In this podcast episode, Alex interviews Theo and David about the Model Context Protocol (MCP). They discuss MCP's function as a way to provide context to AI applications using LLMs through tools, resources, and prompts. They trace the origins of MCP to internal developer frustrations with data transfer and its subsequent validation during an internal hackathon where employees enthusiastically adopted it. The discussion covers the launch of MCP, the initial slow adoption due to confusion about its purpose, and the decision to open-source it to encourage broader integration and innovation. They also highlight the current state of MCP, including its adoption by major companies, the growth of its server builder ecosystem, and the emergence of remote MCP for cloud AI integrations. They touch on the impact of Claude 4 on MCP, particularly regarding agents and long-running tasks, and outline future developments like the registry API for models to discover additional servers. The episode concludes with advice for developers looking to get involved with MCP and examples of creative MCP server applications.
