What Is MCP? The Protocol Behind Many AI Coding Integrations

About four months ago, I came across the term MCP for the first time while working on a project that required building an MCP server so customers could integrate AI agents with their time tracking data and automate workflows.

After shipping the first version, I started noticing MCP everywhere. GitHub has one. Linear has one. Slack, Notion, databases, browser automation tools—the list keeps growing. It seems like every week another company is making their functionalities available through MCP.

So what exactly is MCP?

What Is MCP?

MCP (Model Context Protocol) is a lightweight standard that allows AI agents to discover and use the actions an application makes available, such as creating a ticket, searching for issues, retrieving customer data, or updating a record.

Many companies already expose this functionality —often referred to as capabilities— through REST or GraphQL APIs. The challenge is not that the functionality doesn't exist—it's that every API exposes it differently. MCP provides a standard way for AI agents to discover what's available, understand how to invoke it, and interact with it consistently.

REST APIs are typically designed for developers. MCP servers are designed for AI agents.

How Does It Work?

A typical setup looks something like this:

AI Assistant
      ↓
  MCP Server
      ↓
 REST / GraphQL API
      ↓
 Application

The MCP server exposes information and functionality that the AI assistant can discover and use.

When a user asks the assistant to perform an action or retrieve information, the assistant can interact with the underlying application through the MCP server.

MCP Usage in Practice

Many popular products now provide official MCP integrations, including:

• GitHub

• Linear

• Slack

• Notion

• Stripe

• Browser automation tools

Let's look at a concrete example using the Linear MCP with Claude Code.

Step 1: Install the Linear MCP

claude mcp add linear

Or simply ask Claude:

Install the Linear MCP for me.

Claude will guide you through the installation and authentication process.

Step 2: Use It

Once connected, you can ask things like:

View your open issues

What are my open Linear issues?

Create a ticket

Create a high-priority bug ticket for users being logged out unexpectedly.

Search sprint work

Show me all issues assigned to me this sprint.

From the user's perspective, it feels like a conversation.

For example, when you ask Claude to create a Linear ticket, Claude sends the request to the Linear MCP server. The MCP server exposes actions such as creating tickets or searching issues and knows how to translate those requests into operations in Linear.

##If AI agents can already use APIs, why do we need MCP?

AI agents can interact with REST and GraphQL APIs directly, so MCP isn't solving a technical limitation.

The value of MCP is that it gives AI agents a predictable way to discover what an application can do and how to use it.

For example, an AI assistant could be connected to GitHub, Linear, Slack, and dozens of other tools. Without a common standard, each integration would expose its functionality differently. MCP provides a consistent way for AI agents to discover available actions and interact with those systems.

Why Developers Should Care

MCP is quickly becoming one of the standard ways companies expose their products to AI agents. If your company provides APIs, there's a good chance you'll encounter MCP as organizations look for ways to make their products accessible to AI-powered tools and workflows.

Even if you're not building MCP servers yourself, you can benefit from the growing ecosystem of MCP integrations. By connecting AI assistants to tools you already use—such as GitHub, Linear, Slack, or Notion—you can automate repetitive tasks, retrieve information more easily, and streamline everyday workflows.

Whether you're building products or simply using them, MCP is becoming an increasingly important part of how AI systems interact with software.