The Model Context Protocol (MCP): Supercharge Your AI with Tools

The age of AI agents is here. But for these agents to do more than just chat — to interact with the real world, perform tasks, and use external tools — they need a common language to talk to those tools.

That’s exactly what the Model Context Protocol (MCP) offers.

In this guide, we’ll break down:

• What MCP is
• Why it matters for the future of AI
• How to install and build your own MCP tool
• Some cool things you can already do with it

What is MCP?

MCP stands for Model Context Protocol. It’s a lightweight open-source standard that lets you connect tools to AI models through a simple JSON-based interface.

Think of MCP as the “USB protocol” for AI — plug in tools, and your model can call them just like functions.

Why It Matters

Large Language Models (LLMs) like GPT-4, Claude, and Mistral are incredibly smart — but by default, they:

• Can’t access real-time data
• Can’t interact with files, APIs, or external systems
• Don’t know your custom business logic

MCP bridges that gap.

By declaring tools in a simple schema (mcp.json), and implementing them in any language (Python, Node.js, etc), you allow the model to:

• Understand what the tool does
• Know when and how to use it
• Pass in inputs and read outputs automatically

How MCP Works

Here’s a quick overview:

1. You define tools in an mcp.json file.
2. Each tool points to an executable script (could be Python, Bash, etc).
3. The tool reads stdin (input) and writes to stdout (output) as JSON.
4. An LLM client like Cursor or smithereens reads the manifest and shows the tools to the model.

The model sees something like:

{
  "name": "get_weather",
  "description": "Fetches current weather for a city.",
  "parameters": {
    "type": "object",
    "properties": {
      "city": {
        "type": "string",
        "description": "Name of the city"
      }
    },
    "required": ["city"]
  }
}

Now it knows when to call it, and how.

Installing MCP

Let’s get your first tool running.

1. Install the MCP CLI

npm install -g mcp

MCP is written in TypeScript and runs via Node — make sure you have Node.js installed.

2. Create a Tool Folder

mkdir hello-tool && cd hello-tool

3. Add a Script (Python Example)

# hello.py
import sys, json

for line in sys.stdin:
    args = json.loads(line)
    name = args.get("name", "world")
    print(json.dumps({"result": f"Hello, {name}!"}))
    sys.stdout.flush()

4. Define It in mcp.json

{
  "tools": [
    {
      "name": "say_hello",
      "description": "Greets a user by name.",
      "command": "python3 hello.py",
      "parameters": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "The name of the person to greet."
          }
        },
        "required": ["name"]
      }
    }
  ]
}

5. Run the Tool Locally

mcp run say_hello --args '{"name": "Alice"}'

You should see:

{
  "result": "Hello, Alice!"
}

Testing in Cursor (or Other MCP-Compatible Tools)

If you use Cursor (a dev environment with built-in AI):

1. Open a folder with an mcp.json
2. Cursor will auto-detect the tools
3. Ask the AI to run them (e.g. “Use say_hello on Bob”)

Cursor also handles API keys securely if your tool talks to external APIs.

What Can You Build?

Here are some real-world use cases:

• Weather agent using OpenWeatherMap API
• Crypto bot using CoinCap
• PDF parser that extracts data from documents
• Notion writer to automate note-taking
• GitHub summarizer for issues and PRs

Browse community tools here: awesome-mcp

How It Fits with the Agent Stack

MCP can plug into any AI agent framework:

This separation of concerns is what makes MCP so modular — tools are portable across platforms.

Security & Sandbox

MCP doesn’t enforce a security model out of the box — but tools are local, and you can:

• Use .env files for secrets
• Containerize tools with Docker
• Set permissions per model or user (if hosted)

As enterprise adoption grows, expect future MCP versions to bake in authentication, access control, and sandboxing.

Final Thoughts

MCP is one of the most exciting protocols in the emerging AI agent ecosystem. It’s:

• Simple to use
• Language-agnostic
• Hugely extensible

Whether you’re building a personal AI assistant, an enterprise automation agent, or just hacking on fun tools — MCP gives you a plug-and-play way to bring real-world functionality into your AI’s reach.

More

• MCP GitHub
• awesome-mcp
• Cursor.dev
• Smithereens Agent Stack