MCP is eating the world—and it's here to stay

Model Context Protocol (MCP) has emerged as a promising framework for building agents and workflows on top of Large Language Models (LLMs) by addressing past challenges faced by similar attempts. Despite not being revolutionary, MCP is praised for its simplicity and timely introduction, which coincides with the improved reliability of LLMs that can now handle complex tasks with reduced error handling. MCP offers a shared, vendor-neutral protocol that simplifies tool integration across platforms, allowing tool developers and agent developers to work more efficiently without being bogged down by platform-specific requirements. The protocol's tooling is accessible, with SDKs available in multiple languages, facilitating easier development and sharing of tools. MCP's growing momentum is evident as major foundational model providers like OpenAI and Google's DeepMind have adopted it, and a robust ecosystem of independent resources is emerging. While skepticism remains about its longevity, MCP's current success is attributed to its practicality and the community's enthusiasm, making it likely to persist as a crucial component in the future of APIs.