There's a running argument in the AI tooling world right now do you need MCP servers, or can you just use Skills? I've been on both sides of this. At Harness, I've built Claude Skills for DevOps workflows and worked with our MCP server that wraps the entire platform. And the honest answer is: it depends, but most people are picking the wrong one for the wrong job.

In this talk I'll break down where the boundary actually is. Skills are great when the knowledge is stable conventions, workflow logic, best practices. MCP servers earn their keep when you need live data, real-time API calls, or actions with side effects. The interesting part is what happens when you layer them: a Skill that knows how to debug a failed deployment, calling an MCP server that pulls live pipeline logs and execution data.

I'll walk through real examples I've built skills that started as simple "SKILL.md" files and grew into MCP-backed workflows, and cases where I over-engineered an MCP server when a markdown file would've done the job. If you're building agents and trying to figure out the right architecture, this talk should save you some wrong turns.