MCP Explained in 5 Minutes

Everyone has heard of MCP by now. It is constantly mentioned alongside AI agents, coding assistants, and tool use. But while most people know what MCP is supposed to do, far fewer understand how it actually works or how to use it effectively.

At a high level, MCP gives AI applications a standard way to connect with external tools and data sources. Instead of building a custom integration for every API, database, repository, or browser, an AI application can connect to an MCP server and discover the capabilities it provides.

That sounds simple, but concepts like hosts, clients, servers, tools, resources, and transports can quickly make MCP feel more complicated than it really is. Once you understand the basic flow, however, the whole system becomes much easier to reason about.

In this guide, we will break down how MCP works in about five minutes and then put it into practice by connecting Claude Code to Tavily for web search, GitHub for repository workflows, and Playwright for browser automation.

What Is MCP?

The simplest way to think about MCP is as a common language between an AI application and the tools it wants to use.

Without MCP, every external service may require its own custom integration. With MCP, the AI application can connect to different MCP servers through the same standard interface.

MCP does not replace APIs. An MCP server usually talks to the underlying API or service on behalf of the AI application. What MCP standardizes is how those capabilities are presented, discovered, and called by the AI.

An MCP server can expose three main capabilities:

Tools: Actions the model can perform, such as searching the web, creating an issue, or running a query.
Resources: Information the application can read, such as files, documents, or database records.
Prompts: Reusable prompt templates or workflows exposed by the server.