What Is MCP and Why Does It Matter?

Why You Should Read This Series

If you’ve worked with AI, you’ve probably hit this wall: language models are incredibly smart, but they can’t access the real world. They can’t read your files, connect to your database, or send an email. Every time you need external data, you’re stuck copy-pasting between tabs.

MCP was created to solve this problem. This series starts from the fundamentals and builds up to a real-world project. In this first episode, you’ll learn what MCP actually is and why it’s become one of the most important standards in the AI world.

Prerequisites

No special technical prerequisites for this episode. Basic familiarity with AI concepts is enough. If you’re starting from scratch, check out our AI Development: Zero to Hero series first.

What Is MCP?

MCP stands for Model Context Protocol — an open-source, standardized protocol introduced by Anthropic (the company behind Claude). Its goal is to provide a universal, standardized way to connect Large Language Models (LLMs) to external tools and data sources.

In simpler terms: MCP is a “common language” that translates between AI and real-world tools. Before MCP, every tool and every model had a different way of communicating. Now there’s a single standard that everyone can use.

Analogy

USB for AI: Remember before USB when every device had a different connector? Printers used one type, cameras another, keyboards yet another. USB came along and said “everyone use one standard.” MCP does exactly the same thing for AI — a universal port that any tool can plug into.

The Problem MCP Solves

Let’s look at the problem more closely. Imagine you’re using an LLM like Claude or GPT. These models can write text, generate code, answer questions. But they have a major limitation:

No access to your files — they can’t read a spreadsheet and analyze it
No database connections — they don’t have real-time data
No ability to take action — they can’t send emails, call APIs, or save files
Stale knowledge — their training data is months old

Now imagine you want to build an AI Agent that connects to your company database, generates reports, and posts results to Slack. Before MCP, you’d need to write custom integrations for each of these. Switch models or add a new tool? Start over from scratch.

The M×N Problem

This is where the real issue shows up. Say you have 3 AI models (Claude, GPT, Gemini) and 5 tools (database, email, Slack, GitHub, filesystem). Without a standard, you need 3 × 5 = 15 separate integrations. Each model needs its own connection to each tool.

Add a new model? That’s 5 new integrations. A new tool? 3 more. This doesn’t scale.

MCP solves this. With a standard layer in the middle, instead of M×N integrations, you only need M + N. Each model learns MCP once, each tool implements MCP once. After that, everything works with everything.

Note

If you’re not familiar with terms like Agent or Tool Use, check out our AI Glossary where all these concepts are explained.

How MCP Works

MCP uses a simple but powerful architecture: the Client-Server model. Let’s break down the pieces:

1. MCP Host

The application the user interacts with. For example, Claude Desktop, VS Code, or a custom application. The Host receives user requests and passes them to the Client.

2. MCP Client

The part of the Host responsible for communicating with MCP Servers. Each Client can connect to one or more Servers. The Client “speaks” the MCP protocol and translates requests into the standard format.

3. MCP Server

This is where the magic happens. Each MCP Server exposes one or more “capabilities” to the AI. For instance, one server might connect to a PostgreSQL database, another to GitHub, and another to local files.

Analogy

Think of MCP like a restaurant. Host = the customer (you). Client = the waiter (carries your order). Server = the chef (does the actual work). MCP Protocol = the menu (a shared language everyone understands). You tell the waiter what you want, the waiter communicates it to the kitchen, and food comes back.

Three Core MCP Server Capabilities

Every MCP Server can offer three types of capabilities:

Tools: Actions the AI can perform. For example, “run this query on the database” or “send an email.” The AI decides when to use each tool.

Resources: Data the AI can read. For example, file contents, API responses, or database records. Unlike Tools, Resources are read-only.

Prompts: Pre-defined templates for interacting with the AI. For example, a “code summarization” or “bug analysis” template that the AI can use.

A Simple Example

Let’s see MCP in action. Say you’re using Claude Desktop with an MCP Server connected to your local filesystem.

When you type “Please read report.csv and create a chart from it,” here’s what happens:

Claude (via the MCP Client) gets the list of available tools from the Server
It discovers that a read_file tool exists
It asks the Server to read report.csv
The Server reads the file and returns its contents
Claude analyzes the data and generates the chart

All this communication happens in standard JSON-RPC format. A simple request looks like this:

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "read_file",
    "arguments": {
      "path": "report.csv"
    }
  }
}

Simple, right? This standardization means any tool that speaks MCP works with any model that understands MCP.

MCP vs. Direct API Calls — Why Use a Standard?

You might ask: “I can call APIs directly — why do I need MCP?” Fair question. Let’s compare:

Feature	Direct API	MCP
Tool integration	Custom code per model	Write once, works everywhere
Switching models	Rewrite integrations	No changes needed
Tool discovery	Read docs + write code	AI discovers and uses tools automatically
Security	Manage yourself	Standard security layer
Ecosystem	Each project is an island	Thousands of ready-made servers

The main advantage of MCP is reusability. When someone writes an MCP Server for PostgreSQL access, everyone can use it — with Claude, with GPT, with any other model. No need to reinvent the wheel.

The second advantage is automatic discovery. When an MCP Server connects, the AI automatically learns what tools are available and how to use them. No manual configuration needed.

The MCP Ecosystem

One of MCP’s strengths is its growing ecosystem. Here’s where you can use MCP today:

Clients (Applications That Support MCP)

Claude Desktop: Anthropic’s desktop application. The first and most complete MCP implementation.
Claude Code: Claude’s CLI tool for coding. Uses MCP for file access and developer tools.
VS Code + GitHub Copilot: Microsoft’s code editor. Copilot Chat supports MCP Servers.
Cursor: AI-native code editor with full MCP support.
Windsurf (Codeium): Another AI-first editor with MCP support.

Ready-Made Servers

Hundreds of MCP Servers are available out of the box:

Filesystem: Read and write local files
GitHub/GitLab: Manage repos, issues, pull requests
PostgreSQL/MySQL: Query databases
Slack/Discord: Send and read messages
Google Drive/Notion: Access documents
Playwright: Browser automation and web testing
Docker: Container management

Note

You can find the full list of official and community MCP Servers at the official MCP repository. New servers are added daily.

What Can You Build With MCP?

Let me give you some practical ideas to spark your imagination:

1. Data-Driven Assistant

An AI assistant directly connected to your company database. Ask “What were last month’s sales?” and get an accurate answer — without writing SQL yourself.

2. Smart Developer Tools

An MCP Server connected to your Git repo, CI/CD pipeline, and monitoring system. Ask “Did the last deployment have any issues?” and the AI checks the logs for you.

3. Business Automation

Connect AI to your CRM, email, and calendar. For example: “Schedule a meeting with John and send him a summary of our last conversation.”

4. Document Analysis

An MCP Server connected to Google Drive or SharePoint. The AI can read company documents, summarize them, and answer questions.

If You Know About Agents

MCP and Agents are closely related. MCP is actually one of the most important tools for building powerful Agents. If you’ve read our Building AI Agents series, MCP is the standardized version of the “Tool Use” concept we discussed there — but much more powerful.

A Brief History of MCP

MCP was introduced by Anthropic in November 2024. The idea started from a simple observation: every team trying to connect AI to their tools was reinventing the wheel.

Instead of keeping this technology proprietary, Anthropic decided to open-source it. The MCP specification is public, and anyone can use it — even Anthropic’s competitors.

That decision paid off. Major companies like Microsoft, Google, and dozens of others quickly adopted MCP. Today, MCP has become a genuine standard — not just an Anthropic project.

MCP and the Future of AI

Why does MCP matter? Because it changes the trajectory of AI.

Until now, language models were like a “brain in a jar” — incredibly intelligent but with no hands or feet. MCP gives them hands and feet. When AI can interact with the real world, its capabilities grow exponentially.

Think about it: the difference between a scientist who only thinks and a scientist who has a laboratory. Both are brilliant, but only the second one can put ideas into practice.

MCP is taking AI from “just talking” to “actually doing.” And this is just the beginning.

Next Episode…

Now that you know what MCP is and why it matters, it’s time to get hands-on. In Episode 2: Installing and Running Your First MCP Server, you’ll learn how to set up your environment and run your first MCP Server. We start building!

Glossary

Terms like MCP, LLM, Agent, and more are explained in our AI Glossary.