Junghwan Park (9bow) is an AAIF Ambassador, a PyTorch Ambassador, and the lead maintainer of the PyTorch Korea User Group (PyTorchKR). A Korean version of this post is available here. TL;DR: MCP 7-28 is less about new features than simplifying and hardening the protocol for production use. The release deprecates Roots, Sampling, and protocol-level Logging,…
The Model Context Protocol (MCP) has quietly become one of the most important standards in agentic AI. In a little over a year, it went from a promising idea to the default way AI applications connect to external tools, data sources, and services. If you're building AI assistants or agentic systems today, you're almost certainly…
Deploying Model Context Protocol architectures within enterprise banking environments that require strict security patterns like out-of-band personal data isolation, distributed tracking, and decoupled service guards.
Large language models don't “see” interfaces, buttons, or navigate visually. They live and die by language, and that fact arguably makes LLMs one of the most consequential accessibility developments in the history of computing, not because they were designed for it, but because they can't help it.
I’m a big fan and user of both MCP and Agent Skills, so when I learned there’s a Skills Over MCP working group (WG) that’s looking into integrating Skills with MCP, I had no choice but to lurk.
MCP servers can call tools, but clients cannot expose them back to servers. Jerome Swannack, one of MCP's original architects, explains how client-to-server tool sharing could unlock more modular, pluggable MCP systems.
The Agentic AI Foundation held its first event in India last week in Bengaluru and the community came out in force for a dose of agentic truthtelling. The 57 sessions were packed and the energy in the room was strong. The lessons learned could fill many notebooks but we saved you the time of watching…
Many MCP failures come from weak security architecture or lack of operational guardrails for what the agents can and cannot do. In her talk, Cecilia Liu of Docker examined two real MCP “horror stories” and walked through how to build a three-layer control plane for MCP: an MCP gateway that enforces policy, identity, and audit…
Most MCP tools are designed for programmers. Agents are not programmers. Building tools around tasks and intents, then obsessing over description quality, produces dramatically better selection accuracy and fewer failures at runtime.
Getting an AI agent to work is a different problem from getting it to run in production without someone watching it. Datadog built its first hundred agents across SRE, code generation, and security investigation. The hardest parts had nothing to do with model quality.
The next MCP specification release candidate is a big one. The headline change is that MCP is becoming stateless at the protocol layer, but the more useful story is what that does for people building agentic systems in practice.
When Prime Video's engineering teams started building with agents, they followed a pattern that made obvious sense. Give the agent access to the same tools the engineers have. Stand up MCP servers. Let the agent figure out what to call.
Almost all of MCP's current adoption is happening in cloud environments, on large models with generous compute budgets.Manufacturing floors, medical devices, vehicles, smartphones,are where the next wave of AI deployments is headed.
Nick Cooper, Member of Technical Staff at OpenAI and a contributor to the MCP ecosystem, shared that he'd been thinking about this debate for a year, but stopped short of picking a side: "As for whether or not one is better than the other, it's not a decision between one or the other. You clearly…
The operational scale of MCP is significant: Google Cloud maintains the reliability for its MCP servers supporting AlloyDB, Spanner, Cloud SQL, Bigtable, and Firestore. The team's open source MCP Toolbox for Databases has seen rapid adoption, surpassing 13,000 GitHub stars and managing over 20 million tool calls across 40+ databases in the last month.
As we integrate AI into more workflows, a common question is: how do AI systems find each other and related artifacts? AI Catalog addresses this by providing a shared, trusted discovery layer that gives different kinds of AI artifacts a common place to be found.
Red Hat engineers Wesley Chun and Calum Murray opened with a problem that a lot of MCP server builders have already run into: your code passes every test, the agent still fails, and you have no idea where things went wrong. Their answer is MCP Checker, an open source evaluation framework built specifically for the…
MCP Apps exist because displaying data in chat is wasteful. Take the case of a simple request like “What’s the weather today in New York?” When a user asks for it, the model gets JSON from the tool, converts it to text, and sends it back, token by token. The user reads a wall of…
For the better part of a decade, the "Microservice" has been the undisputed unit of value in software engineering. We spent years perfecting the art of the stateless, predictable container, wrapping it in layers of GitOps and CI/CD pipelines to ensure that what we deployed yesterday is exactly what runs tomorrow. But now we are…
At the MCP Dev Summit 2026, Pedro Rodrigues, AI Tooling Engineer at Supabase, challenged a growing rift in the developer community. "People keep asking if they should build an MCP server or use Agent Skills," Rodrigues noted. "But that’s the wrong frame. They aren't competitors; they are the two halves of a working agent."
At the MCP Dev Summit, Aaron Wang, a software engineer at Duolingo, shared how the company went from a handful of engineers experimenting with MCP, to 250 weekly active users (about 30% of the company) in under six months. This is how they did it.
Over the past year, I’ve spent a lot of time in the trickiest parts of Model Context Protocol: authorization flows, security, and the messy reality of how clients behave in the wild. MCP is exciting, and for good reason: it gives AI systems a common way to connect to the rest of the software world.…
A deep dive into Alex Hancock's talk at MCP Dev Summit North America 2026 Alex Hancock opened his talk at MCP Dev Summit with a couple of questions for the audience. “How many people in the room have an idea for how MCP or agents could work better?” Nearly every hand went up. “Of those,…
At the MCP Dev Summit NA in New York on April 2–3, the Agentic AI Foundation brought together a roundtable with the MCP Project maintainers: Clare Liguori of AWS, David Soria Parra of Anthropic, Caitie McCaffrey of Microsoft, and Nick Cooper of OpenAI, with Stephen O’Grady of RedMonk moderating. The conversation covered the state of…
New York City · Agentic AI Foundation & Linux Foundation The biggest ideas and sharpest insights from MCP Dev Summit North America: where the builders, contributors, and organizations shaping the future of agentic AI came together in New York City. With 1,200 people at this year’s MCP Dev Summit (double from the previous one), it’s…
New York City event will explore how open standards and shared infrastructure enable secure, scalable AI agents in production Summary The Agentic AI Foundation today announced the conference session lineup for MCP Dev Summit North America, which will be held in New York City on April 2-3, 2026. Keynotes and Day 0 workshops will be…
MCP joins the Linux Foundation: What this means for developers building the next era of AI tools and agents
Anthropic’s Model Context Protocol is only a year old, but has quickly become the default for large language models (LLMs) to access data, tools and services in agentic loops. Now, Anthropic is moving it into a newly founded open source foundation under the Linux Foundation: the Agentic AI Foundation (AAIF).
Today, we’re donating the Model Context Protocol (MCP) to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, co-founded by Anthropic, Block and OpenAI, with support from Google, Microsoft, Amazon Web Services (AWS), Cloudflare, and Bloomberg.
