Choosing Your Interface: A Complete Rundown of the Most Popular MCP Clients
Hey everyone, let's talk about something that's quietly changing the game for how we interact with AI, especially if you're a developer or someone who uses AI for complex tasks. It's called the Model Context Protocol, or MCP. I know, "protocol" sounds a bit dry, but honestly, this is a pretty big deal.
Think about how frustrating it is when your AI assistant is stuck in its own little bubble. It has a ton of knowledge from its training data, but it can't see what's on your screen, access your local files, or connect to the tools you use every day. You end up having to copy & paste EVERYTHING. Well, MCP is here to fix that.
Turns out, MCP is like a universal translator or a USB-C port for AI. It's an open standard that lets AI applications (the "clients") talk to external tools & data sources (the "servers") in a standardized way. This means your AI can suddenly do things like read a file from your computer, check the status of a pull request on GitHub, query a database, or even interact with a website. It's a HUGE leap forward, turning conversational AI into a true interactive assistant.
This was kicked off by Anthropic, the folks behind Claude, & it's been gaining a lot of traction. Instead of developers having to build custom, one-off integrations for every single tool, they can just use this common language. It’s making AI agents WAY more powerful & useful.
So, when we talk about "MCP clients," we're not talking about some standalone app. We're talking about the applications you already use that have adopted this protocol to become smarter & more connected. Today, we're going to do a deep dive into the big three: VS Code, Claude, & Cursor. We'll break down how each of them uses MCP, what it means for you, & how to get started.
The Core Idea: How MCP Actually Works
Before we get into the specific clients, let's quickly get on the same page about the moving parts. It's a pretty straightforward client-server setup:
- MCP Client: This is the application you're interacting with. It could be your code editor or an AI chat app. This client is what sends requests out, asking for information or for an action to be performed.
- MCP Server: This is the tool or data source the client connects to. Think of it as a little adapter that exposes a tool's functions to the MCP network. It could be a server for your local filesystem, a GitHub server, a database server, you name it.
- The Protocol Itself: This is the set of rules & message formats (usually JSON) that the client & server use to communicate. It handles things like discovering what tools a server has, how to call them, & how to handle the responses.
The magic is that the client doesn't need to know the inner workings of the server. It just needs to know how to speak MCP. This decoupling is what makes the whole system so flexible & powerful.
VS Code: Your Supercharged Code Editor
Visual Studio Code is already a powerhouse for developers, so it's no surprise that it has embraced MCP in a big way. For VS Code, MCP is all about making its integrated AI assistant, GitHub Copilot, more than just a code completion tool.
How it Works in VS Code
VS Code acts as a full-fledged MCP client, primarily through its GitHub Copilot Agent mode. This isn't the standard Copilot that just suggests code as you type. Agent mode is a more conversational, task-oriented experience within the Chat view. When you're in Agent mode, you can give Copilot access to a whole toolbox of MCP-powered actions.
Here's the cool part: you get to decide which tools Copilot can use. VS Code has a tool picker that lists all the available actions from your connected MCP servers. You can enable or disable them on the fly for each conversation. It's a great security feature, ensuring the AI only has permission to do what you're comfortable with.
Getting MCP Servers into VS Code
Microsoft has made it pretty flexible to add new tools to your VS Code setup. There are a few ways to do it:
- Direct Installation: There's a curated list of MCP servers you can browse & install directly into VS Code with a click.
- Workspace Configuration: You can add a file to your project. This is awesome for teams because you can define a set of common tools for a specific repository & check it into version control.
- Global User Settings: You can add servers to your main user settings, making them available across all your projects.
- Automatic Discovery: VS Code can even automatically discover & use MCP servers from other tools you have installed, like the Claude Desktop app.
- Command-Line: For the power users, you can even add servers using command-line arguments when launching VS Code.
What This Means for You
With VS Code as your MCP client, you can ask Copilot to do things that were previously impossible. You could say, "Read the
file, find the
function, & then check the associated GitHub issue for the latest update." Copilot can then use a filesystem MCP server to read the file & a GitHub MCP server to fetch the issue details. It's about giving the AI real-world context & the ability to act on it. There are even tutorials out there showing you how to build your OWN MCP servers in Go or .NET to use with VS Code.
Claude: The Native MCP Experience
Given that Anthropic created MCP, it's no surprise that their own AI assistant, Claude, has deep & native support for it. This is primarily experienced through the Claude Desktop application, which is designed from the ground up to be a powerful MCP client.
How it Works with Claude
Using Claude with MCP turns it from a purely conversational AI into an interactive partner. Without MCP, Claude's knowledge is limited to its training data & whatever you paste into the chat. With MCP, Claude can reach outside its own brain & interact with your digital environment.
The setup is managed through a JSON configuration file within the Claude Desktop app's settings. Here, you can define all the MCP servers you want Claude to connect to. Once you've added a server & restarted the app, a little "tools" icon appears, showing you that Claude is now connected & ready to use its new abilities.
A really interesting aspect is
Claude Code, which is a version of Claude tailored for development. Claude Code can act as BOTH an MCP client & a server. This means it can not only use tools but also expose its own functionalities to other MCP clients. You can configure tools on a project-by-project basis or globally, & you can even check in a
file into your repo so that anyone who clones it gets the same set of tools.
What This Means for You
This unlocks a ton of practical use cases. You can ask Claude to:
- "Read this 20-page PDF on my desktop & give me a summary."
- "Take this conversation we just had & save it as a text file in my 'notes' folder."
- "Check my open pull requests on the 'Project-X' repo & then send a summary to the #dev channel on Slack."
This last example is particularly powerful. It shows Claude using multiple tools from different servers (GitHub & Slack) in a single, multi-step task. It's this kind of agentic behavior that MCP truly enables. You can even connect it to tools like Puppeteer to have it take screenshots of a web page, which is incredible for front-end development workflows.
For businesses, this opens up a whole new world of automation. Imagine having a customer service team using an internal version of Claude. When a customer has a question, the agent doesn't have to manually look up information in five different systems. They could just ask Claude a question in plain English. This is where a platform like Arsturn comes into play. You could build a custom AI chatbot with Arsturn, trained on your company's knowledge base, documents, & FAQs. This chatbot could then act as an MCP server, providing instant, accurate answers to your internal team's Claude client. It's about connecting your AI assistants directly to your proprietary data in a secure & controlled way, which is EXACTLY what MCP is designed for. Arsturn helps businesses create these custom AI chatbots that provide instant support & engage with users 24/7, making it a perfect fit for this ecosystem.
Cursor: The AI-First Code Editor
Cursor is a fork of VS Code that's built from the ground up to be an AI-first code editor. So it's a natural fit for it to also function as a powerful MCP client. It combines the familiar interface of VS Code with deeply integrated AI capabilities.
How it Works in Cursor
Much like VS Code & Claude Desktop, Cursor allows you to connect to MCP servers through its settings. You can navigate to the MCP settings & add a new global MCP server by editing a
file. Once a server is connected, its tools become available to Cursor's "Composer Agent," which can then use them to fulfill your requests.
The community around Cursor has been very vocal about wanting robust MCP support, seeing it as a critical feature for building advanced, automated development workflows. The Cursor team has indicated they are actively working on this, so we can expect the integration to become even tighter over time.
What makes Cursor's approach interesting is its focus on making the AI do the work. The goal is to let you describe what you want in natural language & have the AI figure out which tools to use to accomplish the task. It even has an extension API that allows for programmatic registration of MCP servers, which is great for more complex or enterprise setups.
What This Means for You
Using Cursor as an MCP client means you can bring external data & tools directly into your AI-assisted coding workflow. For example, you could connect it to a Brave Search API server to allow the AI to look up documentation or error messages online. You could connect it to a filesystem server to give it the ability to read your entire project directory for better context.
This tight integration is what sets it apart. Instead of feeling like you're talking to a separate chatbot that happens to live in your editor, it feels like the editor itself is intelligent. The lines get blurry, & your workflow becomes a seamless conversation between you, your code, & the AI.
For a business that wants to supercharge its development team, this is huge. You could use a platform like Arsturn to build a no-code AI chatbot trained on your internal documentation, coding standards, & private APIs. Then, you'd expose this chatbot as an MCP server. Your developers, using Cursor, could then ask questions like, "How do I authenticate to our internal user API?" or "Generate a new component following our design system standards." Arsturn helps businesses build these kinds of chatbots trained on their own data to boost conversions & provide personalized experiences, which in this case, would be providing personalized developer experiences. It’s about building meaningful connections, whether with customers or your own team.
So, Which One is for You?
Honestly, there's no single "best" choice here. It really depends on your workflow & preferences.
- If you're a VS Code purist who loves the vast extension ecosystem & wants to add a powerful AI agent to your existing setup, then using Copilot Agent mode with MCP is a fantastic choice.
- If you want the most native & deeply integrated MCP experience, especially for non-coding tasks, the Claude Desktop app is probably your best bet. It's built by the creators of the protocol, after all.
- If you're looking for a code editor that is fundamentally AI-first & want to be on the cutting edge of AI-powered development, Cursor is an incredibly compelling option.
The most important thing is that this technology is here, it's growing, & it's making AI assistants genuinely more helpful. The wall between the AI & your data is coming down, thanks to this simple but powerful protocol.
Hope this was helpful! It's a topic I'm pretty excited about, & I think we're just scratching the surface of what's possible. Let me know what you think or if you've had any cool experiences using MCP yourself.