Building AI Superpowers with Model Context Protocol

Imagine a world where your AI assistant seamlessly integrates with your tools, eliminating tedious tasks.

Last week, I used my AI assistant to help me review my GitHub issues and create a new release. Instead of getting the usual “I can’t access external systems” response, something magical happened: it queried my repo, summarized open issues, and helped me draft release notes. No copy-pasting, no context switching, no manual work.

How? I had just started experimenting with Model Context Protocol (MCP), an open standard from Anthropic that’s changing how AI tools interact with our real-world systems. As someone who’s spent countless hours building AI integrations, this felt like a breakthrough moment. Let me share what I’ve learned about MCP and why I’m excited about its potential.

The Problem: AI Assistants Are Stuck in Chat Boxes

Picture this: you’re chatting with an AI assistant about your codebase. You ask it to check if a particular function exists. The AI responds: “I’d be happy to help, but I can’t directly access your codebase. Could you paste the relevant code here?”

Sigh. We’ve all been there.

This is the reality of most AI tools today – they’re like brilliant minds trapped in a box, unable to reach out and interact with the world around them. Want them to:

Search your company’s documentation?
Create a ticket?
Push code to GitHub?
Query your database?

The typical answer is “I can’t access that system, but I can guide you through doing it manually.” It’s like having a brilliant colleague who can only communicate through Post-it notes.

Enter Model Context Protocol: A Universal Adapter for AI

This is where MCP comes in. Imagine if instead of building custom connections for every tool and database an AI needs to access, we had a standard “port” – like “USB-C for AI systems”. That’s essentially what MCP provides.

When Anthropic open-sourced MCP in late 2024, they described it as a way to “break down information silos” and let AI models securely access the data and tools they need. But what does this mean in practice?

Let me share a real example using one of the community-built MCP servers. A developer named Enes created a Twitter MCP server that lets AI assistants post tweets and search Twitter. Once configured, you can literally say to your AI: “Please tweet: ‘Just learned about MCP - mind blown! 🤯’” and it will post it to your account. No copying and pasting, no switching tabs.

How Does It Work? A Peek Under the Hood

The magic of MCP lies in its simple but powerful architecture. There are three main components:

Your AI Assistant (the “host”): This could be Claude, Cursor, or any AI app that supports MCP
The MCP Server: A program that knows how to talk to some external system (like Twitter, GitHub, or a database)
The MCP Client: The middleman that connects the AI to the server

When you connect an MCP server to your AI tool, the server tells the AI “Hey, here are the things I can do for you.” For example, a GitHub MCP server might say “I can create issues, read files, and make pull requests.” The AI then knows exactly what tools it has available and how to use them.

Here’s a concrete example using the official GitHub MCP server:

// This is what happens when you ask "Create an issue for the bug we discussed"
const result = await githubServer.tool("create_issue", {
  repo: "example-graphql-server",
  title: "Fix N+1 database query in GraphQL resolver...",
  body: "During testing, observed...",
});

The beauty is that you never have to write this code. The AI formulates the right tool call based on your natural language request.

Real-World Magic: MCP in Action

Let me share some fascinating examples I’ve discovered:

Database Whisperer: Using a Postgres MCP server, I can ask AI “How many users signed up last month by region?” and it writes and executes the SQL query, then explains the results in plain English. No more context switching between chat and database tools.
Code Archeologist: With the Git MCP server, an AI assistant can actually dig through commit history. “When was this function last modified?” triggers a git log search, and it comes back with the exact commit date and author.
Meeting Notes to Slack: One team built an MCP server that lets their internal AI assistant read meeting transcripts and automatically post summaries to relevant Slack channels. No copy-paste, or any human interaction at all is needed!
Backend Builder: Dennis Ivy (Appwrite) demonstrated how the Appwrite MCP server can be used to build backend infrastructure/systems.

Getting Started: Your First MCP Server

Want to try it yourself? The TypeScript SDK makes it surprisingly simple to create an MCP server. Here’s a minimal example:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new McpServer({ name: "hello-world", version: "1.0.0" });

// Register a simple greeting tool
server.tool(
  "greet",
  "Says hello to someone",
  {
    type: "object",
    properties: {
      name: { type: "string" },
    },
  },
  async params => {
    return {
      content: [{ type: "text", text: `Hello, ${params.name}!` }],
    };
  }
);

await server.connect(new StdioServerTransport());

This creates a basic server that knows how to greet people. Not exactly groundbreaking, but it demonstrates the core concepts. From here, you could expand it to do anything you need - call APIs, run shell commands, or interact with your custom tools.

The Growing MCP Ecosystem

What excites me most is how quickly the MCP ecosystem is growing. There’s already an impressive collection of open-source MCP servers for popular tools:

Puppeteer for browser automation
Sentry for retrieving/analyzing debugging information
Google Drive for retrieving or searching through files
EverArt for image generation
And many more community contributions!

Each of these is like a new superpower you can give to your AI assistant. Mix and match them based on your needs.

Looking Ahead: The Future of AI Integration

As a software engineering leader, I’m particularly excited about what MCP means for development workflows. Imagine AI assistants that can:

Actually run and debug your tests instead of just suggesting fixes
Deploy code changes while explaining what they’re doing
Monitor system metrics and proactively suggest optimizations
Create and update documentation by directly reading and interpreting your codebase

All of this becomes possible with MCP. Not as separate tools, but as capabilities that any MCP-compatible AI can use.

Want to Learn More?

If you’re intrigued by MCP (and I hope you are!), here are some great resources to explore:

Start small! Maybe try connecting an existing MCP server to your favorite AI tool. Or build a simple MCP server that wraps one of your existing tools. The beauty of MCP is that you can add capabilities incrementally, each one making your AI assistant a bit more powerful.

While I’m still exploring MCP’s full potential, I’m confident this represents the future of AI assistance. The days of isolated, minimally-capable chat interfaces are numbered. Instead, we’re moving toward a world where AI assistants can seamlessly interact with our digital tools and systems through protocols like MCP, making them truly powerful partners in our work.

Have you tried MCP yet? I’d love to hear about your experiences!