MCP Dev Summit North America

Most MCP servers work like snapshots - ask for context, get a response, done. But what happens when your code changes while the AI is working? Or system metrics spike during deployment? Your agent has stale data.

I've been building streaming MCP servers that push live updates to AI agents. Think file watchers notifying code changes, system monitors streaming metrics, or database triggers sending updates as they happen.

I'll walk through building a live log monitoring MCP server from scratch. We'll extend the basic MCP protocol to handle streaming data using WebSockets, implement event subscriptions, and keep agents synchronized with rapidly changing data.

The demo shows an AI agent monitoring application logs in real-time, detecting anomalies and suggesting fixes as errors occur - not minutes later when someone checks the logs.

This isn't theoretical - I'm using similar patterns in production for DevOps monitoring and trading systems. I'll share the code, discuss gotchas, and show how streaming MCP opens up new use cases.

You'll leave with practical patterns for building reactive MCP servers that keep your AI agents always current.