Here’s the thing about AI: it’s only as smart as the information it can access. You can have the most powerful language model in the world, but if it’s stuck in a box, unable to interact with your data, your tools, & your systems, it's not much more than a very clever conversationalist. This is where the Model Context Protocol, or MCP, comes in, & honestly, it’s a total game-changer.

Think of MCP as the universal translator for AI. It’s an open standard that lets your AI models securely connect to & interact with pretty much anything, from your local files to remote APIs. It's like giving your AI a set of keys to the digital world, allowing it to fetch real-time data, use external tools, & perform tasks in other applications.

This is a pretty big deal because, before MCP, connecting AI to external systems was a messy, custom-coded nightmare. Developers had to build unique integrations for every single tool & data source, which was not only time-consuming but also created a fragmented & insecure mess. MCP solves this by creating a standardized way for AI clients & servers to talk to each other.

But here’s where it gets interesting: MCP isn’t a one-size-fits-all solution. There are two main flavors of MCP servers: local & remote. Understanding the difference between these two is key to building MCP tools that actually work for your specific needs. So, let’s break it down.

The main difference between local & remote MCP servers is pretty straightforward: it’s all about where they live.

Local MCP servers run on the same machine as the MCP client (your AI application). They communicate using standard input/output (stdio), which is a fancy way of saying they talk to each other directly on your computer. This makes them super fast & secure, which is perfect for tasks that involve sensitive data or require direct access to your local files & software. For example, you could use a local MCP server to give your AI assistant access to your code editor, your local database, or even your web browser.

Remote MCP servers, on the other hand, are hosted on the internet. They're accessible via HTTP & Server-Sent Events (SSE), which allows for real-time communication between the client & the server. This is where things get REALLY powerful. With remote MCP servers, you can connect your AI to pretty much any cloud-based service or API you can think of. Imagine an AI that can check your Asana tasks, pull data from your Intercom conversations, or even manage your Jira tickets. That’s the power of remote MCP.

So, which one should you use? Well, it really depends on what you’re trying to build. If you’re creating a tool that needs to work with local files or software, a local MCP server is the way to go. But if you want to connect your AI to the wider world of web services & APIs, a remote MCP server is your best bet.

Let's start with something simple: a local MCP tool. We're going to build a basic weather server using FastMCP, a Python framework that makes building MCP servers a breeze.

First things first, you'll need to install FastMCP. You can do this with a simple pip command: