LLMs and MCP have fundamentally changed my work as a solo data practitioner. I spend less time writing code, but throughput and durability have improved dramatically. MCP servers now act as core tooling for LLMs in my workflows, but have also started to become data products in their own right. 

This is a practical talk that will focus on my experience of shifting to this way of thinking and working, including what has worked for me and the realities of where there are still rough edges.

MCP are poised to be the connective tissue between large‑language models and the data they need. Despite this, many projects still stall at the prototype stage. This talk distills what actually works in production, what still breaks, and why. We’ll look at the real environments where MCP is thriving, the hurdles teams hit when they move to productize it. Finally, we’ll share a view of the road ahead as context becomes a first‑class product surface.