MCP servers explained: what they do and how to use them

The largest directory alone lists over 9,800 MCP servers — for GitHub, Notion, databases, browsers, file systems, and pretty much anything that is a tool or a data source. If you’re wondering what these servers actually do and which ones you need: that’s what this card is about.

The protocol behind them — MCP, the open standard AI clients use to talk to external services — has its own article. This one stays on the practical side: the server.

What an MCP server does

An MCP server is a service that offers tools to an AI. Each tool is a named capability with a clear job: search my notes, read this file, create a GitHub issue. The client — Claude, ChatGPT, Cursor — receives the list of tools when it connects, and decides on its own when to call which one.

That’s the whole trick. The server doesn’t need to understand AI; it just answers requests. The model doesn’t need to understand the service; it only sees described tools. Which is why the same server works with every client that speaks the protocol.

Which servers exist

The directories are full, but almost everything falls into four groups:

• Service connectors. GitHub, Notion, Slack, Google Drive, databases. The server translates between the AI and the service’s API. Useful when you already work there.
• System tools. File system, terminal, browser automation (Playwright, for instance). Mostly interesting for developers building AI agents or letting AI write code.
• Search and retrieval. Web search, documentation lookup, scraping. They give the model current information that wasn’t in its training data.
• Memory servers. They store what you tell your AI and make it findable in later sessions. The most interesting group if repeating your context forever annoys you — and the one with the biggest quality differences.

For everyday use you don’t need ten servers. Most people are fine with one or two: one for work (GitHub, Notion) and a memory.

How to connect a server

Setup follows the same pattern in every client — enter the address, sign in once:

• Claude Desktop and ChatGPT: Settings → “Connectors”, paste the server’s address. Sign-in opens in the browser.
• Claude Code: one terminal command, e.g. claude mcp add --transport http memexa https://brain.memexa.ai/mcp.
• Cursor: one line in the MCP config file.

After that, the server shows up in the client’s tool list and the AI uses it on its own whenever a question calls for it. What this looks like for Memexa is on the setup page — with copy-paste configuration for every client.

Local or hosted?

Many servers in the directories are small programs you start yourself — via npx in a terminal, configured in a JSON file. That’s fine for developers and the most common hurdle for everyone else: the server only runs while your machine runs, you handle updates yourself, and on your phone it doesn’t exist.

Hosted servers invert this. They run as a service on the web, you connect with a URL and a browser sign-in (OAuth), and the same state is reachable from every device and every client. In exchange, you hand your data to someone else — which brings us to the real question.

What to look for when choosing

The protocol is open; the quality behind it is not regulated. Three questions separate the solid servers from the risky ones:

1. Where does the data live? With local servers, on your machine. With hosted ones: which country, under which law? A server that doesn’t answer this has already answered it.
2. What is the server allowed to do? A tool that can delete files or send mail deserves more suspicion than one that only reads. Don’t connect more permissions than the task needs.
3. Can you leave? If the service disappears or you want to switch: is there a complete export in an open format?

Memexa’s answers: EU hosting, only you, one-click Markdown export.

Frequently asked questions

Do I need to know how to code?

For hosted servers with OAuth sign-in: no — entering an address and signing in is all. Local servers from the community directories usually assume a terminal doesn’t scare you.

How many servers can I connect at once?

Technically many; practically, restraint pays off. Every server makes the tool list longer, and the model walks it on every request — too many similar tools make its choices worse, not better.

What separates memory servers from each other?

Most are scratchpads: the AI stores text snippets it deems worth keeping and searches them later. That already helps. A hosted memory like Memexa goes further — you also throw in PDFs, scans and mail, everything is read, linked, and returned with its source, down to the page number. The difference shows three months later, when you want to know where a fact came from.