Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Cloud desktops for AI agents: create a Linux computer, see its screen, click, type and run tasks.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
MANDALA_API_KEY, MANDALA_MODEL_KEY, MANDALA_PROFILE, MANDALA_BASE_URL, MANDALA_COMPUTER_ID, MANDALA_NO_LIFECYCLE, MANDALA_READ_ONLY, MANDALA_TAGS비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://app.mandala.computer/mcpClaude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"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"
}
}
}
}터미널에서 이 명령어를 실행하세요
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-mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"mandalacomputer": {
"url": "https://app.mandala.computer/mcp"
}
}
}프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"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"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"mandalacomputer": {
"type": "http",
"url": "https://app.mandala.computer/mcp"
}
}
}프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"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"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://app.mandala.computer/mcp메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
MANDALA_PROFILE envMANDALA_BASE_URL envMANDALA_COMPUTER_ID envMANDALA_NO_LIFECYCLE envMANDALA_READ_ONLY envMANDALA_TAGS env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
서버가 도구 목록을 보여 주기 전에 로그인을 요구해요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 mandalacomputer 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
648ms 만에 응답(HTTP 401)
OAuth 로그인을 지원한다고 밝혀서, AI 앱이 로그인 화면으로 안내해 줘요
아직 점검하지 않았어요. 엔드포인트를 점검할 때 도구 목록을 읽어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 3개 중 3개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: MANDALA_API_KEY, MANDALA_MODEL_KEY
둘 다 가능해요. 내 컴퓨터에 설치해도 되고, 클라우드 버전에 연결해도 돼요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.22.0)은 5일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Reelsaw
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
Drive a self-hosted Reelsaw: turn long videos into vertical clips, edit, re-render and export them.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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
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