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PopNx MCPMCP servers for people who use AI · explained in plain words

Mandala Computer

by mandalacomputerversion 0.22.0i★ –iComputer or cloud
Registry nameio.github.mandalacomputer/mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Computer or cloud
  • Transport: stdio, Streamable HTTP
  • Category: Other
  • You need to fill in: MANDALA_API_KEY, MANDALA_MODEL_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 3 of 3 passed
  • Runs on the maker’s server: what you ask it goes through their server.
  • You fill in yourself: MANDALA_API_KEY, MANDALA_MODEL_KEY, MANDALA_PROFILE, MANDALA_BASE_URL, MANDALA_COMPUTER_ID, MANDALA_NO_LIFECYCLE, MANDALA_READ_ONLY, MANDALA_TAGS

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • Runs in the cloudNothing to install; it connects over the internet
  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Works in Claude on the web, desktop and mobile:

  1. Open Settings → Connectors
  2. Press Add custom connector
  3. Paste the address below and press Add
  4. If it asks you to sign in, sign in with the maker
SettingsConnectors1+ Add2https://…3
https://app.mandala.computer/mcp

Or run it on your computer

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
      }
    }
  }
}

Run this in your terminal

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

Or run it on your computer

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Or run it on your computer

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "mandalacomputer": {
      "type": "http",
      "url": "https://app.mandala.computer/mcp"
    }
  }
}

Or run it on your computer

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

ChatGPT connects only to servers on the internet:

  1. Open ChatGPT on the web → Settings → Connectors (Apps)
  2. Press Create or Add (some plans need Developer mode under Advanced first)
  3. Paste the address below, give it a name and save
  4. If it asks you to sign in, sign in with the maker
SettingsConnectors1+ Create2https://…3
https://app.mandala.computer/mcp

Menu names can differ slightly by plan and over time.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • MANDALA_API_KEY secret env
    API key from Settings → Credentials → API keys. Optional after `mandala login`, which saves one.
  • MANDALA_MODEL_KEY secret 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.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

The server asks you to sign in before it lists its tools.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account mandalacomputer, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • ✓ Pass

    Endpoint responds

    Responded in 648 ms (HTTP 401)

  • ✓ Pass

    Sign-in with OAuth

    Declares OAuth sign-in, so your AI app can take you to log in

  • … Not checked yet

    No hidden instructions in tool descriptions

    Not checked yet; we read the tool list when we check the endpoint

Compared with others in Otheri

  • More stars than 0% of the other 32673 servers in this category
  • Published to the registry more recently than 86% of the other 32673 servers in this category
  • Basic checks equal to or better than 100% of the other 32673 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Mandala Computer safe?

It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: MANDALA_API_KEY, MANDALA_MODEL_KEY

Does it run on my computer or in the cloud?

Either way works: install it on your computer or connect to the cloud version.

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.22.0) reached the registry 5 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Systemdox

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

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

Runs in the cloud★ 211Updated 2 days ago
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Systemdox

by puglieseweb

Your architecture in your agents' context: checks, specs, ADRs and docs over MCP, at write time.

Runs on your computerUpdated today
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

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