One Year of MCP — with David Soria Parria and AAIF leads from OpenAI, Goose, Linux Foundation

One year ago, Anthropic launched the *Model Context Protocol (MCP)*—a simple, open standard to connect AI applications to the data and tools they need. Today, MCP has exploded from a local-only experiment into the de facto protocol for agentic systems, adopted by OpenAI, Microsoft, Google, Block, and hundreds of enterprises building internal agents at scale. And now, MCP is joining the newly formed *Agentic AI Foundation (AAIF)* under the Linux Foundation, alongside Block's *Goose* coding agent, with founding members spanning the biggest names in AI and cloud infrastructure. We sat down with *David Soria Parra* (MCP lead, Anthropic), *Nick Cooper* (OpenAI), *Brad Howes* (Block / Goose), and *Jim Zemlin* (Linux Foundation CEO) to dig into the one-year journey of MCP—from Thanksgiving hacking sessions and the first remote authentication spec to long-running tasks, MCP Apps, and the rise of agent-to-agent communication—and the behind-the-scenes story of how three competitive AI labs came together to donate their protocols and agents to a neutral foundation, why enterprises are deploying MCP servers faster than anyone expected (most of it invisible, internal, and at massive scale), what it takes to design a protocol that works for both simple tool calls _and_ complex multi-agent orchestration, how the foundation will balance taste-making (curating meaningful projects) with openness (avoiding vendor lock-in), and the 2025 vision: MCP as the communication layer for asynchronous, long-running agents that work while you sleep, discover and install their own tools, and unlock the next order of magnitude in AI productivity. We discuss: * The *one-year MCP journey:* from local stdio servers to remote HTTP streaming, OAuth 2.1 authentication (and the enterprise lessons learned), long-running tasks, and MCP Apps (iframes for richer UI) * Why *MCP adoption is exploding internally* at enterprises: invisible, internal servers connecting agents to Slack, Linear, proprietary data, a