原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Mandala Computer

作者:mandalacomputer版本 0.22.0i★ –i电脑或云端
注册名称io.github.mandalacomputer/mcp

安装

概览

作者提供的说明(英文原文)

Cloud desktops for AI agents: create a Linux computer, see its screen, click, type and run tasks.

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。

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: 电脑或云端
  • 传输方式: stdio, Streamable HTTP
  • 分类: 其他
  • 你需要填写:MANDALA_API_KEY, MANDALA_MODEL_KEY

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:3 项中通过 3 项
  • 在作者的服务器上运行:你向它提出的内容会经过他们的服务器。
  • 需要你自己填写: MANDALA_API_KEY, MANDALA_MODEL_KEY, MANDALA_PROFILE, MANDALA_BASE_URL, MANDALA_COMPUTER_ID, MANDALA_NO_LIFECYCLE, MANDALA_READ_ONLY, MANDALA_TAGS

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • 在云端运行无需安装任何东西,通过互联网连接
  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

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 …”。

适用于网页版、桌面版和手机版 Claude:

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址,然后点击 Add
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://app.mandala.computer/mcp

或者在你的电脑上运行

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "mandalacomputer": {
      "command": "npx",
      "args": [
        "-y",
        "mandala-computer-mcp"
      ],
      "env": {
        "MANDALA_API_KEY": "your-value",
        "MANDALA_MODEL_KEY": "your-value",
        "MANDALA_PROFILE": "your-value",
        "MANDALA_BASE_URL": "your-value",
        "MANDALA_COMPUTER_ID": "your-value",
        "MANDALA_NO_LIFECYCLE": "your-value",
        "MANDALA_READ_ONLY": "your-value",
        "MANDALA_TAGS": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add --transport http mandalacomputer https://app.mandala.computer/mcp

或者在你的电脑上运行

claude mcp add mandalacomputer --env MANDALA_API_KEY=your-value --env MANDALA_MODEL_KEY=your-value --env MANDALA_PROFILE=your-value --env MANDALA_BASE_URL=your-value --env MANDALA_COMPUTER_ID=your-value --env MANDALA_NO_LIFECYCLE=your-value --env MANDALA_READ_ONLY=your-value --env MANDALA_TAGS=your-value -- npx -y mandala-computer-mcp

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": {
    "mandalacomputer": {
      "url": "https://app.mandala.computer/mcp"
    }
  }
}

或者在你的电脑上运行

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": {
    "mandalacomputer": {
      "command": "npx",
      "args": [
        "-y",
        "mandala-computer-mcp"
      ],
      "env": {
        "MANDALA_API_KEY": "your-value",
        "MANDALA_MODEL_KEY": "your-value",
        "MANDALA_PROFILE": "your-value",
        "MANDALA_BASE_URL": "your-value",
        "MANDALA_COMPUTER_ID": "your-value",
        "MANDALA_NO_LIFECYCLE": "your-value",
        "MANDALA_READ_ONLY": "your-value",
        "MANDALA_TAGS": "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": {
    "mandalacomputer": {
      "type": "http",
      "url": "https://app.mandala.computer/mcp"
    }
  }
}

或者在你的电脑上运行

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": {
    "mandalacomputer": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "mandala-computer-mcp"
      ],
      "env": {
        "MANDALA_API_KEY": "your-value",
        "MANDALA_MODEL_KEY": "your-value",
        "MANDALA_PROFILE": "your-value",
        "MANDALA_BASE_URL": "your-value",
        "MANDALA_COMPUTER_ID": "your-value",
        "MANDALA_NO_LIFECYCLE": "your-value",
        "MANDALA_READ_ONLY": "your-value",
        "MANDALA_TAGS": "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。

ChatGPT 只能连接互联网上的服务器:

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add(部分套餐需要先在 Advanced 中开启 Developer mode)
  3. 粘贴下面的地址,起个名字,然后保存
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://app.mandala.computer/mcp

菜单名称可能因套餐和版本不同而略有差异。

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

  • MANDALA_API_KEY 机密 env
    API key from Settings → Credentials → API keys. Optional after `mandala login`, which saves one.
  • MANDALA_MODEL_KEY 机密 env
    Optional Anthropic or OpenAI key. Enables run_agent_chat, and makes run_agent and start_agent_run bill this key instead of the account's AI credits.
  • MANDALA_PROFILE env
    Optional saved profile from `mandala login --profile`; used when MANDALA_API_KEY is not set.
  • MANDALA_BASE_URL env
    Optional API base URL. Defaults to https://app.mandala.computer/api/v1.
  • MANDALA_COMPUTER_ID env
    Optional computer id to bind at startup, so use_computer is not needed.
  • MANDALA_NO_LIFECYCLE env
    Optional: 1, true, yes or on withholds the tools that create, clone or delete computers and snapshots.
  • MANDALA_READ_ONLY env
    Optional: 1, true, yes or on keeps only read-only tools.
  • MANDALA_TAGS env
    Optional comma-separated tool tags to expose (for example computers,input); empty means every tool.

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

服务器要求先登录,才会列出它的工具。

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7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 mandalacomputer 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

  • ✓ 通过

    端点有响应

    在 648 毫秒内响应(HTTP 401)

  • ✓ 通过

    使用 OAuth 登录

    声明支持 OAuth 登录,你的 AI 应用可以带你去登录

  • … 尚未检查

    工具描述中没有隐藏指令

    尚未检查;我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32678 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32678 个服务器的 86% 更新
  • 基础检查结果等于或优于此分类中其他 32678 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

Mandala Computer 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?

你需要填写:MANDALA_API_KEY, MANDALA_MODEL_KEY

它在我的电脑上运行还是在云端?

两种方式都可以:安装在你的电脑上,或者连接云端版本。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.22.0)于5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。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Starreel

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

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211前天更新
安装

Starreel

作者:starreel

Turn a script into a finished, downloadable short-drama episode — via MCP or REST.

在你的电脑上运行今天更新
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。

mandala-computer-mcp

An MCP server for Mandala Computer — cloud desktops for AI agents.

Point Claude Code, Claude Desktop, or anything else that speaks MCP at a real Linux desktop it can see and drive. Screenshots come back as images, so the model looks at the screen and clicks what it sees.

Status: alpha. The tool surface is settling; expect breaking changes

before 1.0. Tracks the platform's /api/v1, which is itself still moving.

Install

MCP requires Node 20.3 or newer. The TypeScript CLI login setup requires Node 22 or newer (mandala-computer on npm). Sign in once, then add the MCP server:

npm install -g mandala-computer

mandala login claude mcp add mandala -- npx -y mandala-computer-mcp

On Node 20, MCP can use an existing saved profile or an environment key. The TypeScript CLI guides you through browser approval and saves an API key in ~/.mandala/credentials.json. MCP only reads that file; it never issues or approves a device, writes credentials, or starts login automatically. The Python distribution also provides a mandala command; use the TypeScript CLI for login.

To select a separate profile and workspace:

mandala login --profile Work --workspace Research

claude mcp add mandala -- npx -y mandala-computer-mcp --profile Work

--profile takes precedence over MANDALA_PROFILE, then the saved default. Names are case-sensitive. An explicit key or nonempty MANDALA_API_KEY takes precedence over every profile and avoids accessing the credential store. Empty explicit keys fail; an empty or whitespace-only environment key is absent. You can still use a key from Settings → Credentials → API keys with MANDALA_API_KEY.

Saved credentials require POSIX protection: a real directory owned by you with mode 0700, and an owned regular file with mode 0600 and one link. Symlinks, hardlinks, unsafe permissions, malformed files and unsupported protection are refused before any API request. Windows file loading is unsupported; explicit or environment keys remain available. Only your home directory's store is read.

A saved profile also binds its API base URL. --base-url or MANDALA_BASE_URL must match that stored base after canonicalization, including the entire path prefix and port. An explicitly empty base flag is invalid with saved credentials. Each stdio session resolves once: restart to pick up another saved key. Revoking the key in Settings → Credentials → API keys makes later calls fail; MCP does not switch profiles, reread the file, or retry a refused action. Run login explicitly when new credentials are needed.

A saved profile can also carry a default workspace, set with mandala workspaces use (and removed with --clear); the CLI keeps it in ~/.mandala/defaults.json, beside credentials.json, which it never changes. Started from that profile, the secret tools (list_secrets, get_secret, create_secret, set_secret, replace_secret, delete_secret), create_computer and list_computers use it when a call leaves out workspace_id, as mandala computers create and computers list do; an explicit workspace_id always wins. Their descriptions say so and name the workspace, list_secrets, list_computers and create_computer say when the default chose the workspace, and startup prints it on stderr. There is no "every workspace" value: to list elsewhere, pass another workspace_id (or 'unassigned'), or start the server from a profile with no default. MCP only reads that file, once at startup, with the same checks as credentials.json. It is not applied when the profile's key is confined to a workspace (that key's own scope applies), when the default was saved for another account than the profile is logged in to now, or with an explicit or environment key. A file that cannot be read (not valid JSON, another version, or readable by others) is noted on stderr: list_secrets, get_secret and list_computers then use account-wide, but create_secret, set_secret, replace_secret, delete_secret and create_computer refuse a call without workspace_id, sending nothing, rather than act account-wide; pass workspace_id, or fix or delete the file and restart the server. The hosted HTTP server never reads it, and its tools are unchanged.

Claude Code — as a plugin, which installs the server and a skill together:

# Run mandala login first, or export MANDALA_API_KEY in this shell.

/plugin marketplace add mandalacomputer/mcp /plugin install mandala-computer@mandala

The skill — plugin/skills/mandala-computer/SKILL.md — is the part the tools cannot say for themselves: when a cloud desktop is the right answer at all, that it consumes running capacity until suspended or stopped, that run_agent is usually the right level and a screenshot per click is not, and which refusals are worth a second try. It is a description of *when and how*, not a second client; once the server is installed it stays out of the way. MANDALA_MODEL_KEY, if exported alongside, is passed through and turns on run_agent when the configured filters permit it. Without it, run_agent runs on the account's AI credits when they can pay (see Agents on AI credits).

To use an environment key instead of a saved profile:

claude mcp add mandala -e MANDALA_API_KEY=com_… -- npx -y mandala-computer-mcp

Claude Desktop — in claude_desktop_config.json:

{

"mcpServers": { "mandala": { "command": "npx", "args": ["-y", "mandala-computer-mcp"] } } }

Cursor, Windsurf and the rest take the same three fields — command, args, env — in whichever file they keep their MCP servers in.

Your MCP client starts this as a subprocess. It uses the saved profile’s API base, or https://app.mandala.computer/api/v1 by default with an environment key. Programmatic local hosts can call runStdio({ profile: 'Work' }); the exported StdioConfig allows a local key or profile. createServer still requires an explicit API key, and HttpConfig has no local credential options.

Use

Ask for what you want. A first session usually goes:

Create a computer from the base template, open example.com, and show me what's

on the screen.

Under that, the model is doing roughly this:

create_computer(template="base")     → builds it and selects it for the session

wait_for_computer(until="guest") → the desktop inside is answering, not just the VM open_url(url="https://example.com") → puts the page on the screen screenshot() → an image the model can point at click(x=640, y=400) → clicks what it saw screenshot() → looks again

create_computer(size="large") is the fast path: the named shapes from list_sizes are the ones the platform keeps pre-booted, so one of those is usually answered in about a second where a custom cpu/ram_mb/disk_gb shape boots cold. A size sets the template and the numbers together, so send it alone or the explicit fields alone.

use_computer binds a machine to the session, so every later call can leave computer_id out. Pass computer_id explicitly on any call to override it without changing the binding, which is how you drive two machines at once.

Set MANDALA_COMPUTER_ID to bind one at startup and skip use_computer entirely.

The tools

This is the unfiltered inventory. The filters below can withhold tools from both listing and calling; workflows in this README apply only when the needed tools are available. The offline fixture checks exercised operation coverage; verification against the live publication is a separate required CI gate. Parameter and response-mode support remains a separate contract.

Choosing a machine — list_templates, list_sizes, list_computers, get_computer, use_computer, wait_for_computer, get_desktop_url

Lifecycle — create_computer, start_computer, stop_computer, suspend_computer, restart_computer, update_computer, clone_computer, delete_computer, move_computer_to_workspace, move_computer, list_moves, get_operation, list_operations, wait_for_operation — see Lifecycle operations

move_computer_to_workspace changes workspace membership and who can access a computer. It requires an account-wide member or owner credential and explicit computer_id and workspace_id arguments (null means unassigned). The selected computer and default workspace are not used. The computer must be stopped, with no saved session, snapshots or snapshot history copies, secret bindings, or proxy credentials. Refusals leave those resources intact; the tool never removes them to force a move. This is separate from move_computer, which relocates a computer to different hardware.

Driving the desktop — screenshot, click, type_text, paste_text, press_key, scroll, drag, move_mouse, mouse_button, cursor_position, wait

Every one of these that acts — all but screenshot and cursor_position — takes context: true and then answers the desktop as it stands just after the action: the windows list_windows would list and the one with focus, without a second call. They are read once, straight after the action, so a window still opening may not be listed yet; when they cannot be read the action still happened, and the answer says why. When the focused window is Chromium the answer also carries page: the page's URL and title and one line per interactive element visible in it — what it is, what it says (and its accessible name when that is something else), and the point to click. Firefox, the default browser, has no page; then page_not_read gives the platform's reason beside the windows.

Inside the guest — exec, exec_poll, exec_kill, get_execution, read_execution_output, open_url, list_windows, window_action, read_clipboard, write_clipboard, read_file, write_file, write_file_chunk, get_file_upload, cancel_file_upload, list_directory

write_file writes straight to the path by default: a file already there is truncated as the upload starts, so a failed or cut-off write can leave only part of the new contents, with the old ones gone. With overwrite: false it creates the file only if nothing is there, and a path that is taken is refused without that attempt writing anything (Linux computers only). Those contents are written beside the path and published only once complete, so incomplete contents never appear at it, but a failure while publishing or answering can leave the complete file there. After any error read the path before retrying or overwriting. When a partial file must never appear at the path, use overwrite: false or write_file_chunk. read_file and write_file take no_wake: true to refuse (409) rather than resume a computer that is not running. For embedders, the Api raises that refusal as FileExistsError, and a create-only 409 whose reason could not be read as CreateOnlyConflictError, which says nothing about the path; isTransient is false for both.

Retained versions — retain_execution_output, get_result, read_result_output, delete_result, publish_artifact, get_artifact, read_artifact, delete_artifact

Passive metadata — list_activities, get_activity, get_activity_results, read_signals

Being told rather than asking — wait_for_event, poll_events, wait_for_file_change

Snapshots — list_snapshots, snapshot_holdings, create_snapshot, restore_snapshot, clone_snapshot, snapshot_schedule, get_retention, delete_snapshot

Your own templates — get_template_schema, check_template, publish_template, get_template, retire_template

Building one — build_template, list_builds, `get_bu

只显示了开头部分;其余内容请在仓库中阅读

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