glovebox-mcp

glovebox-mcp

A sandboxed computer-use MCP server that lets an AI agent drive a real browser and desktop apps, confined to a nested X11 window so it cannot touch your real screen or files.

Category
Visit Server

README

glovebox-mcp

A sandboxed computer-use MCP server — let an AI agent drive a real browser and desktop apps (mouse, keyboard, screenshots, vision grounding), confined to a nested X11 window so it can never touch your real screen, files, or other apps.

Like a lab glovebox: the agent reaches in and manipulates real applications, sealed off from everything else. Bring the sandbox up, log into whatever sites or apps you want to automate inside that window, and the agent operates only there — you can watch it live and close it instantly.

Speaks the Model Context Protocol, so it works with MCP clients like Claude Code. Your host can run Wayland; the sandbox gives the agent a real X server to drive.

glovebox-mcp — an agent filling a sign-up form inside the sandbox

<sub>An agent driving a real browser in the sandbox — gliding the cursor, inserting a unicode name (Nadja Kovačič), typing, and submitting. All confined to a nested X11 window.</sub>

Why a nested X11 sandbox?

  • Most desktop automation (xdotool, PyAutoGUI) is X11-only, but many modern desktops run Wayland.
  • Xephyr provides a real X server inside a single window (DISPLAY :1). Everything the agent does — clicks, typing, screenshots — is confined to that window, not your real desktop.
  • You stay in control: watch it live, pkill Xephyr to close everything.

Requirements

  • Linux — the sandbox nests a real X server (Xephyr), so it works even on Wayland hosts (via Xwayland). Not macOS/Windows. Developed on Ubuntu; any modern Linux with the packages below.
  • Python 3.10+ and uv (used for the virtualenv).
  • System packagesxserver-xephyr (Xephyr), openbox, scrot, x11-utils, xdotool, wmctrl, xclip (+ tesseract-ocr for basic). On Debian/Ubuntu the installer auto-installs them via apt (sudo); on Fedora/Arch it prints the matching dnf/pacman command. The MCP server itself is distro-agnostic — any Linux with these tools works.
  • A browser in the sandbox (Chromium or Chrome).
  • NVIDIA GPU (≥6 GB VRAM) — only for the local vision mode.

Install

Pick a vision backend and run its one-liner (clone → install). Each one installs the system packages (auto via apt on Debian/Ubuntu) and the Python deps for that mode, and writes a ready-to-paste mcp-config.json with your paths.

none — no local models; your agent reads screenshots itself (lightest, instant):

git clone https://github.com/segentic-lab/glovebox-mcp && cd glovebox-mcp && ./install.sh none

basic — Tesseract OCR grounding (parse_screen → text + coordinates, CPU-only):

git clone https://github.com/segentic-lab/glovebox-mcp && cd glovebox-mcp && ./install.sh basic

local — OmniParser on an NVIDIA GPU (parse_screen → text + icons, pixel-precise; ~4 GB weights, ≥6 GB VRAM):

git clone https://github.com/segentic-lab/glovebox-mcp && cd glovebox-mcp && ./install.sh local

Your choice is written to .vision-mode (override per run with the GLOVEBOX_VISION env var).

Works with any MCP client / harness

Claude Code, Cursor, Codex, or your own agent — it's a standard MCP server, not tied to any one host. Two compatibility notes: basic/local return element coordinates as text, so they work even with text-only agents; none relies on the client passing the tool's screenshots to a multimodal model (fine for Claude Code, Cursor, and other image-capable MCP clients).

Quickstart

  1. Start the sandbox (leave it running):
    ./start-display.sh              # 1440×900 Xephyr window with a browser
    ./start-display.sh 1920x1080    # …or pass a screen size (or set $RES)
    
    Log into any sites or apps you want to automate in that window.
  2. Register the server with your MCP client. install.sh already wrote mcp-config.json with your real install path — copy its glovebox block into your client's MCP config:
    { "mcpServers": { "glovebox": {
        "command": "/abs/path/to/glovebox-mcp/.venv/bin/python",   // filled in by install.sh
        "args":    ["/abs/path/to/glovebox-mcp/server.py"],
        "env":     { "DISPLAY": ":1" }
    } } }
    
    Restart the client so it loads the server.
  3. Ask the agent to screenshot / click / type — it operates only on the :1 window.

Driving it with an AI agent? Paste AGENTS.md into the agent's system prompt — it teaches the observe → act → verify loop, grounding, the upload/unicode gotchas, and when to stop.

Tools

Tool What
parse_screen() Vision grounding → JSON of every element (id, type, label, interactive, pixel-center) + a numbered Set-of-Mark image at /tmp/glovebox_annotated.png. (local mode: OmniParser on GPU, ~2 s.)
click_element(id) Click an element from the last parse_screen (no coordinate guessing).
screenshot() Screenshot of an instance.
click(x,y) · move_mouse · scroll · drag · double_click Pointer ops.
type_text(text) Unicode-safe typing (ASCII via xdotool; anything with č/š/ž… is inserted via the clipboard, because xdotool's synthetic unicode is silently dropped by some GTK apps).
press_keys("ctrl+a"/"Return"/…) Keys/combos (xdotool syntax).
upload_file(filepath, selector?) Attach a local file to a page's <input type=file> via the Chrome DevTools Protocol. The nested X11 file picker is invisible to automation and hangs the renderer, so use this for all uploads — never click an upload button expecting a dialog. Works on Chromium started by launch_app/start-display.sh (they open a per-instance --remote-debugging-port, 9222+N). Browser file inputs only — for native apps see open_file.
open_file(filepath, app?) Open a local file in a native app on the instance's display (e.g. app="gimp") or via xdg-open. GTK apps get the same X11/D-Bus handling as launch_app.
list_files() The instance's staging folder files/<N>/ (under the install dir) + its contents.
launch_app(command, name?, size?) · list_instances() · close_instance(n) Multi-instance control (see below).
wait_ms(ms) · get_screen_size() Timing / sandbox size.

Every control tool takes instance=N and optional observe / settle_ms (see below). In local mode OmniParser is lazy-loaded on first parse_screen (~6 s once, then ~2 s/parse).

Vision backend (selectable)

GLOVEBOX_VISION env var, or the .vision-mode file, or default local:

Mode parse_screen Needs When
none disabled (returns a note) — use screenshot() + reason nothing (mcp, mss, xdotool) lightest; let the agent's own vision do grounding
basic Tesseract OCR → text elements + coords tesseract-ocr + pytesseract no GPU; text-only grounding
local OmniParser → text + icons + coords torch + CUDA + OmniParser weights best grounding

Switch anytime with ./install.sh <mode> (installs only what that mode needs).

Multi-instance (a fleet of app windows)

Every control tool takes instance=N (default 1 = the start-display.sh sandbox). Spin up more — each its own Xephyr display/window on the host desktop:

  • launch_app(command, name?, size?) → starts the next free :N running any GUI app (chromium, gimp, inkscape, xterm, …). Chromium auto-gets X11 flags, a per-instance profile, a remote-debugging port, and D-Bus isolation. Returns the instance id.
  • list_instances() · close_instance(n).

Because each display has its own cursor, multiple agents can drive different instances in parallel — one window each. The only shared resource is the GPU for local-mode parse_screen (it just queues). The host display for new windows is GLOVEBOX_HOST_DISPLAY (default :0); XAUTHORITY is auto-discovered.

One-call action + observe

click · click_element · type_text · press_keys · scroll · drag · double_click take observe (none default · screenshot · parse) and settle_ms. With observe="screenshot" the action returns its result and the resulting screen in a single call (with settle_ms to let the page update first) — no separate screenshot round-trip. Default none keeps routine steps cheap; opt into screenshot/parse on the steps that change the page (navigations, submits).

Files & uploads

Each instance gets a staging folder files/<N>/ inside the install dir — a stable place to drop files for that instance (readable by native apps and, since it's under $HOME, by snap Chromium too). list_files(instance) shows the folder and its contents.

  • Browser <input type=file>upload_file(path, instance) (via CDP). The nested file picker is invisible to automation and hangs snap Chromium, so never click an upload button expecting a dialog.
  • Native apps (GIMP, Inkscape, editors) → open_file(path, instance, app="gimp"), or just drive the app's own Open dialog — unlike the browser's, it's a real visible window you can type a path into (Ctrl+L in a GTK file chooser).
  • Saving / downloads → apps run as your user, so they can save anywhere you can write. launch_app Chromium instances are pre-configured to download and "save as" into files/<N>/; point native apps' Save dialogs there too, then list_files(instance) to see the results.

Maintenance (local mode)

install.sh clones OmniParser, downloads the v2 weights, and applies two patches automatically:

  • PaddleOCR made optional (this uses easyocr): OmniParser/util/utils.py's from paddleocr import PaddleOCR is wrapped in try/except and the module-level paddle_ocr = PaddleOCR(...) is guarded with … if PaddleOCR is not None else None.
  • transformers is pinned to 4.49.0 — newer releases break Florence-2's remote config.

If you upgrade OmniParser manually, re-apply the PaddleOCR patch. Weights live in OmniParser/weights/.

Stop

pkill Xephyr      # closes the sandbox (browser + WM + display)

Safety

  • The agent's input and vision are scoped to the sandbox display — it does not see or control your real desktop.
  • The server process runs as your user (shell/file access, like any MCP server); only its GUI control is sandboxed to the Xephyr window. For OS-level isolation from your files, run it inside a VM or container.
  • You can watch everything live and close it instantly with pkill Xephyr.
  • Automate responsibly — only sites and services you are authorized to use.

Files

  • server.py — the MCP server (all tools).
  • install.sh — mode-aware installer (none / basic / local).
  • start-display.sh — launches the Xephyr sandbox (display + window manager + browser).
  • AGENTS.md — drop-in tool-usage instructions for the AI agent (paste into its system prompt).
  • mcp-config.json — a ready-to-paste MCP client config snippet.

Credits

local vision mode uses Microsoft's OmniParser (cloned and weights downloaded by install.sh, under its own license). Screen capture uses mss; input is driven with xdotool. Not affiliated with Microsoft.

Contributing

Shipped as-is under MIT. Issues and PRs are welcome, but this is maintained by one person — no support or response time is guaranteed. If it's useful to you, a ⭐ helps.

License

MIT — see LICENSE.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured