OmniMCP

OmniMCP provides rich UI context and interaction capabilities to AI models through Model Context Protocol (MCP) and microsoft/OmniParser. It focuses on enabling deep understanding of user interfaces through visual analysis, structured planning, and precise interaction execution.

• Visual Perception: Understands UI elements using OmniParser.
• LLM Planning: Plans next actions based on goal, history, and visual state.
• Agent Executor: Orchestrates the perceive-plan-act loop (omnimcp/agent_executor.py).
• Action Execution: Controls mouse/keyboard via pynput (omnimcp/input.py).
• CLI Interface: Simple entry point (cli.py) for running tasks.
• Auto-Deployment: Optional OmniParser server deployment to AWS EC2 with auto-shutdown.
• Debugging: Generates timestamped visual logs per step.

cli.py uses AgentExecutor to run a perceive-plan-act loop. It captures the screen (VisualState), plans using an LLM (core.plan_action_for_ui), and executes actions (InputController).

• Real Action (Calculator): python cli.py opens Calculator and computes 5*9.
• Synthetic UI (Login): python demo_synthetic.py uses generated images (no real I/O). (Note: Pending refactor to use AgentExecutor).

• Python >=3.10, <3.13
• uv installed (pip install uv)
• Linux Runtime Requirement: Requires an active graphical session (X11/Wayland) for pynput. May need system libraries (libx11-dev, etc.) - see pynput docs.

(macOS display scaling dependencies are handled automatically during installation).

Requires AWS credentials in .env (see .env.example). Warning: Creates AWS resources (EC2, Lambda, etc.) incurring costs. Use python -m omnimcp.omniparser.server stop to clean up.

AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY
AWS_SECRET_ACCESS_KEY=YOUR_SECRET_KEY
ANTHROPIC_API_KEY=YOUR_ANTHROPIC_KEY
# OMNIPARSER_URL=http://... # Optional: Skip auto-deploy

git clone [https://github.com/OpenAdaptAI/OmniMCP.git](https://github.com/OpenAdaptAI/OmniMCP.git)
cd OmniMCP
./install.sh # Creates .venv, installs deps incl. test extras
cp .env.example .env
# Edit .env with your keys
# Activate: source .venv/bin/activate (Linux/macOS) or relevant Windows command

Ensure environment is activated and .env is configured.

# Run default goal (Calculator task)
python cli.py

# Run custom goal
python cli.py --goal "Your goal here"

# See options
python cli.py --help

Debug outputs are saved in runs/<timestamp>/.

Note on MCP Server: An experimental MCP server (OmniMCP class in omnimcp/mcp_server.py) exists but is separate from the primary cli.py/AgentExecutor workflow.

1. CLI (cli.py) - Entry point, setup, starts Executor.
2. Agent Executor (omnimcp/agent_executor.py) - Orchestrates loop, manages state/artifacts.
3. Visual State Manager (omnimcp/visual_state.py) - Perception (screenshot, calls parser).
4. OmniParser Client & Deploy (omnimcp/omniparser/) - Manages OmniParser server communication/deployment.
5. LLM Planner (omnimcp/core.py) - Generates action plan.
6. Input Controller (omnimcp/input.py) - Executes actions (mouse/keyboard).
7. (Optional) MCP Server (omnimcp/mcp_server.py) - Experimental MCP interface.

# Setup (if not done): ./install.sh
# Activate env: source .venv/bin/activate (or similar)
# Format/Lint: uv run ruff format . && uv run ruff check . --fix
# Run tests: uv run pytest tests/

Running python cli.py saves timestamped runs in runs/, including:

• step_N_state_raw.png
• step_N_state_parsed.png (with element boxes)
• step_N_action_highlight.png (with action highlight)
• final_state.png

Detailed logs are in logs/run_YYYY-MM-DD_HH-mm-ss.log (LOG_LEVEL=DEBUG in .env recommended).

# --- Initialization & Auto-Deploy ---
2025-MM-DD HH:MM:SS | INFO     | omnimcp.omniparser.client:... - No server_url provided, attempting discovery/deployment...
2025-MM-DD HH:MM:SS | INFO     | omnimcp.omniparser.server:... - Creating new EC2 instance...
2025-MM-DD HH:MM:SS | SUCCESS  | omnimcp.omniparser.server:... - Instance i-... is running. Public IP: ...
2025-MM-DD HH:MM:SS | INFO     | omnimcp.omniparser.server:... - Setting up auto-shutdown infrastructure...
2025-MM-DD HH:MM:SS | SUCCESS  | omnimcp.omniparser.server:... - Auto-shutdown infrastructure setup completed...
... (SSH connection, Docker setup) ...
2025-MM-DD HH:MM:SS | SUCCESS  | omnimcp.omniparser.client:... - Auto-deployment successful. Server URL: http://...
... (Agent Executor Init) ...

# --- Agent Execution Loop Example Step ---
2025-MM-DD HH:MM:SS | INFO     | omnimcp.agent_executor:run:... - --- Step N/10 ---
2025-MM-DD HH:MM:SS | DEBUG    | omnimcp.agent_executor:run:... - Perceiving current screen state...
2025-MM-DD HH:MM:SS | INFO     | omnimcp.visual_state:update:... - VisualState update complete. Found X elements. Took Y.YYs.
2025-MM-DD HH:MM:SS | INFO     | omnimcp.agent_executor:run:... - Perceived state with X elements.
... (Save artifacts) ...
2025-MM-DD HH:MM:SS | DEBUG    | omnimcp.agent_executor:run:... - Planning next action...
... (LLM Call) ...
2025-MM-DD HH:MM:SS | INFO     | omnimcp.agent_executor:run:... - LLM Plan: Action=..., TargetID=..., GoalComplete=False
2025-MM-DD HH:MM:SS | DEBUG    | omnimcp.agent_executor:run:... - Added to history: Step N: Planned action ...
2025-MM-DD HH:MM:SS | INFO     | omnimcp.agent_executor:run:... - Executing action: ...
2025-MM-DD HH:MM:SS | SUCCESS  | omnimcp.agent_executor:run:... - Action executed successfully.
2025-MM-DD HH:MM:SS | DEBUG    | omnimcp.agent_executor:run:... - Step N duration: Z.ZZs
... (Loop continues or finishes) ...

Key limitations & future work areas:

• Performance: Reduce OmniParser latency (explore local models, caching, etc.) and optimize state management (avoid full re-parse).
• Robustness: Improve LLM planning reliability (prompts, techniques like ReAct), add action verification/error recovery, enhance element targeting.
• Target API/Architecture: Evolve towards a higher-level declarative API (e.g., @omni.publish style) and potentially integrate loop logic with the experimental MCP Server (OmniMCP class).
• Consistency: Refactor demo_synthetic.py to use AgentExecutor.
• Features: Expand action space (drag/drop, hover).
• Testing: Add E2E tests, broaden cross-platform validation, define evaluation metrics.
• Research: Explore fine-tuning, process graphs (RAG), framework integration.

Core loop via cli.py/AgentExecutor is functional for basic tasks. Performance and robustness need significant improvement. MCP integration is experimental.

1. Fork repository
2. Create feature branch
3. Implement changes & add tests
4. Ensure checks pass (uv run ruff format ., uv run ruff check . --fix, uv run pytest tests/)
5. Submit pull request

MIT License

• Issues: GitHub Issues
• Questions: Discussions
• Security: security@openadapt.ai