What is an MCP server?

Technical definition

An MCP server implements the server side of the Model Context Protocol, advertising a set of capabilities that a client can discover and invoke. Those capabilities fall into three kinds: tools, which are callable functions that do something; resources, which are data the client can read; and prompts, which are reusable templates.

A client connects, discovers what the server offers, and calls capabilities on the model's behalf. Servers exist for file systems, databases, project trackers, search, browsers, and most major SaaS products, published by vendors and by the community.

In plain terms

An MCP server is a connector that lets an AI use a specific system.

Want your AI to read your project tracker? There is an MCP server for that. Query your database, search the web, work with your files? Same pattern, one standard connection each.

Why it matters

MCP servers decide what an AI can reach, and reach is what separates a chatbot from something useful. An AI that can only read what you paste is limited by your typing. One connected to your real systems can answer questions about your business.

For most small businesses the value is not writing MCP servers. It is that the tools you already pay for increasingly ship one, so your AI can work with the real data rather than a description of it.

What to check before connecting one. An MCP server grants an AI access to a live system, so the permission scope matters: read-only where possible, and never broader than the task needs. Who published it matters too, since a community server is code you are trusting with a connection to your data. And a server returning content from outside your business is a prompt injection path, because the AI reads what comes back as text.

Connect what a task needs, not everything available.