hypruse
The hypruse server gives AI agents native control over a Hyprland Wayland desktop via MCP tools:
Desktop Snapshot — Retrieve a full semantic snapshot of the desktop in one call: monitors, workspaces, all open windows (address, class, title, geometry), active window, and cursor position.
Screenshot — Capture the focused monitor, a specific window (by address), or an arbitrary region (
x,y,WxH). Returns the image plus coordinate/scale metadata for mapping pixel positions to global coordinates.Mouse Control — Move cursor, click (left/right/middle, single or double), drag (click-and-hold from one coordinate to another), and scroll (vertical/horizontal) — all in global coordinates.
Keyboard Input — Type arbitrary unicode text or press key combos (e.g.,
ctrl+shift+t,super+enter,F5, arrow keys) to the focused window.Window & Workspace Management — Via Hyprland IPC: switch workspaces, focus/move/close windows, toggle fullscreen or floating mode.
Application Launching — Launch apps via Hyprland exec, optionally on a specific workspace (handles single-instance apps). Waits for the new window and returns its address, class, title, and workspace.
Security — Includes an activity beacon, a panic keybind to kill the server, and MCP approval prompts for tool calls.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@hypruseTake a screenshot of my focused window."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
hypruse
Computer use for Hyprland. An MCP server that gives AI agents native hands on your Wayland desktop: workspaces, windows, mouse, keyboard, screenshots.
No ydotool daemon. No root. No portals. No X11.

Why
Computer use exists on macOS and Windows. On Linux there is effectively nothing: the Claude Desktop Linux beta explicitly ships without screen control, Anthropic's reference implementation is an X11 container, and the existing Wayland attempts lean on setuid uinput hacks or GNOME-only portals.
Meanwhile Hyprland already exposes everything an agent needs, better than any accessibility bridge: a complete IPC surface for state and window management, and first-class Wayland protocols for input. hypruse just wires them to MCP:
Semantic first.
desktopreturns the real window/workspace tree (addresses, classes, titles, geometry) in one call. The agent switches workspaces and focuses windows the way you do (instantly, over IPC), not by squinting at pixels.Vision when it matters. Screenshots of a monitor, an exact window crop, or a zoomed region, with the geometry/scale metadata to map any pixel back to a clickable coordinate: a coarse-to-fine loop grounded in the GUI-agents research.
Native input. Clicks and scrolls are spoken directly over the Wayland wire (
zwlr_virtual_pointer_v1); typing goes throughwtype's virtual keyboard with a proper XKB keymap, unicode-safe on any layout.
Related MCP server: hyprland-mcp
How it works
agent (Claude Code, or any MCP client)
│ stdio
▼
hypruse
├── hyprctl -j ········▶ desktop state: monitors, workspaces, windows, layers
├── hyprctl dispatch ··▶ focus / move / close / launch / movecursor
├── grim ··············▶ screenshots: monitor, window crop, region
├── busctl (AT-SPI) ···▶ accessibility tree: named controls, current
│ values, exact coords (ui / marks / click_ui)
├── wtype ·············▶ keyboard (zwp_virtual_keyboard_v1, real XKB keymap)
└── raw Wayland wire ··▶ click & scroll (zwlr_virtual_pointer_v1)Optional binaries gate two more tools: imagemagick draws the numbered
overlay for marks, and wl-clipboard backs the opt-in clipboard tool.
Design decisions:
No ydotool / uinput. That path needs a daemon, udev rules or root, and types US scancodes that break on other layouts. hypruse is just another Wayland client of your compositor, same standing as
wlrctl.No portals.
xdg-desktop-portal-hyprlanddoes not implement the RemoteDesktop portal (InputCapture is capture, not injection), so anything built on libei/portals silently degrades on Hyprland. hypruse doesn't try.Cursor positioning through the compositor's own cursor dispatcher (global logical coordinates, exact on any monitor layout), with only button/axis events on the virtual pointer, sidestepping the known multi-monitor bugs of absolute virtual-pointer motion (hyprwm/Hyprland#6749).
Tools
tool | what it does |
| One-call semantic snapshot: monitors, workspaces, windows (address/class/title/geometry), active window, cursor, and layer surfaces (launchers, bars, notification popups) with a best-effort kind and geometry. A listed layer is one the compositor tracks, not one you can see: transparent and dormant surfaces are reported too, so screenshot when visibility matters |
| Focused monitor, exact window crop by address, or |
| Native-resolution re-capture around an estimated point (optionally clamped to a window): the precision step before clicking small controls, same metadata contract |
| Read a window's accessibility tree (AT-SPI, GTK/Qt apps that expose one) and return clickable elements by name with exact global coordinates, no screenshot; reports current values too (typed text, slider position, checkbox state); falls back to vision when an app exposes nothing |
| Set-of-Marks capture: the window screenshot with every accessible control drawn as a numbered mark, plus a JSON legend (role, name, current value, exact click point per number); needs ImageMagick for the drawing, degrades to the legend alone without it |
| Click a control by accessible NAME or by a |
| move / click / drag / scroll (discrete wheel notches) in global coordinates |
| Type literal text (unicode-safe) or press app-level combos ( |
| Switch workspace, focus/move/close windows, fullscreen, floating (pure IPC, milliseconds; |
| Start an app (optionally silent on another workspace), block on its actual |
| The user's own keybinds, decoded ( |
| Execute a keybind by combo ( |
| Run an ordered list of actions (pointer/keyboard/click_ui/hypr/wait_for) in one call; stops the moment the desktop changes in a way the current step did not expect, so a click/type/enter micro-sequence costs one round-trip instead of several |
| Block on real compositor events (window open/close, workspace change, title change, layer surfaces appearing/closing, urgency, screen sharing on/off) with a match filter and timeout; a filtered wait whose condition already holds (window already gone, workspace already active, layer already mapped) answers instantly with |
| Read or write the text clipboard via |
The acting tools (pointer, keyboard, click_ui, hypr, use_bind, sequence) take an optional then argument that appends the result to the same call, so the agent sees the effect without a second round-trip: then='desktop' adds a fresh semantic snapshot (~20 ms, cheap, best for window/focus changes), then='screenshot' a stable capture (best for visual changes), then='ui' the acted-on window's controls with their current values (a few hundred exact tokens, best after typing or toggling; click_ui reads the window it clicked even if the click handed focus to a dialog, the others read the focused window), then='none' nothing (the default everywhere except sequence, which defaults to 'desktop').
Features
The tools group into five capabilities, ordered most-reliable-and-cheapest first. An agent that reaches for them in this order is both faster and more accurate, and many tasks never need a screenshot at all.
1. Semantic desktop control (start here)
desktop returns the entire window and workspace tree in one call: every window's address, class, title, and geometry, the active window, the cursor, and any layer surfaces the compositor is tracking (launchers, bars, notification popups). hypr and launch then act on it over IPC in milliseconds: switch workspace, focus/move/close/fullscreen/float a window by address, or start an app.
Use it well: never take a screenshot to find or arrange windows. Read desktop, act on the address you want. launch blocks on the real openwindow event and hands back the new window's address, so there is nothing to poll or guess; it also relocates single-instance apps (browsers) that ignore workspace rules.
2. Click controls by name, no pixels
When an app exposes an accessibility tree (most GTK and Qt apps), you can target controls by name instead of by pixel. ui lists every control with its exact global coordinate, and reports the current value of the controls that carry one: the text in a field, a slider's percentage, a checkbox's state. click_ui resolves a name and clicks it in one call, through the real cursor (so the beacon and every safety guarantee still apply). marks draws numbered marks over a screenshot with a legend, for when you want to see the options first and then click_ui(mark=N).
Use it well: reach for click_ui name="Save" before estimating any pixel, since it is exact and spends no image. Read a form's state with ui (did the box actually tick?) instead of screenshotting it. An ambiguous name returns the candidates rather than guessing. When an app exposes no tree (terminals, canvas apps, Electron/Chrome without --force-renderer-accessibility) the tool says so, and you fall back to vision.
3. Vision when it matters: the zoom loop
For everything the accessibility tree cannot name, screenshot (monitor, window crop, or region) and zoom (a native-resolution re-capture around a point) carry a strict coordinate contract, global = geometry + pixel / scale, that stays exact on every monitor and fractional scale.
Use it well: don't guess a small control from a full-screen image. Work coarse-to-fine: screenshot the window, estimate the target, zoom there, re-estimate on the sharp crop, then click. This two-step loop is the research-backed way to hit small targets.
4. Fewer round-trips: the latency lever
For an agent the model calls dominate task latency, not the desktop, so the real speedups are structural. sequence runs an ordered micro-plan (click, type, press enter, wait) in a single call, stopping the moment the desktop changes structurally in a way a step did not intend (a window opening, closing, or moving, an unexpected workspace switch, or a seat-taking launcher or on-screen keyboard; it deliberately ignores bare focus changes and notification popups). then='desktop' | 'screenshot' | 'ui' fuses a fresh view of the result into the acting call itself. wait_for blocks on real compositor events (a window or launcher opening, a title changing, a workspace switch, an urgency hint, screen-sharing starting) instead of sleeping and hoping.
Use it well: collapse a known click/type/enter flow into one sequence. After typing into a form, add then='ui' to read the effect back in a few hundred exact tokens instead of a screenshot. After a launch or a shortcut that opens something, wait_for the event rather than sleeping.
5. Safe delegation: trust layers
hypruse hands an agent your real seat, so it ships the controls to bound what that agent can do. Beyond the always-on approval prompts and the Waybar activity beacon, opt-in env flags narrow what an agent can touch: HYPRUSE_READONLY exposes only the observation tools; HYPRUSE_CONFINE restricts input to the windows the agent launched, or a class/workspace allowlist; HYPRUSE_AUTH_GUARD (on by default) refuses to drive authentication dialogs; HYPRUSE_STRICT refuses to act if you took the seat back; HYPRUSE_MARK tags agent-owned windows and announces when the agent opens a window or captures the screen. Two more record rather than restrict: HYPRUSE_JOURNAL writes an auditable NDJSON line per tool call, refusals included, and HYPRUSE_DRYRUN runs every check and delivers nothing, so you can watch an agent plan the work before it touches your desktop.
Use it well: run read-only for the first week. When you trust a workflow, allowlist its tools and, if you want to walk away, confine the agent to a scope so your password manager on another workspace stays untouchable. Keep a panic bind handy (hypruse stop, or pkill -f hypruse). The Security model has the full story.
Install
Requirements: Hyprland (both config managers: hyprland.conf and the Lua hyprland.lua that 0.56 introduced), grim, wtype (most Hyprland setups already have both), and uv. The accessibility tools (ui/marks/click_ui) use busctl, which ships with systemd. Optional: wl-clipboard for the opt-in clipboard tool, imagemagick for numbered marks captures.
Arch Linux, from the AUR:
yay -S hypruse # or hypruse-git for mainThen let it set itself up and verify the environment:
hypruse init # detects your MCP clients, registers (asks first), runs doctor
hypruse doctor # just the diagnosticsManual registration, Claude Code:
claude mcp add -s user hypruse -- uvx hypruseFrom a source checkout:
claude mcp add -s user hypruse -- uv run --directory /path/to/hypruse hypruseAny other MCP client: run uvx hypruse as a stdio server. hypruse is also in
the official MCP registry as
io.github.IlyasKhallouki/hypruse, so clients that browse the registry can
install it from there.
Read-only mode: set HYPRUSE_READONLY=1 in the server config to expose only the observation tools (desktop, screenshot, zoom, ui, marks, binds, wait_for). The agent can see and narrate but cannot click, type, or launch. A good first week.
Claude Desktop (Linux beta)
The Linux beta ships without Anthropic's first-party computer use, but stdio MCP servers work in chat, which makes hypruse the workaround. In ~/.config/Claude/claude_desktop_config.json:
{
"mcpServers": {
"hypruse": {
"command": "uvx",
"args": ["hypruse"],
"env": { "HYPRUSE_SCREENSHOT_MODE": "image" }
}
}
}Two Desktop-specific notes: use image mode (Desktop renders inline MCP images and has no file-read tool), and the app must run natively inside your Hyprland session so the server inherits WAYLAND_DISPLAY/HYPRLAND_INSTANCE_SIGNATURE; from a VM or container it cannot reach your compositor. If your Desktop install bypasses tool-approval prompts, treat the Waybar indicator + panic keybind as mandatory, not optional.
Security model
Read this section before installing. hypruse hands an agent your mouse, your keyboard, your screen contents, and an app launcher. The layers that keep that sane:
Approval: MCP clients gate tool calls. In Claude Code, allowlist the read-only tools (
desktop,screenshot) and leavepointer/keyboard/hypr/launchon ask-first until you trust a workflow.Visibility: the server maintains an activity beacon (
$XDG_RUNTIME_DIR/hypruse/state.json); the shipped Waybar module is invisible when idle and shows a robot indicator while an agent has hands on your desktop.Interruption: click the indicator, or bind a panic key. The portable form works for every install:
bind = SUPER SHIFT, BackSpace, exec, pkill -f hypruse. Ifhypruseis on your PATH (the AUR or a pipx install),bind = SUPER SHIFT, BackSpace, exec, hypruse stopis nicer: it signals the server to shut down gracefully, releasing any held pointer button and clearing the beacon. For auvxinstall useexec, uvx hypruse stop; for a source checkout,exec, uv run --directory /path/to/hypruse hypruse stop. Killing it mid-action is safe either way: button press/release pairs never span tool calls, and even a long drag's held button is released on the way out.The seat is shared. There is one cursor and one keyboard focus, and Hyprland's focus-follows-mouse means a cursor move alone can retarget keystrokes. Don't type while an agent is driving; watch the indicator.
Scope: stdio only (no network listener), nothing persisted except the beacon and the capped screenshot cache in
$XDG_RUNTIME_DIR(tmpfs, newest 20) and, if you turn it on, the action journal on disk under$XDG_STATE_HOME(rotated, one generation, and text-redacted by default). No clipboard access unless you opt in:HYPRUSE_CLIPBOARD=1registers aclipboardtool (never in read-only mode); clipboards hold passwords, so leave it off unless a workflow needs it. A screenshot sees everything visible: treat an agent session like screen sharing.What the agent reads is untrusted. Window titles, accessibility names and values, and clipboard text flow verbatim into the agent's context, and any web page, filename, or document can put instructions there (prompt injection). hypruse cannot sanitize meaning, so the approval layer is the backstop: keep consequential tools (
launch,keyboard,clipboard) on ask-first when the agent will look at untrusted windows, and treat "the screen told me to" as attacker input when reviewing an approval prompt.Input never lands where it silently would not work. Three always-on checks (no env flag) refuse or annotate rather than report a phantom success: a click aimed under a launcher or on-screen keyboard layer surface (which sits above windows and would swallow it), typing while a launcher holds the keyboard grab, and any input while the session is locked (a live
hyprlock/swaylockprocess, which is anext-session-lockclient invisible to the window and layer lists). While locked,keyboard/click_ui/pointerrefuse unlessallow_auth=truesays a human wants the agent driving the unlock prompt. These are truthfulness aids, not a sandbox: they fail open on an unreadable system state, so they harden the common case without being a boundary you can lean on.
Optional confinement
Six opt-in env flags. The first four narrow what an agent can touch, and each fails toward less action; the last two record and rehearse rather than restrict. All compose with the layers above:
HYPRUSE_CONFINErestricts input to a scope of windows:launched(only windows hypruse itself opened this session),class:firefox,kitty, orworkspace:3,special:notes. Keyboard,click_ui, andhyprwindow ops are refused outside the scope; apointerclick is refused when any window under the point is out of scope (Hyprland's window list is not z-ordered, so hypruse fails closed rather than guess which window is on top). This is what lets you leave an agent working while your password manager sits on another workspace, untouchable.use_bindis refused outright while confinement is set, because a keybind runs an arbitrary compositor action that cannot be scoped to a window.HYPRUSE_AUTH_GUARD(default on) refuses to click or type into a system authentication dialog (polkit agents, the GNOME keyring prompt), so a manipulated agent cannot approve a privilege escalation. SetHYPRUSE_AUTH_GUARD=strictto also refuse typing into a password field inside an ordinary window (a browser login), detected via the accessibility tree. A per-callallow_auth=trueonpointer/keyboard/click_uioverrides it, and because it changes the tool's arguments the override surfaces distinctly in the approval prompt.HYPRUSE_AUTH_GUARD=0disables it.HYPRUSE_STRICTrefuses to act when the cursor or focused window moved since hypruse's last action (the human, or a popup, took the seat): the agent must re-readdesktop/screenshotand retry, so it never types into a window you just switched to.HYPRUSE_MARKmakes the agent's presence legible on the desktop: it tags every window the agent openshypruse-ownedand flashes an on-screen notice when the agent opens a window or captures the screen. It also installs aborder_colorwindow rule on that tag so owned windows get a colored outline, but whether a runtime rule renders depends on your Hyprland version and config precedence (on some setups it does not take effect); when it cannot be installed at all, hypruse says so on stderr rather than leaving you with a marking layer that is quietly not running. For a guaranteed outline, add the rule to your own config, which hypruse's tagging then matches:windowrule = border_color rgb(ff5555), tag hypruse-ownedinhyprland.conf(older Hyprland:tag:hypruse-owned), orhl.window_rule({ match = { tag = "hypruse-owned" }, border_color = "rgb(ff5555)" })inhyprland.lua.HYPRUSE_JOURNALrecords what the agent did: one NDJSON line per tool call in$XDG_STATE_HOME/hypruse/journal.ndjson(HYPRUSE_JOURNAL=1), or a path of your own. Read it withhypruse journal, re-run it withhypruse replay. See The record below.HYPRUSE_DRYRUNis a rehearsal: every argument check and every guard above runs, then the call reports what it would have done and delivers nothing.
The record: journal, dry run, replay
The flags above decide what an agent may do in the moment and then forget it happened. HYPRUSE_JOURNAL is the memory. One JSON object per line, so tail -f, grep, and jq all work on a live file:
{"v":1,"seq":7,"ts":"2026-08-02T09:12:13.456Z","kind":"act","tool":"pointer",
"args":{"action":"click","x":800,"y":60},"outcome":"ok","ms":37,"result":"click ok"}kind splits the two questions people actually ask: act is input delivered to your desktop, observe is the agent looking, which is what a privacy audit wants (when the screen was captured, when the clipboard was read). Observation results are never recorded, only that they happened, so the journal never becomes a second copy of everything the agent saw. Refusals are recorded too, with the guard's own message: it is the only place the history of your trust layers doing their job exists.
Typed and copied text is recorded as a length plus a short digest, not as text, because keystrokes are passwords. HYPRUSE_JOURNAL_TEXT=1 keeps it verbatim, which you need only to replay typing. Note what a digest is and is not: it proves two entries typed the same thing and it will not hand a reader your password, but it is an unsalted SHA-256 prefix next to an exact character count, so a four-digit PIN is trivially recovered from it. Treat the journal as sensitive either way. It is written 0600 in a 0700 directory, and rotated at HYPRUSE_JOURNAL_MAX_BYTES (8 MiB, one previous generation kept as .1; set 0 to never rotate).
The journal is a recorder, not a guard: if it cannot be written the action still happens and hypruse warns once on stderr, because failing your desktop over a log line is the wrong trade. What it does not record is a then= observation as its own entry: the acting call that carried it is recorded, then=screenshot and all, but the capture does not get a second line of its own.
Read it back with hypruse journal (--acts for actions only, --refused for what the guards stopped, -n N to tail, -v to include each result):
1 09:12:10 session start pid 4211 0.10.0 confine=launched auth_guard=1 strict=True
14 09:12:13 act pointer action=click x=800 y=60
16 09:12:15 act keyboard action=type text=<11 chars>
19 09:12:19 act hypr action=close_window target=0x5f2a10
REFUSED TrustError: 0x5f2a10 (Signal) is outside the agent's confinement scope
312 actions (0 dry), 604 observations, 1 refused by a trust layer, 0 errorsHYPRUSE_DRYRUN=1 turns the same session into a rehearsal. Every acting tool validates its arguments and runs every trust guard, then reports the plan instead of executing it, so a dry run refuses exactly what a real run would:
DRY RUN, nothing was delivered: would click push button 'Send' at (1204, 880) in signalNothing reaches the desktop: not the click, not the keystroke, not even the window focus that normally precedes typing. Enforced twice, once at each tool and once at the input path itself, so a code path nobody thought of fails loudly rather than quietly acting during a simulation. The agent is told dry run is on, so it reports a plan instead of retrying an action that "did not work". The scope is the agent's actions, not the server's own startup: HYPRUSE_MARK, if you set it, still installs its window rule when the server starts.
hypruse replay <journal> re-issues a journal's actions through the same tool functions, so the same guards apply to the replay. It prints the plan and stops there unless you pass --execute. Before it takes the seat it refuses outright, rather than failing halfway and leaving your desktop part-way through someone else's plan, when: an action was recorded by a newer hypruse, a recorded window no longer exists (--skip-missing runs the rest), typed text was recorded as a digest, the entry is a click_ui(mark=N) whose numbering died with the session that drew it, the entry is a clipboard write and HYPRUSE_CLIPBOARD is not set, or HYPRUSE_READONLY or HYPRUSE_DRYRUN is set. It paces itself from the recorded timing, capped by --max-gap and scaled by --speed, and its own actions are recorded and marked, so replaying the same file again runs the original plan rather than the plan plus the replay of it.
The honest limit is window addresses: they are heap pointers, so yesterday's journal mostly names windows that are gone, and an address can even be reused by a different window later, which no pre-flight can catch. Replay is for re-running a flow on a desktop that still looks like the one recorded.
Dry run and replay compose in the obvious direction: let the agent work with HYPRUSE_DRYRUN=1, read the journal, and replay it with --execute once the plan is one you like.
Performance
Measured on a live session (Hyprland 0.55, 1080p, 20 windows): desktop
~20 ms (one batched hyprctl call), workspace/window dispatch ~10-20 ms, full-monitor screenshot ~65 ms
(fast JPEG default; ~800 ms if you ask for lossless PNG, grim's zlib
path dominates), region/zoom captures well under that. If tool
calls feel slow, it is almost certainly the MCP approval prompt in
front of each call, not the server. Allowlist the tools you trust and the
latency disappears. Claude Code (.claude/settings.json):
{
"permissions": {
"allow": [
"mcp__hypruse__desktop",
"mcp__hypruse__screenshot",
"mcp__hypruse__hypr"
// add pointer/keyboard/launch once you trust your workflows
]
}
}Coordinates
Everything speaks Hyprland's global logical coordinates, the space hyprctl cursorpos and window at use. Screenshots are pixel-space; each capture returns geometry and scale so global = origin + pixel / scale. On scale 1.0 monitors (most setups) image pixels are global coordinates.
The zoom tool does the precision arithmetic for the agent: give it an estimated global point and it captures a native-resolution box around it, clamped to the screen (or to a window), with the same metadata contract. That two-step loop, estimate on the full view then re-estimate on the zoom, is the research-backed way to hit small controls.
Captures default to JPEG q90: on a 1080p frame that is roughly 12x faster to encode than PNG (grim's zlib path dominates capture time, measured ~65 ms vs ~800 ms) and 3-4x smaller, while full-res q90 reads UI text well. Pass lossless=true for exact pixels (PNG). In image mode, captures also fit the host's result-size limit (Claude Desktop caps tool results at 1 MB) by degrading quality before resolution, since grim's downscale filter is slower than a full-res capture, and cap the long edge at HYPRUSE_MAX_IMAGE_EDGE pixels (default 1568) so the host never downscales the image under the model. The applied scale is folded into the returned metadata, so coordinate mapping stays exact; tune with HYPRUSE_MAX_IMAGE_BYTES, or pass scale for a deliberate zoom-out.
By default the screenshot tool writes the image under $XDG_RUNTIME_DIR/hypruse/ and returns its path; MCP hosts with a file reader (Claude Code's Read) render it natively. This default exists because some hosts (including Claude Code 2.1.x) serialize inline MCP image blocks to base64 text the model cannot see. HYPRUSE_SCREENSHOT_MODE=image switches to inline image content blocks for hosts that render them correctly.
Development
uv sync --group dev
uv run pytest # unit tests, no compositor needed
uv run pytest -m e2e --override-ini addopts= # live seat-safe checks
uv run python scripts/e2e_input.py # supervised: takes the seat ~10sThe input e2e is deliberately manual: it borrows your cursor and keyboard, counts down, proves click/scroll/type delivery by reading the target terminal's screen back over kitty remote control, and restores your focus.
Roadmap
Grounded in measured hot-path latencies and the finding that LLM calls are 76 to 96% of computer-use task latency (OSWorld-Human), so cutting round-trips beats shaving milliseconds. The round-trip work that framing motivated has largely shipped: sequence, act-and-observe then= (including then='ui'), and the accessibility-tree tools (ui, marks, click_ui) that target controls by name with no screenshot. What remains:
Faster
In-process wlr-screencopy over the raw wire (as input already works): drop grim's fork floor from small captures and add damage-tracked wait-for-stable that returns the instant the screen settles.
Fewer round-trips
Semantic screen diff: after an action, return only what changed (window topology from the event stream, or a bounded changed-region crop) instead of a full frame. The socket2 event expansion behind
wait_foralready tracks most of the topology; the missing piece is folding it into a post-action delta.
Deeper reading
Wider accessibility coverage: close the gaps the
uiandmarkstools hit today, chiefly GTK's newer combo boxes that publish neither their text nor selection (so a rendered dropdown value still needs a screenshot), plus AT-SPI value-change events sothen='ui'can report a control settling without a poll.Notifications: read recent desktop-notification content and history, not just wait for the popup to appear (
wait_foralready matcheslayer_openon the notification namespace).
Trust
recordtool: a scoped GIF or mp4 of the agent driving the desktop, via wf-recorder (a wlroots-family binary like grim), a visual companion to the journal.
Platform
sway / niri support: the wire client already speaks the wlr protocols; what remains is an IPC layer alongside
hyprctl.py(contributions welcome).Headless end-to-end tests in CI: needs a QEMU virtio-gpu VM, since Hyprland's aquamarine backend requires a real GPU render node that hosted runners lack.
Measurement
Zoom-loop precision benchmark: measure click accuracy of the coarse-to-fine loop against known targets.
End-to-end task-success benchmark on Hyprland (OSWorld-style): the deferred bigger sibling of the zoom-loop microbenchmark, scoring full multi-step tasks through the real MCP surface so the a11y-versus-vision and round-trip work is judged on task completion, not latency alone.
Related projects
project | approach | on Hyprland |
computer-use-linux | AT-SPI + portals, ydotool fallback | GNOME-first; the RemoteDesktop portal it prefers is not implemented by xdg-desktop-portal-hyprland |
hyprmcp | hyprctl wrapper | window management only; no screenshots or input |
wayland-mcp | evemu input, VLM analysis | requires elevated setup for input; no Hyprland semantics |
Anthropic computer-use-demo | X11 + xdotool in Docker | a sandboxed reference environment rather than a live desktop |
Research
hypruse ships no OCR engine; its universal precision mechanism is the coarse-to-fine zoom loop: screenshot a window, re-capture the target region at native resolution, click through the exact coordinate mapping. That choice follows what the GUI-agents field converged on. Anthropic's computer use grounds clicks from raw pixels and ships a zoom action as the documented fix for small text; its troubleshooting guidance for near-miss clicks prescribes zooming and region cropping, never OCR [1]. OpenAI's CUA is likewise pure pixel grounding under resolution discipline, with no OCR layer at all [2]. Zoom is also the measured lever: training-free iterative zooming roughly doubles high-resolution grounding accuracy (OS-Atlas-7B, 18.9 → 49.7 on ScreenSpot-Pro) [3], and the benchmark's official harness implements a dozen grounding-model adapters plus four zoom/crop strategies, but zero OCR baselines [4]. Vision-only agents match or beat agents that additionally consume HTML or accessibility trees [5], substrates Wayland doesn't guarantee anyway, and state-of-the-art native agents run from screenshots alone [7]. OCR was rejected because it is blind to icons, the element class every grounding model handles worst (SeeClick: 30-52% on icons vs 56-78% on text) [6]; where OCR survives in modern stacks it is a text-disambiguation sidecar, not the targeting mechanism [8].
Where an app exposes an accessibility tree, hypruse also reads it (the ui tool, AT-SPI over D-Bus via busctl) to target controls by name with exact coordinates and no screenshot. This follows the strongest Linux precedent: OSWorld, the standard computer-use benchmark, exposes the desktop accessibility tree (obtained on Ubuntu through AT-SPI) as a first-class observation alongside screenshots, and reports the accessibility-tree-plus-screenshot combination as its best configuration [9]. The tree gives exact element identity and coordinates that current models cannot reliably infer from pixels: Agent-S tags each element with an id because MLLMs "lack an internal coordinate system," lifting OSWorld success from 11.2 to 20.6 percent [10]; Microsoft's UFO drives Windows through the UI Automation tree and fuses it with vision [11]; browser agents read the accessibility tree, which Playwright serializes to compact YAML, rather than pixels for the same reason [12]. hypruse's Wayland-specific trick is coordinate mapping: AT-SPI screen coordinates are unreliable on Wayland because an app does not know its global position, so hypruse uses window-relative extents plus the window position it already has from hyprctl. Coverage is uneven by nature: canvas, games, terminals, and Electron/Chrome without a flag expose little or nothing [13]. Testing this implementation against real GTK and Qt apps sharpened that in both directions: a typed entry reads back its exact contents, and sliders and toggles report their position and state, but GTK's newer combo boxes publish neither their text nor a selection, so reading a rendered dropdown value still needs a screenshot. Toolkits also describe widgets they never laid out (zero-height scroll arrows, unrendered tab pages reporting origins in the millions), which hypruse rejects against the window rect hyprctl knows authoritatively. A strong visual grounder can substitute for the tree entirely [5], so the accessibility tree complements the zoom loop rather than replacing it, and vision stays the guaranteed fallback.
Anthropic, Computer use tool: platform docs (
computer_20251124,enable_zoom, resolution guidance)OpenAI, Computer-Using Agent and the computer use guide
DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning, EMNLP 2025. arXiv:2507.00008
Li et al., ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use. arXiv:2504.07981; official harness
Gou et al., Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents (UGround), ICLR 2025 Oral. arXiv:2410.05243
Cheng et al., SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents, ACL 2024. arXiv:2401.10935
Qin et al., UI-TARS: Pioneering Automated GUI Interaction with Native Agents. arXiv:2501.12326
Agyeya et al., Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents (Tesseract as a textual-grounding sidecar). arXiv:2504.00906
Xie et al., OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments. arXiv:2404.07972; the a11y tree is obtained on Ubuntu through AT-SPI (code)
Agashe et al., Agent S: An Open Agentic Framework that Uses Computers Like a Human. arXiv:2410.08164
Zhang et al., UFO: A UI-Focused Agent for Windows OS Interaction (UI Automation + vision). arXiv:2402.07939
Playwright, ARIA snapshots: the accessibility tree as compact structured text
Wang et al., GUI Agents: A Survey (accessibility-API coverage gaps; a11y complements vision). arXiv:2412.13501
License
Available Tools
9 toolsbindsA
The user's own Hyprland keybinds: combo, action, arg, and a
description when the config provides one. This is how the desktop's
owner drives it: to perform one of these workflows, call use_bind
with the combo (it runs the bound action). NOTE: the keyboard tool
canNOT trigger these compositor binds (synthetic keys reach apps, not
Hyprland's bind matcher), so do not try to press them.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tool's output (combo, action, arg, description) and notes that synthetic keys from keyboard cannot trigger these compositor-specific binds. No destructive behavior is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the return value and purpose. It is clear and to the point, though the second sentence could be slightly reworded for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no annotations, the description sufficiently covers the tool's purpose, output, and usage context. It is complete for a simple listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so baseline is 4. The description does not need to add parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: it provides the user's own Hyprland keybinds (combo, action, arg, description). It also explains its role in the workflow and distinguishes it from sibling tools like keyboard and use_bind.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to use `use_bind` with the combo to perform a workflow and warns against using the `keyboard` tool to trigger these binds. It provides clear guidance on when to use this tool (to get binds) and what to do next.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
desktopA
Semantic desktop snapshot: monitors, workspaces, windows (address,
class, title, at + size in global coords), active window, cursor,
and layers when layer-shell surfaces are up: launchers (wofi/rofi),
bars, notification popups, and on-screen keyboards are NOT windows and
appear only there, with a best-effort kind and global geometry you can
screenshot by region or click into. Call first; act on the addresses
it returns.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It details what is included (windows, layers, cursor) and what is not (launchers, bars as windows), and mentions best-effort geometry. It does not explicitly state read-only behavior but the snapshot nature implies non-destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence that efficiently packs a lot of information. It could be better structured but is not overly long or verbose, earning its content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown), the description covers key elements: content, coordinate system, special surfaces, and usage order. It could mention limits or error scenarios, but is sufficiently complete for a snapshot tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty (0 parameters, 100% coverage). Following the baseline guideline for zero parameters, the description adds no parameter details, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a semantic desktop snapshot including monitors, workspaces, windows with coordinates, active window, cursor, and layer-shell surfaces. It distinguishes from sibling tools like screenshot (captures image) and pointer (cursor control) by providing coordinate information for later actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises 'Call first; act on the addresses it returns,' establishing clear usage order. While it does not list when not to use or exhaustive alternatives, the instruction implies it is the initial step for tools that require addresses.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
keyboardA
Keyboard to the focused app. action='type' (text, unicode-safe) |
'key' (keys combo: 'ctrl+shift+t', 'esc', 'F5'; aliases
enter/esc/tab/backspace/pgup/pgdn/arrows, else XKB keysyms). Pass
window (an address from desktop) to focus that window first, so
keystrokes land in the intended app rather than whatever currently
holds focus. This drives shortcuts the focused application handles
(ctrl+t, ctrl+l). It does NOT trigger Hyprland's own keybinds
(super+...): those go through use_bind, and workspace/window actions
through hypr. then ('desktop'|'screenshot'|'ui'|'none') appends the
result to this call. allow_auth=true overrides the default refusal to
type into a password field or a system authentication dialog (only when
a human intends that credential entry).
| Name | Required | Description | Default |
|---|---|---|---|
| keys | No | ||
| text | No | ||
| then | No | none | |
| action | Yes | ||
| window | No | ||
| allow_auth | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses key behaviors: unicode-safe typing, key aliases, window focusing for reliability, that it does not trigger Hyprland binds, and the allow_auth override. This covers safety and operational traits comprehensively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Despite length, every sentence is essential and packed with information. The description is well-structured, front-loaded with core purpose, uses clear formatting (vertical bars, examples). No redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all parameters and usage guidelines thoroughly. Does not describe the output schema or error behavior, but given the tool's nature and existence of output schema, this is a minor gap. Overall, it provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description compensates fully by explaining each parameter: action values ('type'/'key'), keys format and aliases, text, window, then options, and allow_auth boolean with behavioral implications. Provides examples and valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool sends keystrokes to the focused app, with actions 'type' or 'key', and can target a specific window. It differentiates from sibling tools (use_bind, hypr) for Hyprland-specific actions, establishing a distinct purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly explains when to use this tool (for application shortcuts) and when not to (Hyprland binds, workspace actions), directing to siblings use_bind and hypr. Also notes special case of allow_auth for password fields.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
marksA
Set-of-Marks capture: a screenshot of the window WITH its accessible
controls drawn as numbered red marks, plus a JSON legend mapping each
number to the control's role, name, current value, and exact global
click point. One glance replaces the estimate-zoom-estimate loop for
every control the accessibility tree knows: read the number off the
image and call click_ui(mark=N) (or pointer at the legend's x,y).
window is an address from desktop (default: focused); name filters
the marked controls. Falls back to the plain legend when ImageMagick is
not installed, and to a fall-back-to-vision note when the app exposes
no tree (then use screenshot + zoom).
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| window | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the capture behavior, reliance on accessibility tree, fallback to plain legend when ImageMagick is absent, and a note when no tree exists. It does not mention destructive actions, rate limits, or authentication, but these are not critical for a read-like capture tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately long but all sentences serve a purpose: core functionality, usage advice, fallback conditions. It is front-loaded with the primary action. Minor redundancy (e.g., 'Falls back...') could be more concise, but overall well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of fallbacks (ImageMagick, no tree) and the presence of an output schema (return values not needing further explanation), the description covers all necessary context: parameters, output (JSON legend), and use case. It references sibling tools appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds meaning: `window` is 'an address from desktop (default: focused)' and `name` 'filters the marked controls'. It does not specify the exact format of the window address, but provides enough context for the agent to use parameters correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool captures a screenshot with numbered red marks and a JSON legend mapping each mark to control details. It uses the specific verb 'capture' and resource 'Set-of-Marks', and distinguishes from siblings like 'screenshot' and 'ui' by highlighting marks and legend.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises when to use the tool: to replace the 'estimate-zoom-estimate loop' for controls in the accessibility tree. It provides follow-up actions (using `click_ui(mark=N)` or `pointer`), and specifies fallbacks for missing ImageMagick or no accessibility tree, with an alternative (screenshot + zoom).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pointerA
Mouse in global coordinates. action='move' (x,y) | 'click' (optional
x,y first; button left/right/middle; double=true) | 'drag' (x,y →
to_x,to_y holding button) | 'scroll' (scroll_dy notches, positive =
content down; optional x,y first). then appends the result to this
call so you skip a round-trip: 'desktop' a fresh snapshot, 'screenshot'
a stable capture, 'ui' the focused window's elements with current
values, 'none' (default) nothing. allow_auth=true overrides the
refusal to click over a system authentication dialog.
| Name | Required | Description | Default |
|---|---|---|---|
| x | No | ||
| y | No | ||
| then | No | none | |
| to_x | No | ||
| to_y | No | ||
| action | Yes | ||
| button | No | left | |
| double | No | ||
| scroll_dx | No | ||
| scroll_dy | No | ||
| allow_auth | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses coordinate system, action behaviors, 'then' chaining, and auth override. However, it lacks explicit information on error handling or side effects like destructive actions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single dense paragraph that front-loads the core concept. It covers actions and special parameters efficiently. Could be more structured (e.g., list actions), but remains clear and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has 11 parameters and an output schema (not shown). Description covers key actions and special parameters well. However, it does not fully explain parameter combinations or error handling, leaving some gaps for complex use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description explains parameters for each action (e.g., x,y for move, button for click). It adds meaning beyond schema titles, though scroll_dx is omitted. Overall, it compensates well for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is for mouse operations in global coordinates, lists all actions (move, click, drag, scroll), and explains special parameters like 'then' and 'allow_auth'. It distinguishes itself from sibling tools like keyboard or desktop.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for mouse input and provides context like coordinate system and action options. It mentions 'allow_auth' for authentication dialogs but does not explicitly contrast with alternative tools or specify when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screenshotA
Capture the focused monitor, a window (window: "active" or an
address from desktop, cheapest for reading one app), or a region
"x,y,WxH". Returns the image (or a file path to read) + JSON metadata
with geometry/scale for pixel→global mapping. scale 0.1-1.0:
optional deliberate downscale, usually leave unset. stable=true
waits (up to 2s) until two consecutive frames match, so a capture
right after an action is not taken mid-animation; metadata gains
stable. Captures are fast JPEG by default; lossless=true returns
PNG for pixel-exact work. Before clicking a small control, follow with
zoom at the estimated point.
| Name | Required | Description | Default |
|---|---|---|---|
| scale | No | ||
| region | No | ||
| stable | No | ||
| window | No | ||
| lossless | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully discloses behavior: default JPEG, lossless PNG option, stable wait up to 2s, scale downscale, and metadata details. Contradicts no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single paragraph, front-loads main verb and resource. Slightly dense with multiple details but no wasted words. Could be split for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers input parameters comprehensively, mentions output format (image/path + JSON metadata), and provides usage advice. Output schema exists, so return details are not needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description explains all 5 parameters: window, region, scale, stable, lossless. Each parameter's purpose and effect are clearly described beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool captures a monitor, window, or region, and specifies output format. It distinguishes from siblings like 'zoom' by mentioning its purpose after screenshot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context on when to use 'window' (cheapest for reading one app) and advises to follow with 'zoom' for clicking small controls. Lacks explicit when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sequenceA
Run an ordered list of actions in ONE call, so a click/type/enter
micro-sequence costs one round-trip instead of several. Each step is
{"op": "pointer"|"keyboard"|"click_ui"|"hypr"|"wait_for", ...that tool's
args}, e.g. [{"op":"pointer","action":"click","x":800,"y":60},
{"op":"keyboard","action":"type","text":"hello","window":"0x.."},
{"op":"keyboard","action":"key","keys":"enter"}]. With stop_on_change
(default) the run stops, best-effort, when it notices a STRUCTURAL
change between steps that the step did not intend: a window opening
(e.g. a dialog), closing, or moving, a switch to an unexpected
workspace, or a seat-taking layer surface (a launcher or on-screen
keyboard) appearing, so later steps do not act on stale state.
Notification popups and bars are not treated as changes. It does NOT catch
a bare focus change, so to type into a specific window reliably give
that keyboard step a window= address (it focuses first). Bounded to 20
steps and ~30s total. then observes the final state ('desktop'
default, 'screenshot', 'ui', 'none').
| Name | Required | Description | Default |
|---|---|---|---|
| then | No | desktop | |
| steps | Yes | ||
| stop_on_change | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description bears full burden. It thoroughly explains the stop_on_change behavior, detailing what structural changes are detected and what are not (e.g., notification popups, bare focus changes). It also specifies limits (20 steps, 30 seconds) and the `then` parameter's role. This provides excellent transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise yet comprehensive. It front-loads the core purpose, then logically explains behavior, parameters, and constraints. Every sentence adds value without redundancy, making it easy to digest.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the presence of an output schema (not shown), the description is largely complete. It covers step structure, stop logic, and the then parameter. Minor omissions like return value details are likely covered by the output schema, keeping completeness high.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains the structure of the steps array (objects with op and args) and the effects of stop_on_change and then. While it does not list every allowed op or argument detail, it references sibling tool args, which adds significant value beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Run an ordered list of actions in ONE call' with a concrete example of a micro-sequence. It clearly distinguishes from sibling tools like pointer, keyboard, etc. by combining multiple actions into a single round-trip.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use (multi-step sequences) and contrasts with individual tool calls. However, it does not explicitly state when NOT to use or provide direct comparisons to alternatives, though the context implies that single actions should use the respective individual tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
uiA
Read a window's accessibility tree (AT-SPI) and return its elements
with GLOBAL click points, so you can target a control by NAME with no
screenshot and no pixel guessing. window is an address from desktop
(default: the focused window). name filters to elements whose
accessible name contains it (case-insensitive); actionable (default)
keeps only interactive roles (buttons, entries, menu items, ...).
Returns [{role, name, x, y, clickable}] where x,y is the click point:
focus the window, then click it with pointer (the window must be
visible to receive the click), or do both in one call with click_ui.
Controls that carry a CURRENT VALUE also
report it: value (text typed into an entry, or a slider/spinner
number), percent for a slider's position, checked for a box or
toggle. Password fields never report contents, and many dropdowns
expose no value at all, so read the screen with screenshot when a
rendered value matters. Not every app exposes a tree (terminals, and
Electron/Chrome without --force-renderer-accessibility, expose little
or nothing); when it does not, fall back to screenshot + zoom.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| window | No | ||
| actionable | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses important behaviors: the tree may not be available for certain apps, password fields never report contents, dropdowns may lack value, and click points are global requiring window focus. It also mentions that the tool returns elements with roles, names, and click points. The description does not contradict annotations (none provided).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is detailed and well-structured, front-loading the main purpose. Each sentence adds value, though it is slightly long. There is no redundancy, and the technical details are clearly presented. It could be slightly more concise, but overall it is effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (accessibility tree with varying behavior), the description is remarkably complete. It explains limitations, fallback strategies, return format, and exceptions. It covers all aspects needed for correct invocation, including implicit output schema description. No output schema is provided, but the description compensates with clear return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It thoroughly explains all three parameters: 'window' (address from desktop, default focused), 'name' (filters by accessible name, case-insensitive), and 'actionable' (default true, keeps interactive roles). It also describes the return structure, adding significant value beyond the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it reads the accessibility tree and returns elements with click points, distinguishing it from sibling tools like screenshot (visual fallback) and pointer (clicking). It specifies the action 'Read a window's accessibility tree' and the resource 'elements...with GLOBAL click points'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on when to use the tool (to target a control by name without screenshot or pixel guessing), and when to fall back (when the tree is not exposed, like in terminals or Electron/Chrome). It also mentions limitations (password fields, dropdowns) and suggests alternatives (click_ui, screenshot). However, it does not explicitly state when NOT to use this tool versus specific siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zoomA
Native-resolution re-capture around a point: the precision step of
the coarse-to-fine loop. Screenshot first, estimate the target's global
x,y, zoom there, re-estimate on the zoomed image (scale ~1.0, so
global = geometry[:2] + image_pixel), then click. size "WxH" in
logical pixels (default 480x360) is clamped to the screen; window
(an address from desktop) clamps to that window instead. The metadata
echoes the requested point back as point; stable=true waits for
the frame to settle first; lossless=true returns PNG.
| Name | Required | Description | Default |
|---|---|---|---|
| x | Yes | ||
| y | Yes | ||
| size | No | ||
| stable | No | ||
| window | No | ||
| lossless | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
In the absence of annotations, the description does a good job disclosing key behaviors: size clamping, window constraint, stable waiting, lossless PNG, and coordinate transformation. However, it omits potential side effects or requirements like permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat dense and technical, mixing procedural steps with parameter details. It is front-loaded with purpose but could be more structured (e.g., bullet points) for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the availability of an output schema, the description covers workflow and parameter behavior well. It lacks error handling or prerequisites but is adequate for a visual interaction tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description adds significant meaning to all parameters: explains 'size' format and clamping, 'window' as address, 'stable' for frame settling, and 'lossless' for format. x/y are implied but not explicitly described, leaving minor ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's role as 'Native-resolution re-capture around a point: the precision step of the coarse-to-fine loop.' It explains the workflow (screenshot, estimate, zoom, re-estimate, click) and distinguishes from sibling tools like 'screenshot' by focusing on refinement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage within a coarse-to-fine loop but does not explicitly state when to use this tool versus alternatives like 'screenshot' or 'desktop'. No 'when-not-to-use' guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
11 tool updates
v0.9.4- Added
desktop - Removed
hypr - Changed
keyboard1 field changed- added
Input schema / properties / allow_authAdded value: +{ + "default": false, + "title": "Allow Auth", + "type": "boolean" +}
- Removed
launch - Added
marks - Added
pointer - Added
screenshot - Added
ui - Removed
use_bind - Removed
wait_for - Added
zoom
4 tool updates
v0.6.0- Removed
pointer - Removed
screenshot - Added
sequence - Removed
zoom
7 tool updates
v0.5.0- Removed
desktop - Changed
hypr3 fields changed- added
Input schema / properties / thenAdded value: +{ + "default": "none", + "title": "Then", + "type": "string" +} - added
Output schema / properties / result / anyOfAdded value: +[ + { + "items": {}, + "type": "array" + }, + { + "type": "string" + } +] - removed
Output schema / properties / result / typeRemoved value: -"string"
- Changed
keyboard4 fields changed- added
Input schema / properties / thenAdded value: +{ + "default": "none", + "title": "Then", + "type": "string" +} - added
Input schema / properties / windowAdded value: +{ + "default": "", + "title": "Window", + "type": "string" +} - added
Output schema / properties / result / anyOfAdded value: +[ + { + "items": {}, + "type": "array" + }, + { + "type": "string" + } +] - removed
Output schema / properties / result / typeRemoved value: -"string"
- Changed
pointer3 fields changed- added
Input schema / properties / thenAdded value: +{ + "default": "none", + "title": "Then", + "type": "string" +} - added
Output schema / properties / result / anyOfAdded value: +[ + { + "items": {}, + "type": "array" + }, + { + "type": "string" + } +] - removed
Output schema / properties / result / typeRemoved value: -"string"
- Changed
screenshot1 field changed- added
Input schema / properties / losslessAdded value: +{ + "default": false, + "title": "Lossless", + "type": "boolean" +}
- Changed
use_bind3 fields changed- added
Input schema / properties / thenAdded value: +{ + "default": "none", + "title": "Then", + "type": "string" +} - added
Output schema / properties / result / anyOfAdded value: +[ + { + "items": {}, + "type": "array" + }, + { + "type": "string" + } +] - removed
Output schema / properties / result / typeRemoved value: -"string"
- Changed
zoom1 field changed- added
Input schema / properties / losslessAdded value: +{ + "default": false, + "title": "Lossless", + "type": "boolean" +}
2 tool updates
v0.4.1- Changed
screenshot1 field changed- added
Input schema / properties / stableAdded value: +{ + "default": false, + "title": "Stable", + "type": "boolean" +}
- Added
zoom
3 tool updates
v0.2.1- Added
binds - Added
use_bind - Added
wait_for
6 tool updates
v0.1.1- First observed
desktop - First observed
hypr - First observed
keyboard - First observed
launch - First observed
pointer - First observed
screenshot
TDQS
Scored across 9 tools
Most tools have clearly distinct purposes, but there is overlap between screenshot, zoom, and marks for visual capture. The missing use_bind tool (referenced in binds) may cause confusion. Overall, nearly all tools are well-differentiated.
All tool names are single lowercase words (desktop, binds, screenshot, etc.) with no mixing of conventions. The naming pattern is simple and fully consistent.
9 tools is well-scoped for a desktop automation server, covering state observation, input, and sequencing without being overwhelming.
Several critical tools are missing but referenced (hypr, use_bind), and there is no explicit tool for common desktop actions like focusing a window or sending compositor commands. This creates dead ends and likely agent failures.
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