_A field guide to MCP, A2A, generative UI, and related standards._
MCP has rapidly become the de facto standard for tool calling in AI systems, not because it was perfectly designed, but because it was the first open protocol to achieve real traction. What began as a pragmatic interface for connecting models to tools has now become a foundation for a much broader ecosystem.

We trace how MCP evolved from an early standard into widely adopted infrastructure across major LLM providers and thousands of public servers, and we examine the challenges that came with that growth: fragmented implementations, unresolved security risks such as prompt injection and tool poisoning, and increasing context bloat as servers expose ever-larger tool surfaces.

But tool calling is no longer the whole story. As AI systems become more capable, they also need richer interfaces, longer-running workflows, asynchronous communication, and ways to share artifacts across systems. That pressure has driven the emergence of adjacent ideas and protocols: MCP Apps and A2UI for generative user interfaces, and A2A for long-running, multi-agent interactions.

We cut through the noise to explain what these agentic protocols actually specify, what they don't, and how it fits into this large AI landscape. The result is a practical map of where these protocols stands today and how these efforts are shaping the next generation of agentic systems.