Every few months, a new AI tool or platform claims it can “connect to everything.” But how do these assistants actually talk to other apps, databases, or websites?
The answer is something called MCP = Model Context Protocol. It’s not a product or an app. It’s a shared language that helps AI models and tools understand each other without confusion.
Let’s make it simple with an example from your living room.
Table of Contents
Big Idea
MCP is like a universal remote for AI. One simple set of buttons that lets any smart model use any tool safely and smoothly.
A Story You Already Know
Picture your living room on movie night.
You have four remotes: one for the TV, one for the sound bar, one for the lights, and one for the AC.
You grab the wrong one, point it the wrong way, and nothing happens.
Frustrating. Right?
Then you buy a universal remote. Now one controller manages everything. You press a button, and it knows which device to talk to.
That’s what MCP does, more or less.
AI tools used to be like that messy pile of remotes. Each spoke its own language. MCP gives them one shared language so every AI application can work with any tool.
How MCP Works (Plain English)
When an AI apps uses MCP, three main parts work together:
1️⃣ Host - The Stage
The Host is where you interact with the AI. It could be a chat window, IDE like Cursor, or even a voice app. It’s the place where everything happens in terms of interaction.
2️⃣ Client - The Interpreter
Inside the Host is the Client. It speaks MCP language on behalf of the AI for us.
If you ask, “Check today’s weather, in New York” the Client knows how to build that request and send it out to AI model.
3️⃣ Server - The Toolbox
The Server holds the real tools: things like “get weather,” “summarize a file,” or “run a query.” Each tool sits neatly in this toolbox, waiting for any AI application that knows MCP language to use it.
Now, imagine this flow:
You type: “What’s the temperature in San Francisco?”
The Host gets your question.
The Client checks the MCP Server and finds a tool named get_weather.
The Server runs it and returns: {temperature: 22, conditions: "sunny"}.
The Host shows: “It’s 22°C and sunny in San Francisco.”
You don’t see the wiring. However, everything works smoothly because they all speak, interpret and understand MCP language.
The Three Building Blocks of MCP
🛠 Tools - Things That Do
Actions that perform or change something.
Example: download a file, run code, send an email, write a post, etc..
📚 Resources - Things You Read
Data sources the AI can look at but not modify.
Example: company handbook, spreadsheet, documentation snippet, weather data, content swipefile, etc..
🗒 Prompts - Things That Guide
Instruction sets or templates that help an AI application start a task.
Example: a “Code Review Mode” prompt that reminds the model what to check.
These three make MCP more than a connector. It’s a structured system with safety rules and reusable parts.
Why MCP Matters?
Let’s look at what came before it.
If you had 3 AI apps and 3 tools, each AI app had to connect with each tool separately. That’s 9 custom connections. Add one more tool, and it jumps to 12. Each connection had its own bugs and maintenance issues.
With MCP, it’s simpler. Each AI app implements the Client once. Each tool implements the Server once. Now it’s 3 + 3 = 6 connections. Clean and scalable.
Add a new tool? Plug it in.
Add a new AI? It already speaks the language.
That’s why companies like Anthropic (Claude Desktop), Cursor, and OpenAI are using MCP. It standardizes communication between AI applications, AI models, and the tools.
Common Questions
Q: Can the AI run dangerous commands through MCP?
A: Not without permission. The Host can ask you first, so you stay in control.
Q: Where do these tools live?
A: On MCP Servers. They can be on your machine or hosted online.
Q: Is MCP just for developers?
A: No. It’s for anyone who wants AI to connect with other services without rebuilding everything from scratch.
Your Turn - Mini Exercise
Grab a pen. Write down three tasks you do manually every day: check emails, update a spreadsheet, post to Slack.
Now imagine a small “tool” doing each one. If your AI assistant could trigger those safely through MCP, how much time would that save?
That’s the kind of automation MCP enables.
Key Takeaways
MCP is a universal remote for establishing communication between AI apps, AI tools and GenAI models.
It connects Hosts, Clients, and Servers in a simple loop.
It reduces integration work from M × N to M + N.
It defines how AI apps use tools, read data, and follow prompts.
It keeps you in control through permissions and safety checks.
Closing Thought
Every major tech shift starts with a shared standard, example, like USB for hardware or HTTP for the web. For AI agents, that standard is shaping up to be MCP for now (but may be something else in future, who knows?)
In the next lesson, we’ll explore why MCP had to exist in the first place and how the old “connect everything manually” approach created endless chaos for developers and AI application builders.
PS: If you learned something new, reply and tell me one task you wish your AI assistant could handle. Your idea might feature in the next demo.