there is no read-only switch, no policy file, no approval gate to configure. The one thing the tool guarantees is that nothing is silent: every call, over MCP and over the CLI alike, lands an audit row in ~/.container-host-aiops/audit.db, and destructive writes still capture their before-state and record an inverse where one exists.

Each tool declares a risk_level, kept in agreement with its [READ]/[WRITE]

documentation tag by a test, and carried into the audit row as a descriptive

tier — so a reviewer can see at a glance that a row was a high-risk delete. It

is a label, not a gate.

Capability matrix (38 MCP tools)

Domain	Tools	Count	R/W
Overview	`overview`	1	read
Containers	`list_containers`, `inspect_container`, `container_logs`, `container_stats`, `container_top`, `container_restart_summary`	6	read
Images	`list_images`, `inspect_image`, `dangling_images`, `image_disk_usage`	4	read
Volumes	`list_volumes`, `inspect_volume`, `dangling_volumes`	3	read
Networks	`list_networks`, `inspect_network`	2	read
System	`system_info`, `system_version`, `system_df`, `system_events`	4	read
Stacks	`list_endpoints`, `list_stacks`, `stack_detail` (Portainer), `list_compose_stacks` (docker+podman)	4	read
Pods (Podman)	`list_pods`	1	read
Analyses (flagship)	`restart_loop_rca`, `resource_pressure_analysis`, `image_and_volume_bloat`	3	read
Writes	`remove_container`, `prune_images`, `prune_volumes`, `recreate_stack`	4	write (high)
	`restart_container`, `stop_container`, `start_container`, `update_container`	4	write (medium)

The three analyses accept injected data for offline analysis, or pull live from a configured target. list_endpoints/list_stacks/stack_detail require a portainer target; list_compose_stacks (Compose project rollup by the com.docker.compose.project label, with per-stack health) works on docker or podman; list_pods requires a podman target (Docker/Portainer have no pod concept).

Platform support matrix

Capability	docker	portainer	podman
Container / image / volume / network / system reads	✅	✅ (proxied)	✅ (compat)
Flagship analyses (restart-loop RCA, resource pressure, bloat)	✅	✅	✅ (compat)
Lifecycle + prune writes (stop/start/restart/remove/update/prune)	✅	✅	✅ (compat)
Compose-stack rollup (`list_compose_stacks`)	✅	✅ (proxied)	✅
Portainer endpoints / stacks / `recreate_stack`	—	✅	—
Podman pods (`list_pods`, libpod)	—	—	✅

Quick start

As a Claude Code plugin

One install gives an agent both the skill and the MCP server:

/plugin marketplace add AIops-tools/marketplace

/plugin install container-host-aiops@aiops-tools

The MCP server is fetched with uv and pinned to the package version this plugin declares, so an audit row can be traced back to the code that wrote it. Credentials are still configured with container-host-aiops init — see below.

As an OpenClaw plugin

The same bundle is published on ClawHub, where one install delivers the skill and its MCP server together:

openclaw plugins install clawhub:@zw008/container-host-aiops

openclaw skills info container-host-aiops # expect: Visible to model: yes

Restart the OpenClaw gateway afterwards so it loads the plugin. The MCP server is fetched with uv, pinned to this exact release, so uvx has to be on PATH — without it the skill still installs but reports Visible to model: no. Credentials are configured exactly as below.

As a CLI or standalone MCP server

uv tool install container-host-aiops          # or: pipx install container-host-aiops

container-host-aiops init # wizard: add a Docker/Podman socket or Portainer target container-host-aiops doctor # verify config, secrets, connectivity container-host-aiops overview # one-shot host health container-host-aiops analyze restart-loop # crash-looping containers + cause/action container-host-aiops container list --running # running containers

Run as an MCP server (stdio):

export CONTAINER_HOST_AIOPS_MASTER_PASSWORD=...   # only needed for Portainer targets

container-host-aiops-mcp

Where that password then lives: an exported variable is readable by

every process this shell starts and is recorded by shell history. On a

shared or long-lived host, prefer the interactive prompt, or inject it from

a secret manager for the life of the one command that needs it.

Governance

Every operation — MCP and CLI — passes through the bundled @governed_tool harness. It records; it does not authorize (see above).

Audit — every call (params, outcome summary, status, duration, risk tier, and any operator-supplied approver/rationale) is logged to ~/.container-host-aiops/audit.db (relocatable via CONTAINER_HOST_AIOPS_HOME). The CLI writes the same row the MCP path does — there is no unaudited entry point.
Runaway guard — a safety backstop, not an authorization gate: the same call hammered in a tight loop trips a circuit breaker so a stuck agent can't burn unbounded calls/time. Disable with CONTAINER_HOST_RUNAWAY_MAX=0; optional hard ceilings via CONTAINER_HOST_MAX_TOOL_CALLS / CONTAINER_HOST_MAX_TOOL_SECONDS.
Undo recording — reversible writes record an inverse descriptor built from the fetched before-state.
Risk tier — a descriptive label on the audit row derived from risk_level; it gates nothing.

Scope

This is the container-host member of the AIops-tools family (governed AI-ops with audit + budget + undo), for single-host Docker / Portainer / Podman. It is deliberately NOT for a cluster orchestrator, a hypervisor, a storage appliance, a backup product, or OT / industrial edge — those are separate tools/lines.

Missing a capability?

Coverage is intentionally a curated subset of the Docker Engine + Portainer + Podman (libpod) APIs. Missing a call, or want another container host family? Open an issue or PR — contributions welcome.

Verification status

Docker — exercised against a live Docker Engine 27.5.1 daemon: doctor and overview connected over the local socket, the restart-loop and image/volume bloat analyses were run against real crash-looping containers and real reclaimable data, and a governed stop_container wrote a row to the audit DB and recorded a working undo descriptor.
Portainer and Podman — mock-validated only. Those API paths are modelled from each project's public API shape and have not been exercised against a live server.
The full checklist — what the mock suite guarantees, what the Docker run already satisfied, and what a Portainer/Podman run still has to prove — is in docs/VERIFICATION.md. container-host-aiops doctor is the fastest live check on any platfor

只显示了开头部分；其余内容请在仓库中阅读

Container Host AIops

What it does

安装

安装 Python (uv)

打开电脑版 Claude 应用的设置文件

粘贴这些设置并保存文件

退出 Claude 再重新打开

试一试

你可以改用这些方式

安装 Python (uv)

打开终端

粘贴这条命令并按 Enter

在 Claude Code 中输入 /mcp

试一试

安装 Python (uv)

点击这个按钮打开 Cursor

在 Cursor 中以 Agent 模式打开 AI 聊天

试一试

安装 Python (uv)

点击这个按钮打开 VS Code

在 VS Code 中以 Agent 模式打开 AI 聊天

试一试

安装在电脑上

一览

哪些应用可以使用它

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

讨论

用户评价

与“开发者工具”中的其他服务器比较i

常见问题

同类别的其他选择

REA

GitHub

Serena MCP: the IDE for your agent

作者的 README

Container Host AIops

What it does

What works

What this tool does, and does not, decide

Capability matrix (38 MCP tools)

Platform support matrix

Quick start

As a Claude Code plugin

As an OpenClaw plugin

As a CLI or standalone MCP server

Governance

Scope

Missing a capability?

Verification status