IoT MCP Hub

The inspiration for this came from the frustration of managing a smart home where every device demands its own app, and none of them talk to each other. You track weight on one app, log food in another, monitor heart rate in a third - all generating valuable data that stays trapped in silos.

The Model Context Protocol (MCP) provides the perfect foundation to solve this. What if every IoT device in your home could expose an MCP interface? Your smart scale becomes an MCP server. Your fitness tracker becomes an MCP server. Your smart fridge becomes an MCP server.

The Vision

Instead of apps owning your devices, devices become first-class data sources that any authorized application can subscribe to. Your nutrition app doesn’t need to own your scale - it just subscribes to weight measurements via MCP. Your health dashboard correlates heart rate, sleep, and activity without needing accounts across five different ecosystems.

Local AI Revolution

The real magic happens when you combine this with local compute. A Blackwell GPU or even a modest NVIDIA card sitting idle in your home can run surprisingly capable LLMs via your LAN. No data leaves your house, yet you get AI insights that correlate patterns across all your devices:

• “Your weight fluctuates on weekends when your smart fridge shows more snack access and your fitness tracker shows less activity”
• “Your heart rate variability improves on nights when your thermostat maintained 68°F vs 72°F”
• “Your productivity (tracked via keyboard/mouse activity) peaks 2 hours after your smart coffee maker brews”

Decentralized Computing

Why should AI only run in datacenters? Your home network likely has multiple devices with compute power:

• Gaming PC with GPU (idle most of the day)
• NAS with CPU cores to spare
• Smart TV with processing capabilities
• Even modern routers have meaningful compute

IoT MCP Hub orchestrates this distributed compute pool, scheduling AI workloads during idle times and optimizing for energy costs. Run your LLM inference when solar panels are generating excess power. Train personal models overnight when electricity is cheap.

Privacy First

Everything stays local by default. The hub runs on your LAN. Data doesn’t touch the cloud unless you explicitly configure backup. When you grant an app access to device data, it’s temporary, revocable, and auditable. You see exactly which app accessed which sensor at what time.

Technical Challenges

Making this work requires solving some hard problems:

• Most IoT devices aren’t designed to be MCP servers - need adapter layer
• Device discovery and automatic MCP endpoint registration
• Security model for app authorization without becoming a UX nightmare
• Handling spotty IoT connectivity and offline operation
• Managing compute workload distribution across heterogeneous hardware

But the potential payoff is huge: true smart home intelligence without sacrificing privacy or control.