JARVIS MCP Server
Enables macOS control, web scraping, news fetching, and workspace automation through an MCP server.
README
JARVIS — Advanced AI Virtual Assistant
<img width="2926" height="1672" alt="image" src="https://github.com/user-attachments/assets/7b39c08f-5014-403c-a2a1-bc61a91a0d51" />
JARVIS is a futuristic, highly capable virtual assistant built with Electron and Python. It features a stunning "Stark Industries" HUD interface and leverages a multi-LLM architecture to provide real-time intelligence, system control, and automation.
🚀 System Architecture
1. Frontend (Electron HUD)
- Visuals: A premium, glassmorphism-inspired "Iron Man" HUD with real-time audio visualizers (Arc Reactor), system stat monitors, and cinematic animations.
- Speech Stack:
- STT (Speech-to-Text): Supports Vosk (Local/Offline), Groq Whisper, and Sarvam.
- TTS (Text-to-Speech): Integrated with Sarvam (Bulbul v3), Groq Orpheus, and native Web Speech API.
- Interaction: Features a "Clap to Wake" cinematic sequence and always-on voice listening.
2. Backend (Python MCP Server)
- Model Context Protocol (MCP): A dedicated Python server (
mcp_server.py) provides JARVIS with "hands" to interact with the OS. - Capabilities:
- macOS Control: Application launching, volume control, screen locking, screenshots, and system info.
- Web Intelligence: Web scraping for summarization, news fetching via RSS, and advanced Chrome control via AppleScript.
- Workspace Automation: One-command setup for 'Coding', 'Research', 'Relax', and 'Web Dev' modes.
3. Intelligence Layer
- Groq (Llama 3.3/3.1): Used for sub-500ms intent detection and tool routing.
- Gemini 3 Flash: The primary conversational brain, providing high-intelligence responses with minimal latency.
- OpenRouter (Gemma 4): Fallback engine and advanced reasoning specialist.
🛠️ Setup & Installation
Prerequisites
- macOS (Optimized for Mac; some features may not work on Windows).
- Node.js (v18+)
- Python 3.10+
1. Clone & Install Dependencies
# Install JS dependencies
npm install
# Install Python dependencies
pip install fastmcp psutil feedparser requests beautifulsoup4
2. Environment Configuration
Create a .env file in the root directory and add your API keys:
GROQ_API_KEY=gsk_...
GEMINI_API_KEY=AIza...
OPENROUTER_API_KEY=sk-or-...
SARVAM_API_KEY=your_sarvam_key
3. Run the App
npm start
Note: On first run, it will download the Vosk model (~40MB) if not present in the models/ folder.
📦 Exporting & Distribution
To package the application into a standalone macOS .app or .dmg file:
npm run build
The build output will be located in the dist/ folder.
🔧 Core Features
- "Hey JARVIS": Start speaking anytime to interact.
- "Clap to Wake": A loud clap wakes JARVIS up with a cinematic intro sequence.
- Workspace Modes: Say "Setup coding workspace" to automatically open Terminal, VS Code, and relevant browser tabs.
- System Telemetry: Real-time monitoring of CPU, RAM, and Network on the HUD.
- Memory System: JARVIS remembers facts you tell it about yourself (name, profession, etc.) across sessions.
👨💻 Created By
Akshat Singh — Tech Creator & Developer. Designed to bring the future to the present.
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