Model Context Protocol

You will hear this as teams start building AI systems that need to connect to external tools, databases, and services in a standardized way. Model Context Protocol, or MCP, is an open standard that defines how AI models communicate with tools, data sources, and other services. It provides a consistent interface for connecting models to external capabilities without requiring custom integration code for each tool or system.

Think of it as a universal plug for AI integrations. Rather than building a bespoke connection between a model and each data source, MCP defines a shared language that allows models and tools to communicate interoperably. This makes it easier to build agentic AI systems that can use many tools reliably.

A customer service platform wants its AI agent to check order status, look up account history, and file support tickets across three different backend systems. Without MCP, each integration requires custom development and maintenance. With MCP, each system exposes its functionality through a standardized server, and the AI model can query all three using the same protocol. Adding a fourth system requires building one more MCP server rather than a full custom integration.

This shows up as AI systems become more agentic and need to interact with more tools. MCP reduces integration complexity and creates a more reliable, scalable foundation for connecting models to the systems they need. For customer operations teams building AI-assisted workflows, it simplifies the path from prototype to production by making tool connectivity more consistent and maintainable over time.