The Model Context Protocol: Understanding Its Limits and Planning Your Agent Stack

The Model Context Protocol (MCP) received significant fanfare as a standardized way for AI agents to access tools and external systems. Anthropic's launch generated enthusiasm in the AI community, particularly among developers building local and experimental agentic systems. But as organizations move from proof-of-concept to production deployments, MCP's limitations are becoming apparent.

This isn't a story about MCP "failing" or being replaced overnight. Rather, MCP is settling into its actual role: one integration pattern among many, useful in specific contexts but insufficient as the primary fabric for enterprise agentic systems. Understanding where MCP fits (and where it doesn't) is essential for anyone building production-grade agent infrastructure.