Emuluxe for AI Agents.
The MCP Server.
The Model Context Protocol (MCP) server allows AI models to programmatically interact with the Emuluxe engine. Give your agents the power to run high-fidelity mobile simulations, inspect UI, and fix issues on real device hardware.
"Claude, can you verify the checkout button alignment on an iPhone 15 Pro and fix the padding if it overflows?"
First-Class Model Support
Connect the Emuluxe MCP server to the world's most advanced AI assistants and autonomous coding environments.
Autonomous Simulation Capabilities
When LLMs can control high-fidelity mobile simulation, the nature of QA changes. Bridge the gap between reasoning, rendering, and AI-powered debugging.
Agentic Hardware Control
Allow your AI agents (Claude, Gemini, GPT) to programmatically trigger device rotations, network throttling, and high-fidelity hardware signatures.
Visual Context Injection
MCP tools enable models to 'see' the current simulation state via structured DOM snapshots, accessibility tree data, and AI-ready context.
Automated QA Agents
Build autonomous agents that run visual regression tests and report bugs with deep-linked simulation URLs for manual verification.
Open Standard.
Infinite Scale.
By adopting the Model Context Protocol, Emuluxe enables a standardized interface for any AI model to discover and invoke our high-fidelity mobile simulation tools. No custom API glue code required.
Standardized MCP Tools
A clean set of tool definitions for launching, rotating, and inspecting high-fidelity device simulations.
Visual State Sync
Send visual snapshots to multimodal models for real-time layout auditing, AI-powered debugging, and verification.
Local & Cloud Bridging
Agents can interact with both your local dev server and our high-fidelity cloud hardware foundry.
npx @emuluxe/mcp-server --port 8080
# Define tools exported to model
tools:
- name: emuluxe_launch_sim
desc: Launch hardware simulation
- name: emuluxe_set_device
desc: Switch active hardware profile
- name: emuluxe_capture_audit
desc: Get AI-driven layout insights