What is an MCP server?
An MCP server gives an AI hands. On its own, a model like Claude or ChatGPT can only talk. An MCP (Model Context Protocol) server lets it do things — look up an order, update a ticket, search your docs, run a query — by exposing those capabilities as tools the AI can call. This is the plain-language explanation of what they are and how they work.
The one-sentence version
An MCP server is a program that offers an AI a menu of tools, and any MCP-compatible AI client can connect to it, see that menu, and call the tools to get real work done.
How it works
Every MCP interaction follows the same simple shape:
- Connect. An AI client (Claude, ChatGPT, Cursor, and others) connects to the server’s address.
- Discover. The client asks “what tools do you have?” The server answers with a list — each tool has a name, a description of what it does, and a schema describing its inputs.
- Call. When the AI decides a tool fits the task, it sends a call with the inputs. The server does the actual work — an API request, a database query — and returns the result.
- Use the result. The AI reads the result and continues the conversation, now with real information or a completed action.
The magic is in step 2: because each tool describes itself, the AI can figure out on its own which tool to use and how — you don’t have to program the interaction. The protocol is a standard, so one server works across every AI client that speaks MCP.
What can an MCP server do?
Anything you can wrap in a tool. Common examples:
- Reach a SaaS tool — read and update Jira issues, Notion pages, or Linear tickets.
- Query data — run read-only queries against a Postgres database or a data warehouse.
- Fetch from the web — scrape a page, search, or pull structured data.
- Call your own API — turn your product’s endpoints into actions your AI can take.
You can see and connect dozens of real, verified ones in the MCP server directory.
MCP server vs. API — what’s the difference?
They’re related but built for different callers. An API is designed for a programmer to call in code, with docs a human reads. An MCP server is designed for an AI to call in natural language, with tool descriptions the AI reads to decide what to do. In fact, many MCP servers are thin layers over an existing API — the same capabilities, re-packaged so an AI can discover and use them on its own. That’s also why you can generate an MCP server directly from an API.
How do I actually use one?
You add the server to your AI client. Doing that per server, per client, gets messy fast — and once an AI can take real actions, you probably want control over what it’s allowed to do and a record of what it did. The common setup is to connect servers through an MCP gateway: one URL that carries every server into Claude, ChatGPT, and Cursor, with per-tool rules, a security scan, and a full activity log. That way you get the capabilities without the sprawl or the blind spots.