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Category: Technology
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In 2025 The way we build applications has fundamentally shifted. And that's thanks to Model Context Protocol, or MCP for short.
MCP is the protocol that finally lets us retire a collection of duct tape, baling wire, and hand roll JSON glue code.
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Instead of running yet another custom integration, and every time an AI model needs to talk to an API, MCP standardizes the whole thing like a universal interface spec for your elements. So it's basically a usb-C
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for AI agents. One connector to rule them all.
Plug your AI models into co-repositories communication platforms mapping services, or any tool you need and watch the magic happen.
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No more bespoke adapters, no more SDK bingo. And here's how it works.
The user sends a prompt to the MCP client. Here's the prompt going to the MCP
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host with the client. The client figures out what the user wants.
So let's say do a price comparison on organic chicken breast and have Google Maps send me to the cheapest grocery store on my way home from the gym.
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It then picks the right tools through the MCP server right over here. It then calls any external APIs it needs and collects and processes the results, and then sends everything back to the user.
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All of this is happening behind the scenes. Now the MCP host is where the main app runs - this is everything running right in the center It includes the MCP client and connects to all the tools and data the AI needs to do its job.
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The MCP client, think of this as the middleman, It sits inside the host and talks to one or more MCP servers. So here we have one server,but we could have several more.
What they can do is find the right function to call
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and handle the back and forth to get the answers the AI needs. The MCP server is where all the tools live.
The server connects to external systems and offers up three things.
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First, we have tools, these are functions that the AI can call. Second, we have resources.
So this is where all the data comes from. And last the prompts.
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And as you know prompts are predefined preset instructions that help guide the AI's behavior. Now that we understand how MCP works under the hood, let's take a look at some real world use cases.
Starting with my personal favorite because I work with it all the time.
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And that's GitHub with the GitHub MCP server. You connect your AI agent directly to the GitHub API.
This means the AI can automatically handle tasks like managing repositories,
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issues, pull requests, branches, and releases, all while taking care of authentication and error handling for you. But what does this actually mean for you?
Instead of manually reviewing every pull request or constantly checking for issues, the AI can do the following.
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It can review pull requests automatically and flag potential problems. It helps spot bugs earlier by analyzing code changes.
It helps enforce
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consistent coding standards across your team, but it goes even further. It can sort and prioritize incoming issues so your team knows exactly what to work on.
And what's really most important. It keeps your dependencies up to date
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without you having to lift a finger. And lastly, it scans for security vulnerabilities and alerts you early.
So no more nasty surprises later. But why, you ask, is this a big deal?
Well, if you're managing multiple repositories
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or even just one really busy one, MCP takes care of a lot of the routine work that normally eats up developer time. Instead of spending hours on maintenance, your team gets to really focus on what's most important.
For example, it has.
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Everyone has time to focus on actual development. There are fewer bugs slipping through the cracks, or maybe even no, no bugs at all.
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And you can guarantee for cleaner and more consistent code bases. Now let's take a look at another real world MCP use case.
Here's the situation, you run a company that provides online software.
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Your customers often need help, for example password resets. We all know it.
It's annoying. Billing questions, bug reports, technical troubleshooting of all sorts.
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The problem is that normally you'd need a big support team answering emails, looking up customer data, checking logs, and sometimes escalating the issues to the engineer. Here's the solution to this.
With MCP, you connect your agent to all the tools.
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Your support team normally uses the customer database to find user info, the billing system to check payments. The server logs to analyze issues.
The knowledge base to find help articles. The ticketing system to create or update support tickets.
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Because MCP defines a standard way for the AI to talk to all these tools, you don't have to build a custom connection every single time to connect them.
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It's much easier. The AI can see all the data it needs, call the right functions, and handle most support cases automatically.
So, for example, a customer writes hi, I can't log in. It says my subscription expired but I just paid.
The AI,
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using MCP, can then look up the customer's account, check billing records, verify payment status, update the subscription if needed, and then reply: Thanks for reaching out. I've confirmed your payment and reactivated your account.
You should be able to log in now.
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The benefit is that you have faster support for your customers. Less work for your human support team for your mistakes.
Because I always checks all systems the same way, and it's much easier to scale as your company grows.
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So in simple terms, MCP lets your AI talk to your company's systems like a real support agent, but faster 24/7 and without needingcustom code for every system. So what these two real life examples of MCP have shown us is that MCP really is a game changer.
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Those teams who build their applications using MCP will take their applications to a new level.