You might’ve heard about MCP. But what is it, really? Before we dive into what it is and what it does, let’s take a step back.
The Limitations of AI Models
Let’s talk about the AI models we’re all familiar with. They can do a lot. But they still have no idea what system they’re in. They don’t know what tools are connected. They don’t know what you’ve already done. They don’t even know what they’re allowed to do. Just a prompt and some guesses.
This becomes a problem the moment you ask a model to do anything beyond generating text. Reply to an email in Gmail. Update a doc in Notion. Send a message in Slack. Without context, it either breaks or blindly fakes it.
Enter AI Agents
So we started building AI agents, systems that can actually do the work. They’re more capable than standalone models and a lot more reliable too. However, integrating these agents across different platforms often requires custom wrappers, permission management, and environment-specific logic. This approach is not only time-consuming but also lacks scalability.
The Need for Structure
You wire up APIs. A Gmail script here, a Notion integration there, maybe a Slack bot. But access alone isn’t enough. Maybe you just want the agent to leave a comment on a Notion doc, not go in and start deleting things around. So you build wrappers, limit permissions, and write custom prompts to keep it in check. Now it works, but only in your setup. You’ve hardcoded logic into something that’s meant to be flexible. You’re managing tokens, formatting, and behavior manually. Try running that same agent somewhere else, like in Cursor or Claude, and none of it carries over. You’re stuck rebuilding everything from scratch.
It doesn’t scale. Because what’s missing isn’t access. It’s structure.
That’s where MCP comes in.
Anthropic introduced MCP as a standard way to describe a model’s environment. It defines what tools exist, how they work, what permissions are in place, and how to safely run a request.
Ever since Sam Altman tweeted, and adoption has snowballed. More and more teams started wiring it in.
Now, instead of writing glue code for every integration, you describe the world once and the model figures it out. It no longer matters whether it’s running in Cursor, Claude, or your own stack. The behavior carries over. It just works.
MCP is like USB-C for AI. One simple, universal interface that works across tools, models, and setups.
Remember when everyone had different charging cables? iPhone had its proprietary 30-pin connector, then Lightning. Most Android phones used micro USB. No one could share. USB-C fixed that. One port. One cable. Done.
MCP is doing the same for AI.
Real-World Application: Defang’s MCP Server
Defang’s MCP Server exemplifies MCP’s potential. It enables developers to deploy and manage cloud services directly from their Integrated Development Environments (IDEs) using natural language commands. By integrating with popular IDEs like Cursor, Windsurf, VS Code, and Claude Desktop, Defang allows for streamlined operations such as:
Deploying Services: Automatically detects Dockerfiles and compose.yaml files to deploy services.
Managing Services: Lists all currently deployed services with details like name, ID, URL, and status.
Destroying Services: Terminates specified services with a simple command.
This integration simplifies the development workflow, allowing AI agents to understand and interact with the development environment effectively.
The Broader Impact
By adopting MCP, tools like Defang’s MCP Server reduce the need for custom integration logic, making AI agents more portable and scalable. Developers can define their environment once, and any compliant AI model can operate within it, regardless of the platform.
Top comments (0)