Argus Automation
Argus Automation β SOTA desktop automation for AI agents. Works with Claude Code, Codex, and OpenClaw.
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Argus Automation
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SOTA desktop automation for AI agents.
Works with Claude Code, Codex, and OpenClaw.
Argus (αΌΟΞ³ΞΏΟ Ξ Ξ±Ξ½ΟΟΟΞ·Ο) β the hundred-eyed giant of Greek mythology, the all-seeing guardian who never sleeps. We named this project Argus because it sees your entire desktop through screenshots and controls it with surgical precision β just as the mythological guardian watched over everything entrusted to him.
Why Argus? Every other desktop MCP gives the AI a screenshot and a mouse β then hopes for the best. Argus is different: it's a direct port of Anthropic's own Chicago MCP, the production system behind Claude Code's built-in desktop control. We took the 6,300 lines of battle-tested logic β the security gates, the token budgeting, the batch execution engine β and swapped only the native layer for Windows. The result is arguably the most architecturally advanced computer-use implementation available today, and it's open source.
Two Fundamentally Different Design Philosophies
Every other MCP takes the "hand the model a hammer" approach β provide screenshot + click + type as atomic tools, then hope the model figures out the rest. Every step is: screenshot β look β decide β act β repeat.
Argus takes a fundamentally different approach: model desktop automation as a stateful, governed session β with layered security, token budgeting, and batch execution. The gap is enormous.
Comparison 1: Tool Design β Flat Primitives vs Layered Architecture
CursorTouch (5,000 stars) tools:
Click, Type, Scroll, Move, Shortcut, Screenshot, App, Shell...
Each tool is an independent atomic operation with no context relationship. The model must screenshot β look β decide β act at every single step.
Argus's layered tool design:
Session Layer: request_access, list_granted_applications
Vision Layer: screenshot, zoom
Precision Layer: left_click, double_click, triple_click, right_click,
middle_click, left_mouse_down, left_mouse_up
Input Layer: type, key, hold_key
Efficiency Layer: computer_batch (N actions β 1 API call)
Navigation Layer: open_application, switch_display
State Query Layer: cursor_position, read_clipboard, write_clipboard
Wait Layer: wait
24 top-level tools + 16 batch action types. The essence of this layered design: let the model think at the right abstraction level, instead of starting from pixels every time.
Comparison 2: "Use APIs When You Can" β The Most Underrated Design Principle
This is the most underrated design point. Other MCPs force the model to perceive everything through vision. Argus's principle: if information can be retrieved via a structured API, never waste vision tokens on it. Screenshots are reserved for when you genuinely need visual understanding.
| Task | Other MCPs | Argus | What You Save |
|---|---|---|---|
| Know which apps exist | Screenshot β model reads taskbar | listInstalledApps() β structured data | 1 screenshot + 1 vision inference |
| Open an application | Screenshot β find icon β click | open_application("Excel") β direct API | 2-3 screenshots + multiple clicks |
| Know which app is focused | Screenshot β model reads title bar | getFrontmostApp() β returns bundleId | 1 screenshot + inference |
| Know cursor position | Screenshot β model guesses | cursor_position β exact coordinates | 1 screenshot |
| Read clipboard | Ctrl+V into Notepad β screenshot β read | read_clipboard β returns text | Multiple actions + 2 screenshots |
| Switch monitor | Screenshot β wrong one β trial and error | switch_display("Dell U2720Q") | Trial-and-error loop |
| Read small text | Model squints at compressed screenshot | zoom β high-res regional crop | Misclick costs |
Each avoided screenshot saves ~1,500 vision tokens and 3-5 seconds of latency.
Comparison 3: computer_batch β The Only Batch Execution Engine
This is a capability no competitor has. Here's how big the gap is:
Other MCPs performing "click field β type text β press Enter":
Call 1: screenshot β model receives image β inference β next step
Call 2: click(100, 200) β model receives OK β inference β next step
Call 3: type("hello") β model receives OK β inference β next step
Call 4: key("Return") β model receives OK β inference β next step
Call 5: screenshot β model confirms result
= 5 API round-trips Γ 3-8 seconds = 15-40 seconds
Argus doing the same thing:
Call 1: screenshot
Call 2: computer_batch([
{ action: "left_click", coordinate: [100, 200] },
{ action: "type", text: "hello" },
{ action: "key", text: "Return" },
{ action: "screenshot" }
])
= 2 API round-trips = 6-16 seconds
60% less latency and tokens. And every action inside the batch still gets a frontmost-app security check β not blind execution.
Comparison 4: Security Model β Production-Grade vs None
| Security Dimension | CursorTouch (5k stars) | MCPControl (306 stars) | Argus |
|---|---|---|---|
| App-level permissions | No | No | 3-tier (read/click/full) |
| Frontmost app gate | No (can click any window) | No | Checked before every action |
| Dangerous key blocking | No | No | Alt+F4, Win+L, Ctrl+Alt+Del |
| Click target validation | No | No | 9Γ9 pixel staleness guard |
| Clipboard isolation | No | No | Stash/restore for click-tier apps |
| App deny-list | No | No | Browsersβread-only, Terminalsβclick-only |
CursorTouch's README literally says "POTENTIALLY DANGEROUS". Argus's security model is designed for commercial products β Anthropic's Cowork and desktop app both use the same architecture.
Head-to-Head Summary
| Capability | Argus | CursorTouch (5k stars) | MCPControl (306 stars) | domdomegg (176 stars) | sbroenne (24 stars) |
|---|---|---|---|---|---|
| Batch Execution | Yes | No | No | No | No |
| Token Budget Optimization | Yes | No | No | No | No |
| 3-Tier App Permissions | Yes | No | No | No | No |
| Frontmost App Gate | Yes | No | No | No | No |
| Dangerous Key Blocking | Yes | No | No | No | No |
| Structured APIs (no-screenshot info) | Yes | Partial | Partial | No | Yes |
| Zoom (high-res detail crop) | Yes | No | No | No | No |
| Multi-Display Switch | Yes | No | No | No | No |
| Same Schema as Claude Code Built-in | Yes | No | No | Close | No |
| Anthropic Upstream Code Reused | 6,300+ lines | 0 | 0 | 0 | 0 |
| Tool Count | 24 | 19 | 12 | 6 | 10 |
| Language | TypeScript | Python | TypeScript | TypeScript | C# |
Quick Start
Prerequisites
- Node.js 18+
- Windows 10/11
- Visual Studio Build Tools (for robotjs)
Install
git clone https://github.com/storyweaver/argus-automation.git
cd argus-automation
npm install
npm run build
Configure in Claude Code
Add to your project's .mcp.json:
{
"mcpServers": {
"argus": {
"command": "node",
"args": ["C:/path/to/argus-automation/dist/server-mcp.js"]
}
}
}
Restart Claude Code. You'll see 24 new tools prefixed with mcp__argus__.
Configure in Codex on Windows
Argus also ships a Codex-specific MCP entry point that keeps the Claude Code tool surface unchanged:
[mcp_servers.argus-codex]
command = "node"
args = ["D:\\claude-d\\gui-automation\\argus-automation\\dist\\server-codex-mcp.js"]
cwd = "D:\\claude-d\\gui-automation\\argus-automation"
tool_timeout_sec = 120
This local server does not call the OpenAI API and does not require
OPENAI_API_KEY; Codex invokes it as a local MCP tool server. See
docs/CODEX_WINDOWS_CUA.md for the action schema
and plugin packaging notes.
Test
npm test # 75 tests (unit + integration)
npm run test:unit # Unit tests only
Architecture
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β Upstream Layer β 6,300+ lines from Anthropic's Chicago MCP β
β (only 1 line changed) β
β β
β toolCalls.ts (3,649 lines) β security gates + tool dispatch β
β mcpServer.ts β Server factory + session binding β
β tools.ts β 24 tool schema definitions β
β types.ts β complete type system β
β keyBlocklist.ts β dangerous key interception (win32 branch) β
β pixelCompare.ts β 9Γ9 staleness detection β
β imageResize.ts β token budget algorithm β
ββββββββββββββββββββββββββββ¬βββββββββββββββββββββββββββββββββββββββββββ
β ComputerExecutor interface
βΌ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Windows Native Layer β ~400 lines, new code β
β β
β screen.ts β node-screenshots + sharp (DXGI capture, JPEG, resize) β
β input.ts β robotjs (SendInput mouse/keyboard) β
β window.ts β koffi + Win32 API (window management) β
β clipboard.ts β PowerShell Get/Set-Clipboard β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Tech Stack
Each library is the Windows equivalent of what the macOS version uses:
| Module | macOS (Chicago MCP) | Windows (Argus) | Role |
|---|---|---|---|
| Screenshot | SCContentFilter | node-screenshots (DXGI) | Screen capture |
| Input | enigo (Rust) | robotjs (SendInput) | Mouse & keyboard |
| Window Mgmt | Swift + NSWorkspace | koffi + Win32 API | Window control |
| Image Processing | Sharp | Sharp | JPEG compress + resize |
| MCP Framework | @modelcontextprotocol/sdk | @modelcontextprotocol/sdk | MCP protocol |
The 24 Tools
| Category | Tools |
|---|---|
| Session | request_access, list_granted_applications |
| Vision | screenshot, zoom |
| Mouse Click | left_click, double_click, triple_click, right_click, middle_click |
| Mouse Control | mouse_move, left_click_drag, left_mouse_down, left_mouse_up, cursor_position |
| Scroll | scroll |
| Keyboard | type, key, hold_key |
| Clipboard | read_clipboard, write_clipboard |
| App/Display | open_application, switch_display |
| Batch + Wait | computer_batch, wait |
Security Model
Three-tier per-app permissions β the only MCP server with this level of access control:
| Tier | Screenshot | Click | Type/Paste |
|---|---|---|---|
| read (browsers, trading) | Yes | No | No |
| click (terminals, IDEs) | Yes | Left-click only | No |
| full (everything else) | Yes | Yes | Yes |
Plus: dangerous key blocking, frontmost app gate on every action, session-scoped grants.
Logs
All tool calls logged to:
%LOCALAPPDATA%\argus-automation\logs\mcp-YYYY-MM-DD.log
Known Limitations
- CJK text input: Use
write_clipboard+key("ctrl+v")for non-ASCII text - App discovery: Currently returns running apps only (registry scan planned)
- Pixel validation: Disabled on Windows (async sharp can't satisfy sync interface)
- hideBeforeAction: Disabled (minimizing breaks WebView2 child processes)
License
MIT
Acknowledgements
Built with Claude
This entire project β architecture design, 6,300+ lines of upstream code analysis, Windows native layer implementation, 70 tests, and this README β was built in a single Claude Code session powered by Claude Opus 4.6. The AI agent analyzed Anthropic's Chicago MCP source code, identified the platform-agnostic abstraction boundary (the ComputerExecutor interface), reconstructed missing type definitions from usage patterns, implemented the Windows native layer from scratch, and wrote comprehensive tests β all in one continuous session.
Chicago MCP
The upstream code in src/upstream/ is from Anthropic's @ant/computer-use-mcp package (Chicago MCP), extracted from Claude Code v2.1.88. This is Anthropic's production desktop-control architecture; we ported only the native layer to Windows. The architectural brilliance of separating platform-agnostic logic from native implementation is entirely Anthropic's design.
