MCP Servers Explained

MCP Servers Explained

MCP stands for Model Context Protocol, and it is a standard way for AI models to interact with external tools and data sources. Think of it as USB for AI: a universal interface that lets any model connect to any tool, from databases to APIs to file systems, without custom integration code.

Before MCP, connecting an AI model to external systems required writing custom code for each integration. Want the model to query your database? You had to write a custom function. Want it to access your company's API? Another custom function. Each integration was a bespoke project, and switching models meant rewriting everything.

MCP solves this by defining a standard protocol. An MCP server exposes tools and resources through a well defined interface. The AI model, or more precisely the client application that hosts the model, can discover what tools are available and call them as needed. This is similar to how USB devices all use the same connector, regardless of what they do.

An MCP server might expose tools like search the knowledge base, query the database, send an email, or read a file. Each tool has a name, description, and input parameters. The model can see the list of available tools and decide which one to use based on the task. This is how AI coding assistants can read your files, edit your code, and run terminal commands.

MCP is becoming the standard way to connect AI to real world systems. It is supported by major AI clients and development tools. For developers, building an MCP server is the best way to make your tools accessible to AI. Instead of building custom integrations for each model, you build one MCP server and any compatible AI client can use it.