Behind the Buzzword: What is MCP?

Another month, another bunch of buzzwords for tech professionals to deal with. Right now there’s a lot of excitement and noise around MCP, a technology some see as the missing piece that will finally make AI practical and connected in enterprise environments. But is this a useful advancement or just another drip in the greater wave of AI hype? Let’s break it down.

First, what exactly is MCP?

MCP (Model Context Protocol), often referred to as MCP Server, is an open standard that you can use to allow your LLMs to interact with external tools, systems, and data sources. Instead of complex custom integrations, MCP makes everything feel plug and play. It acts as a universal adapter that lets AI seamlessly access company files, databases, apps, or dev environments.

MCP is made up of three core components:

• The MCP Host: This is the AI application that manages one or more MCP clients.

• The MCP Client: The client connects to MCP servers and passes along context for the host to use.

• The MCP Server*: The server provides that context, exposing data and actions in a structured way.

Together, they make it much easier for LLMs and agents to interact with enterprise systems.

Note: Even though MCP and an MCP Server are technically different things, these terms are often used interchangeably in industry conversations right now.

The rising popularity of MCP

MCP was conceived by Anthropic in November 2024, and has since quickly gained industry momentum, with Google DeepMind, OpenAI, and a wave of other companies jumping onboard. 

MCP has grown so fast that dedicated marketplaces and repositories now exist. For example:

• MCP Market offers a hub of available MCP servers, a leaderboard of MCP servers, and documentation about them.

• MCP Hub on Docker is quickly becoming the largest library of containerized MCP servers. Similar to Docker Hub’s container images, MCP Hub provides hundreds of ready to run servers you can add directly to Docker Desktop.

What are the benefits of MCP?

• For developers: Faster AI integration, better context for AI apps and agents, reduced complexity (Compared to APIs, which we’ll cover later.)

• For end users: Smarter assistants that can act on real world systems with less friction.

Real world use cases for MCP

The power of MCP really shows up when you think about how they can streamline workflows across industries and roles. 

1. Customer Support Automation

An AI agent can connect to ticketing systems, CRMs, and knowledge bases through MCP servers. This allows it to surface answers, update tickets, or escalate issues without a human rep juggling multiple tools.

2. Financial Services

An investment app can use MCP servers to combine live market data, portfolio details, and compliance rules. Advisors would get tailored insights in one place instead of bouncing between platforms.

3. Healthcare Administration

Hospitals can use MCP servers to connect scheduling, lab results, and insurance systems. Staff could simply ask for a patient’s next appointment or lab results and get answers instantly.

4. DevOps and IT Operations

AI assistants can tie into monitoring tools, CI/CD pipelines, and incident platforms via MCP servers. They could restart services, roll back deployments, or draft incident reports—all triggered by natural language commands. 

MCP vs APIs: Why use MCP when APIs already exist?

This is a fair and extremely common question. The answer is that using MCP is often faster, easier, and more scalable than using APIs, which is why they were invented in the first place! Let’s break down why.

Using an API for AI-driven applications and workflows

Using an API in this use case requires a lot of manual work:

• Developers must study the documentation to understand available routes, supported calls, authentication methods, rate limits, and how to structure requests. 

• Once you make a call, you still need to parse the raw response, clean up the data, and figure out how to use it in your application. 

• On top of that, APIs are constantly changing. Endpoints get deprecated, new versions roll out, and breaking changes can cause existing integrations to fail without warning. 

Managing these moving parts takes time, introduces complexity, and often requires ongoing maintenance just to keep things running smoothly. 

Using MCP for AI-driven applications and workflows

With MCP, much of that heavy lifting mentioned above is abstracted away:

• Instead of combing through API documentation or writing boilerplate code to handle authentication and response parsing, you simply prompt the AI in natural language. 

• The MCP server already knows how to talk to the underlying system, translate your request into the correct operations, and send back usable results. It is more plug and play, enabling faster integration and reducing the learning curve. 

Developers and teams can focus on what they want to achieve, not on the mechanics of how to make the systems talk to each other. 

Do MCPs make APIs obsolete?

No. APIs remain critical for traditional software integrations, automation, and scenarios where AI is not involved. But when your application or workflow relies on AI, MCP is often the better option. They streamline access, reduce friction, and dramatically speed up time to value. 

In many ways, MCP is doing for AI what APIs did for web services: creating a standard, widely adopted way to connect and unlock innovation.

Conclusion: MCP is not a buzzword, but a beneficial tool

MCP represents a real step forward in making AI more useful, connected, and practical. By standardizing how AI interacts with systems, MCP lowers the barrier to integration, speeds up development, and opens the door to powerful new use cases. Instead of spending weeks wiring up APIs, you can now plug an AI into your tools in minutes.

Will MCP replace APIs entirely? No. But for AI driven applications, MCP is rapidly becoming the go to choice. As more companies adopt the protocol and marketplaces grow, expect MCP to become as foundational to AI as APIs are to web apps today.

I hope you enjoyed this blog, and you take an AI application and an MCP server for a test run!

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