Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
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.
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。
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。
选择你使用的 AI 应用,然后复制设置。
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
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:
https://app.mandala.computer/mcp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/mcpclaude 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。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"mandalacomputer": {
"url": "https://app.mandala.computer/mcp"
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"mandalacomputer": {
"type": "http",
"url": "https://app.mandala.computer/mcp"
}
}
}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:
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。
MANDALA_PROFILE envMANDALA_BASE_URL envMANDALA_COMPUTER_ID envMANDALA_NO_LIFECYCLE envMANDALA_READ_ONLY envMANDALA_TAGS env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。
服务器要求先登录,才会列出它的工具。
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
注册名称已通过 GitHub 账户 mandalacomputer 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
在 648 毫秒内响应(HTTP 401)
声明支持 OAuth 登录,你的 AI 应用可以带你去登录
尚未检查;我们会在检查端点时读取工具列表
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。
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。
你需要填写:MANDALA_API_KEY, MANDALA_MODEL_KEY
两种方式都可以:安装在你的电脑上,或者连接云端版本。
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。
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。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Starreel
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Turn a script into a finished, downloadable short-drama episode — via MCP or REST.
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。
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.
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.
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.
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