In this episode, we're joined by David Soria Parra, Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP), to explore the biggest evolution of the protocol since its launch, and why MCP is becoming the foundation for the next generation of AI agents.

We discuss why MCP is moving toward stateless communication, what developers misunderstand about state, sessions, and transport layers, and how lessons from real-world deployments at massive scale have shaped the protocol's future. We also dive into MCP v2, SDK migrations, protocol design, extension architecture, governance, developer experience, and how Anthropic thinks about balancing simplicity with long-term flexibility.

Along the way, we explore progressive disclosure, tool search, programmatic tool calling, context bloat, forward compatibility, long-running AI tasks, protocol evolution, open-source governance, observability, and why the future of AI infrastructure will depend on designing protocols that can evolve without breaking the ecosystem.

Timestamps:

[00:00] Introduction

[01:59] Why MCP Had to Become Stateless

[04:28] The Tradeoffs of Stateless Design

[06:13] What We Learned About Agent State

[08:04] Sessions, Models & Implicit State

[09:33] Migrating to MCP v2

[12:19] Lessons from HTTP & Open Source Standards

[18:16] Shipping Fast Without Breaking Everything

[20:35] The Future Complexity of MCP

[22:44] Core Features vs Extensions

[26:47] Progressive Disclosure Explained

[28:16] Solving Context Bloat

[30:50] Why Tool Search Beats Progressive Disclosure

[32:10] The Biggest MCP Anti-Pattern

[34:25] Designing for Forward Compatibility

[38:41] Why "Tasks" Matter

[40:53] JSON, Tokens & Better Tool Calling

[44:44] Observability & Tracing AI Agents

[47:34] Will MCP Ever Be Finished?

[50:22] What's Next for MCP