What is MCP (Model Context Protocol)?

Hey everyone! 👋 It's Era, and today we're going to talk about a technology that is fundamentally changing the rules of the game for artificial intelligence.

You've probably noticed that smart chatbots and AI assistants, despite all their knowledge, often seem "locked in a box." They can write a poem or code, but ask them to check your email, look up current ticket prices, or, say, find a file on your desktop—and they fall short.

Why? Because they didn't have a common language to communicate with the outside world. Until now.

Meet MCP (Model Context Protocol). This isn't just another technical acronym; it's a true "universal translator" that allows AI to safely and effectively interact with any data, tools, and services.

Today, I'm going to do a deep dive into what this "beast" is, why we all need it, and how it works. Buckle up, we're diving into the future!

💡 What is MCP (Model Context Protocol)?

Simply put, MCP is an open standard or "universal language" that allows AI applications (like your chatbot or AI assistant in a code editor) to "talk" to external tools.

Imagine you're visiting a foreign country where every power outlet has its own unique socket. You need a separate adapter for your phone, another for your laptop, and a third for your hairdryer. It's inconvenient, expensive, and inefficient.

Before MCP, this is exactly what the AI world looked like. Want your model (LLM) to access your Google Calendar? You need one custom "adapter." Want it to work with your company's database? You have to write a second, completely different "adapter."

MCP is the universal power adapter for the entire AI world.

It's an open protocol, originally developed by Anthropic (creators of the AI Claude) in late 2024, but it was quickly adopted and supported by industry giants, including Google, OpenAI, Amazon, and many others. It creates a single, standardized way for AI to:

• Discover available tools.

• Understand what those tools do.

• Securely use them to complete your tasks.

🚀 Why Is It Needed? Solving the "Locked-in" AI Problem

The main goal of MCP is to break down "information silos" and release AI into the real world.

The Problem "Before" MCP: Fragmentation

In the past, every developer was reinventing the wheel. If Team A built an AI code editor, they wrote their own custom integration to work with files on disk. If Team B made a similar app, they wrote their own integration. The result? A ton of duplicated work, incompatibility, and headaches.

The Solution "After" MCP: An Ecosystem

Now, a developer just needs to "wrap" their tool (like a weather API or a database) in an MCP Server one time. And voilà! Any AI application that "speaks" MCP can instantly connect and use that tool.

This creates a whole "plug-and-play" ecosystem of tools for AI, much like how:

• HTTP became the standard for websites.

• LSP (Language Server Protocol) became the standard for code editors (it's why your VS Code "understands" Python, Java, and Go).

For users, this means AI assistants will become incomparably more powerful. Your bot won't just be able to talk about booking a table, but actually book it using the restaurant's MCP server. Your AI coding assistant won't just suggest changes, but will be able to run tests, read logs, and create a pull request on GitHub.

⚙️ How Does It Work? The MCP Architecture

Don't be scared by the technical terms; I'll explain it in simple terms. The MCP architecture consists of three key components:

1. The Host: This is the application you interact with directly. For example, your chatbot (like Claude Desktop), your IDE (Cursor, Zed), or any program with an AI agent.

2. The Client: This is the "brain" inside the Host. It manages communication, finds the right servers, and translates the AI's "wants" into the MCP protocol and back.

3. The Server: This is the "adapter" itself. It's a wrapper program around a real tool, whether that's a database, an API, or your file system.

Here's what the communication process looks like:

Interaction Flow:

1. You (User) type into the Host app: "What's my next meeting?"

2. The AI (inside the Host) understands it needs a calendar to answer.

3. The MCP Client (inside the Host) sees it's connected to a "Google Calendar Server." It asks the server: "What tools do you have?"

4. The MCP Server replies: "I have a tool called get_next_meeting()."

5. The MCP Client tells the server: "Okay, execute get_next_meeting()."

6. The MCP Server accesses the real Google Calendar, gets the data ("Meeting at 3:00 PM"), and returns it to the Client.

7. The Client passes this answer to the AI, which formulates a nice response for you: "Your next meeting is at 3:00 PM."

This entire complex dance happens in a fraction of a second and is standardized thanks to MCP. Technically, it uses the JSON-RPC 2.0 protocol for communication, and either stdio (for local tools) or HTTP+SSE (for remote web services) for data transfer.

🛠️ What Can MCP Do? Examples of Capabilities

The most interesting part is what an MCP server can provide to an AI. There are three main capabilities:

1. Tools

This is the most powerful part. These are functions that the AI can actively call to perform actions.

Imagine we're writing a simple MCP server for a calculator in Python. Thanks to libraries like fastmcp, it looks incredibly simple:

Python

# Example of a simple MCP server in Python
from mcp.server.fastmcp import FastMCP

# Create our server
mcp = FastMCP("MathServer")

@mcp.tool()
def add(a: int, b: int) -> int:
    """
    Adds two integers (a and b).
    Returns their sum.
    """
    print(f"Performing addition: {a} + {b}")
    return a + b

@mcp.tool()
def multiply(a: int, b: int) -> int:
    """
    Multiplies two integers (a and b).
    Returns their product.
    """
    print(f"Performing multiplication: {a} * {b}")
    return a * b

# Run the server to "listen" 
# for requests from an AI client
if __name__ == "__main__":
    mcp.run(transport="stdio")

When an AI client connects to this server, it will see:

• A tool add(a, b) with the description "Adds two integers..."

• A tool multiply(a, b) with the description "Multiplies two integers..."

Now, if you ask the AI, "What is 5 times 8?", it will know to call the multiply(5, 8) tool and will get the answer 40 from our server.

2. Resources

This provides passive access to data. The server can give the AI a list of "resources"—for example, files in a folder, tables in a database, or emails in your inbox. The AI can "read" these resources to get context for its answer.

Example: An MCP server for your desktop shows the AI a list of files. You ask, "What did I write in last month's report?" The AI finds the report_october.txt resource and "reads" it to give you a summary.

3. Prompts

These are pre-made templates or "recipes" for action. The server can offer the AI not just a single tool, but an entire mini-scenario.

Example: An MCP server for GitHub might offer a prompt called "Create a bug report." When the AI activates it, the server itself will suggest which steps to take (ask the user for a description, screenshots, logs, etc.) and then use its own create_issue() tool to publish the report.

🌍 MCP in the Real World: Who's Already Using It?

MCP isn't just theory. This technology is already being actively implemented:

• Google: Uses MCP in its Agent Development Kit (ADK) to connect agents to tools like Google Maps.

• Amazon (AWS): Has released an MCP Proxy that allows AI agents to securely connect to AWS services (like RDS databases or S3 storage).

• LangChain: The most popular library for building AI apps has built-in langchain-mcp-adapters.

• AI Code Editors: Tools like Cursor, Zed, and Sourcegraph use MCP to allow the AI to "see" your code, understand the project structure, and interact with your repository.

Let's Wrap Up

Model Context Protocol (MCP) is a fundamental shift from "all-knowing" AI encyclopedias to "all-powerful" AI assistants. It's the "universal glue" that connects the powerful brain of a large language model to the infinite set of tools, data, and services of the real world.

For developers, it means the end of integration chaos and the beginning of an era of compatible, reusable AI tools. And for us, the users, it means that very soon, our AI assistants will become truly useful partners in our work and lives.

I hope this breakdown was helpful for you! This is Era, signing off.

So, what tools would you want to "plug in" to your AI first? Let me know in the comments! 👇