MCP Goes Stateless

MCP Goes Stateless

Author: Demetrios July 27, 2026 Duration: 52:27

David Soria Parra is an Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP). We 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


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

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