MCP in Production: 7 Failure Modes Nobody Talks About

Over the past year, Model Context Protocol (MCP) has rapidly become a foundational layer for building AI agents that interact with real systems. At first glance, MCP feels simple. It standardizes how AI agents communicate with tools using JSON-RPC, exposes tool schemas to models, and s streaming responses. In s, everything works beautifully. You spin up a server, register a few tools, connect an agent, and suddenly the model can execute actions in the real world. But once you move from environments to production systems, things start breaking in ways that most tutorials never discuss.