Model Context Protocol (MCP) is addressing a critical gap in AI infrastructure. When AI models need to interact with tools—databases, APIs, code repositories—how do you enable this safely, reliably, and without reinventing communication patterns?

MCP provides a standardized interface for connecting AI models with tools and services. Rather than each AI framework implementing its own tool-calling mechanism, MCP offers a common protocol. This enables interoperability and reduces the burden on tool developers.

Enterprise adoption is accelerating rapidly. HP is using MCP to modernize their SDLC integration. Stack Overflow has published an MCP server that enables models to search their knowledge base. Major vendors are building MCP into their platforms.

The protocol defines how models request tool calls, how results are returned, and how security and permissions are managed. This standardization enables more sophisticated AI workflows while maintaining clear security boundaries.

Implementation considerations include authentication (who can use which tools), rate limiting (preventing abuse), result formatting (ensuring responses are actionable), and error handling (graceful degradation when tools fail).

MCP will likely become as fundamental to AI infrastructure as REST APIs are to web development. Just as REST standardized how web services communicate, MCP is standardizing how AI systems interact with tools.

The longer-term vision is an ecosystem of MCP servers—standardized, interoperable tool interfaces that any AI system can safely leverage. This enables composition of complex AI workflows without tight coupling to specific implementations.