As AI moved from chatbots to agents that use tools and data, a messy problem appeared: every integration was custom glue code. Connect a model to your files, your database, your APIs — each a bespoke effort. The Model Context Protocol (MCP) standardizes it.
The idea
MCP is an open protocol that defines a common way for AI applications to connect to external tools and data sources. Instead of writing custom code for every model-to-tool connection, you expose your tool or data through an MCP "server," and any MCP-aware app can use it. It's often described as a "USB-C for AI" — one standard port instead of a drawer full of adapters.
Before MCP, every tool integration was a one-off. After MCP, a tool you build once works with any client that speaks the protocol.
Why it matters
Standards create ecosystems. Because MCP is open and shared, a growing library of MCP servers exists for common services — file systems, databases, search, SaaS tools — that any agent can plug into. Build an MCP server for your internal system once, and every MCP-compatible AI tool can use it. That network effect is the whole point.
Where it fits
MCP is infrastructure for the agent era. Agents need to act — read files, query data, call services — and MCP is becoming the common plumbing for that, replacing brittle custom integrations. For anyone building AI products that connect to real systems, understanding MCP is quickly becoming as basic as understanding APIs. It's the layer that turns an isolated model into an agent that can touch the real world.
