Blog - How MCP works

A chat with AI doesn’t magically know what’s in your task tracker, your repo, or the company database. It only sees what you paste — or what you explicitly give it access to.

MCP (Model Context Protocol) is a shared way for an assistant to reach outside systems safely: an issue, a file, an API. Instead of writing a one-off plugin for every chat product, you expose one “socket”, and different apps (an IDE, a desktop assistant, your own agent) plug in with the same language.

Why bother

Without MCP, every AI product reinvents integrations. One tool here, another there, the database somehow else — and nothing gets reused.

With MCP you build a server once that can, say, create issues. Then the same server works in your IDE and in an internal chat. Less glue. Fewer “works only in this app” stories.

Who talks to whom

In short:

• The app (IDE, chat) — where you sit and talk to the model.
• The client — the app’s translator; it speaks MCP.
• The server — a small program that actually talks to an external system, disk, or the database.

The model never connects to anything on its own. It asks the app: “do this”, and the app goes to the MCP server.

What a server can offer

Three things you’ll run into most:

1. Tools — “do something”: create an issue, send an email, run a query. The model picks the tool and args; the app does the rest.
2. Resources — “show me something”: file contents, a task description, a table schema. Mostly reading, not acting.
3. Prompts — ready-made flows on the server side, e.g. “review this change”.

You don’t need every protocol detail to use it. Remember: action, data, ready-made recipe.

What it looks like day to day

You write: “open an issue — login is broken”.

The model proposes a call. The app asks the server. The server does the work. The result comes back into the chat — and only then does the AI answer you.

Local or remote

An MCP server often runs locally next to your IDE (fast, close to your files). Sometimes it lives on a server over HTTP — then the whole team can share it.

Under the hood it’s ordinary request/response. You don’t need to debug that daily; what matters is that everyone speaks the same language.

Why we care

Build one company MCP server — CRM, repos, deploy checklists — and plug it in wherever you already work with AI. No third “only for this chat” plugin.

MCP doesn’t replace the model. It’s more like a shared power plug for context: how the model gets tools and real data, so it stops guessing and starts acting on what’s actually there.