MCP (Model Context Protocol) is an open standard that lets AI models connect to external tools, data, and services through one common interface. Expose a tool once as an MCP server; any compatible AI uses it — USB-C for agents.
Before MCP, connecting an AI model to a tool was bespoke every single time. Model A talking to your CRM needed custom code. Model B talking to the same CRM needed different custom code. Ten models and ten tools meant a hundred fragile integrations. That combinatorial mess is exactly what kept "agents" stuck in demos.
The USB-C analogy
Before USB-C, every device had its own connector and you drowned in adapters. USB-C set one standard, and suddenly any cable fit any port. MCP does that for AI. It defines a common protocol so any compliant model can talk to any compliant tool — no custom adapter per pair.
Two sides of the protocol
MCP server
Wraps a tool or data source — a database, an API, a file system — and exposes its capabilities in MCP format. You build this once.
MCP client
The AI application or agent that connects to servers and calls their tools or reads their resources. Adopt a new model, keep every server.
Why this is the unlock, not a feature
MCP turns an N×M integration problem into N+M. Build a tool once; every model reaches it. Add a model; it reaches every tool you have already exposed. That is the difference between "AI that can answer" and "AI that can act" across your real systems — the foundation of composable, agentic commerce.
The operator's read
Standards win distribution. The teams exposing their systems as clean MCP servers now are the ones whose infrastructure every future agent will route through. This is infrastructure leverage, not feature velocity — a horizontal layer everything else plugs into. If you are building AI-native, MCP is not optional plumbing. It is the plumbing.