MCP Explained: The Open Protocol Powering the Future of AI Apps

MCP Is Exploding in Popularity

The Model Context Protocol (MCP) is quickly becoming a cornerstone in the world of AI. Originally developed by Anthropic, MCP now powers thousands of “servers”—which function like apps, but for AI. In a major milestone, OpenAI adopted MCP in March 2025, just months after its release. Unlike traditional apps, MCP servers can be combined in powerful and flexible ways, laying the groundwork for a rapidly growing AI ecosystem—much like what we saw with mobile apps a decade ago.

What Is MCP?

At its core, MCP (Model Context Protocol) is a standardized way to expand what an AI can do—similar to how installing an app gives your phone new abilities.

Key Components of MCP:

• MCP Host Applications: These are platforms like Claude Desktop or Cursor that can “host” AI extensions.

• MCP Servers: These are the actual extensions or tools the AI can access (like Slack or Google Maps).

• MCP Clients: In many cases, these act similarly to hosts and can also interact with servers.

Because MCP is an open standard, any host can integrate with any server. This means developers can build once and deploy across many platforms—no proprietary lock-in.

Examples of MCP Hosts:

• Claude Desktop

• Claude Code

• Cursor

• oterm (terminal tool)

MCP Servers Are Booming

In just a few months, the number of MCP servers has surged into the thousands. Entire websites (like mcp.so) have emerged to catalog and organize them by functionality.

These servers allow AI models to tap into external tools and data sources—broadening what they can do in real-world applications.

Sample MCP Servers Released by Anthropic:

• Google Maps – Perform location-based searches and get place details.

• Slack – Send and receive chat messages.

• Memory – Store and recall information between AI sessions.

• Time – Convert between time zones or get the current time.

• Puppeteer – Render web pages and return HTML/images.

• EverArt – Generate images, showing MCP’s flexibility beyond just text.

Why MCP Matters

MCP marks the beginning of the AI application ecosystem—where AIs can work with plugins, tools, and services just like smartphones do with apps.

But here’s what makes MCP different:

• Flexibility: Unlike traditional APIs with rigid input/output formats, MCP communicates in natural language, giving it a far wider range of use.

• Composable AI: MCP servers can be mixed and matched to build more intelligent and adaptive workflows.

• Open Ecosystem: Anyone can build tools that work across hosts—driving faster innovation.

Open Standards Make MCP Easy to Adopt

One of the biggest strengths of MCP (Model Context Protocol) is that it’s built on open standards. This means developers don’t have to worry about platform lock-in or creating separate versions for different AI systems.

Why That Matters:

• Both Anthropic and OpenAI now support MCP.

• Developers can build once and deploy across many AI platforms like Claude, Gemini, and others.

• MCP helps prevent a divided ecosystem like Android vs iOS by offering a unified approach.

“Write once, deploy everywhere” is finally real for AI tools.

This level of compatibility has played a huge role in MCP’s rapid adoption by developers.

Seamless Integration and Chaining of Tools

Traditional apps operate in silos. They don’t naturally talk to one another unless you use middleware tools like Zapier, which can be clunky and limited.

MCP changes that by allowing hosts to combine multiple servers into powerful, seamless workflows.

Real-World Workflow Using MCP:

1. Monitor Slack for phrases like: “Find us a place to go to dinner.”

2. Use Google Maps and Yelp MCP servers to find restaurant options.

3. Query the Memory server to recall team members’ food preferences.

4. Use the OpenTable server to make a reservation.

5. Post back on Slack: “I found a place nearby that fits everyone’s preferences. Reservation made!”

All of this happens through server chaining, where outputs from one tool feed into another. It's more than automation—it’s collaborative intelligence.

Towards a Mesh of Intelligent Agents

We're beginning to see the emergence of a network of AI agents—each capable of both sending and receiving tasks.

Example:

• Claude Code acts as a host (calling MCP servers like GitHub), and also as a server (providing coding help to tools like Claude Desktop).

• This creates a mesh-like structure where agents can collaborate, delegate, and respond—just like a team of digital coworkers.

This structure is a step toward more autonomous, distributed AI systems that can self-organize and cooperate to solve complex problems.

How Is MCP Different from Regular Tools?

At first glance, you might think MCP is just another way to run tools. But it’s much more than that.

Key Differences Between MCP and Traditional Tools:

MCP doesn't just handle tools—it also supports:

• Resources (like files, URLs, and databases)

• Prompts (contextual instructions and natural language input)

So yes, MCP includes tools, but it elevates them to first-class citizens in a dynamic, user-driven environment.

A Simple, Real-World Example: Building a Personal News Bulletin with MCP

Let’s walk through a real-life use case that shows how easy it is to get started with MCP (Model Context Protocol).

Using just the Memory MCP server and Claude Desktop as the host, the author created a personal daily news bulletin system—with zero coding.

Here's how it worked:

1. Store personal preferences in the Memory server (e.g., interests, location).

2. Ask Claude Desktop to pull the latest news relevant to those interests.

3. Instruct it to avoid repeating news from the day before by saving a history of what was already shared.

And that’s just the beginning.

Future Enhancements:

• Integrate the Google Tasks MCP server to create to-dos directly from news items.

• Connect an email MCP server to share parts of the bulletin with friends.

• Use a Calendar MCP server to include the day’s events in the bulletin.

Without MCP, this would have required:

• Coding a custom app from scratch.

• Creating a database for user preferences.

• Writing backend integrations for news APIs, email, calendar, etc.

With MCP, all of this happens in one interface using natural language.

How MCP Can Impact You

If you're working in AI—or even just using it—you should start thinking about how to get involved in the MCP ecosystem.

Questions to Consider:

• Should I expose my app’s capabilities via an MCP server?

• Could my app function without a traditional UI?

• Could an MCP host become the new interface for my users?

• Should I make my app an MCP host to unlock new capabilities?

The author is already planning to incorporate MCP into their open-source Islamic AI assistant project—both as a server and host.

What’s Coming Next for MCP?

While MCP is incredibly powerful, it’s still in its early stages. Here are some challenges and improvements on the horizon:

Current Limitations:

• Setup Friction: Installing MCP servers often requires editing JSON, running Docker or Node locally—still too technical for many.

• Security & Authorization: Integrating services like Google Drive needs API keys, multiple permission layers, and isn't yet user-friendly.

• Prompt Injection & Safety: Current protections are basic. Hosts must repeatedly ask for user consent before executing tasks.

Where It's Going:

• Dynamic Server Discovery: In the future, LLMs will be able to search and connect to MCP servers on their own—unlocking even more autonomous behavior.

Final Thoughts: The Future of AI is Modular

MCP may sound technical, but its impact is massive. It’s paving the way for a modular, open, and interoperable AI ecosystem.

The real magic lies in how adding just one MCP server can instantly expand an AI’s capabilities—allowing it to chain together multiple services and complete complex tasks without writing a single line of code.

If you’re building in the AI space, now’s the time to ask: how will MCP shape the future of your work?

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About the author

Aitor Alonso is the co-founder of 1 node, where he focuses on building AI-powered systems that automate work and unlock new capabilities for businesses. Passionate about the future of AI, he writes regularly about emerging technologies, especially around AI agents, automation, and how these innovations are shaping the way we work and build.