Video by Open Data Science and AI Conference via YouTube
MCP’s Biggest Rewrite: Statelessness, Tool Design, and Agent Skills
The Model Context Protocol (MCP) is evolving toward a stateless architecture designed to make AI agent systems easier to scale and operate in production.
In this episode, host Sheamus McGovern speaks with Kyle Stratis, founder of Stratis Data Labs and author of AI Agents with MCP, about the latest MCP changes, MCP clients and servers, tool design, state management, security, and why Agent Skills and MCP are complementary rather than competing approaches.
Learn what these changes mean for building more scalable, reliable, and production-ready AI agents with MCP.
00:00 MCP Servers vs. Agent Skills: What’s the Difference?
00:47 AI Agents with MCP: Introduction with Kyle Stratis
02:25 From Software Engineering to AI Agent Development
05:51 AI Agent Evals, Guardrails & Common Engineering Mistakes
08:45 Building AI Agents with the Model Context Protocol (MCP)
12:36 AI Agents vs. Agentic Workflows: Key Differences
16:03 MCP Architecture: Connecting AI Agents, Tools & Data
18:23 MCP Clients Explained: The Overlooked Part of Model Context Protocol
21:06 Stateless MCP Architecture: What Changed in the Latest MCP Spec
24:44 AI Agent Memory & State Management with MCP
27:08 Error Handling, Retries & Long-Running AI Agent Workflows
29:04 MCP Security: Clients, Servers & Trust Boundaries
33:01 MCP Tool Design: How Many Tools Should an AI Agent Have?
36:18 MCP vs. REST APIs: Why Wrapping API Endpoints Doesn’t Work
40:45 MCP Tool Discovery, Tool Selection & Context Management
44:26 Is MCP Dead? MCP vs. Agent Skills Explained
49:25 MCP + Agent Skills: How They Work Together
53:18 How to Design Better MCP Tools for AI Agents
57:13 The Future of MCP & AI Agent Development
59:17 Final Advice for Building Production AI Agents
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