Conceptual Article

Practical Uses of Model Context Protocol (MCP)

Published on April 29, 2025

Practical Uses of Model Context Protocol (MCP)

Introduction

If you’ve been following the AI development space lately, you’ve probably heard whispers about Model Context Protocol (MCP). When it first appeared in November 2024, it didn’t make many headlines. Fast forward to May 2025, and suddenly it’s the hottest topic across Twitter, Reddit, and YouTube. Why? MCP represents a fundamental shift in how we interact with AI systems. It’s not just another incremental update—it’s a complete reimagining of what’s possible when AI can actually do things in the real world beyond just generating text.

As someone who went from zero coding knowledge to deploying web applications and landing a product consultant role in just 5 months, I can tell you firsthand: MCP is the bridge many of us have been waiting for. It’s the difference between having AI suggest what you should do and having AI roll up its sleeves and work alongside you. In this guide, I’ll walk you through what MCP actually is, how it’s transforming various workflows, and most importantly, how you can start implementing it yourself—even if you’re not technically inclined.

What is Model Context Protocol?

Model Context Protocol is a canary in a coal mine example for AI Agent tool calling. Its debut in November of 2024 wasn’t met with much fanfare but by March of 2025 everyone on Twitter, Reddit and YouTube were talking about it. More importantly, this goes hand-in-hand with AI Development and the need to standardize building processes. Single-handedly MCP has opened the doors to development with tooling for organizing, planning, and executing coding projects.

One of the most controversial MCP servers is filesystem which allows you to agentically create, delete, move, and organize files and directories. Meaning, once installed you can modify the config.json file and add MCP Servers fairly easily.

The best part is that once certain MCP Servers are in place, the process of adding and formatting additional tools is primarily the same. One of the biggest roadblocks initially was formatting the MCP Servers in a way that would connect correctly, I learned quickly that you can instruct the Client (Claude Desktop) to do it for you. This is how, without any prior knowledge or use of IDEs, coding, or frameworks, I was able to plan, build, and deploy a custom web application to DigitalOcean App Platform. More on that later.

I built my MCP Server tool kit to the point of having nearly 200 tools. The best part, this is how I was able to progress my career and land a product consultant role within only 5 months of ever touching a terminal command.

mcp-flow-diagram

Real-World applications of MCP

When we talk about the practical applications of MCP, it helps to understand how businesses can benefit. Here are five powerful applications that I’ve found particularly useful:

Email and Calendar Management For service providers and course creators, managing communication is often time-consuming. MCP enables AI assistants to draft personalized emails, categorize messages, schedule meetings based on your availability, and send follow-up reminders automatically. Rather than just suggesting text, an MCP-powered assistant can actually access your email system and handle the entire process with minimal oversight.
Content Creation and Management For businesses producing regular content, MCP transforms AI from idea generator to production partner. It can research current topics, draft content matching your brand voice, schedule posts across platforms at optimal times, and even repurpose existing content into new formats. The difference is significant: instead of copying and pasting AI suggestions, MCP allows the assistant to work directly with your content management tools.
Project Management Project platforms like Asana or Trello become powerful when integrated with MCP. You can create tasks based on discussion outcomes, track deadlines automatically, generate progress reports, identify bottlenecks before they impact delivery, and suggest resource reallocation when needed. This means your AI assistant becomes an active project manager rather than just a suggestion tool.
Sales and Marketing Assistance For businesses focused on growth, MCP enables AI to actively participate in sales processes by updating CRM records, analyzing prospect data for personalized outreach, monitoring campaign performance, generating follow-ups, and qualifying leads based on your criteria. Instead of just suggesting copy, an MCP-enabled assistant can implement insights from your recent campaigns.
Multi-System Workflow Automation The most powerful application comes from connecting previously siloed business systems. MCP can synchronize data across marketing, sales, and fulfillment, automate complex workflows spanning multiple platforms, create custom integrations without coding knowledge, establish trigger-based actions, and maintain data consistency across all operations. This level of integration previously required expensive custom development but becomes accessible through MCP-enabled AI assistants.

However, I know there are limitations inherently when building locally. I’ve been curious about the lack of access on mobile hardware and use cases where local building isn’t the most practical. That said, something to consider deeply is using MCP servers requires a Client.

Deployment options

MCP Servers are mostly being implemented locally.

If you want to host it on cloud there’s an opportunity to use DigitalOcean’s products. App Platform and Droplets can easily modify an MCP Server to act more like a REST API.

This could be advantageous in scenarios such as:

No access to needed client
Underpowered hardware
Temporary access

Additionally, doctl (DigitalOcean command line interface) can be used in this instance to seamlessly build and deploy from the terminal. That means the process of deployment doesn’t need to be a hang-up for newer developers, your hosting needs can even be deployed with a simple command directly from your LLM if you are using an MCP Server like the BHT Labs custom server that allows you to access run-command tools. It can be seamless and opens new opportunities for testing the idea of sharing tools without local build considerations.

Framework and resources

This is the framework I built my first suite of tools around. Essentially the MCP Client allows you to access all the tools you need to take advantage of these practical uses and Organized AI as a philosophy means using only the tools needed and maintaining good hygiene in testing and production. Especially while the AI landscape is changing and requiring temporary downloads of installation packages and tools.

One thing that I encountered that I believe is a problem that most businesses will face is downloading locally AI Solutions can burden your machines ability to perform. Unless you are a software and Hardware Pro you might not understand how deeply this affects your workspace and your ability to get things done. I’ve had memory usage warnings pop up multiple times while installing and trying different combinations of AI tooling with many web apps being the culprit. It’s no mystery that these tools are powerful, however it is very difficult to understand how much of a burden they can be on your hardware unless you come from a technical background.

Additional applications

Another use case that I’ve found very interesting is the Mermaid MCP server. This allows me to take ideas and concepts, even projects as a whole and create diagrams that are easy to follow for team members that may not be fully aware of how things connect or intertwine within a project. Even as a thinking tool it’s a huge benefit to be able to see visually how things are connected and the different phases of a project before you even start.

One thing that I will say about coding and product management in general using MCP servers in LLMs and ultimately AI tooling: it has made me better at planning. I’m a more efficient planner because I can get my ideas out and into a format that is easy to understand and easy to see quickly.

FAQs

1. What is MCP?

MCP stands for Model Context Protocol, a standardized protocol that allows AI applications to interact with external tools and data sources. It provides a standardized way for AI models to not only generate text, but also perform tasks such as querying databases, sending emails, and automating workflows, making them more versatile and useful.

2. Why should I use MCP?

MCP enables AI models to perform tasks beyond text generation, such as querying databases, sending emails, and automating workflows, making them more versatile and useful. It allows for seamless integration with various tools and data sources, enhancing the capabilities of AI models and making them more efficient.

3. How do I set up an MCP server?

You can set up an MCP server by following tutorials available online, such as the one provided by DigitalOcean, which guides you through building a server in Python. The process involves setting up the server, configuring the tools and data sources, and establishing the connection with the AI model.

4. Can MCP be used for business applications?

Yes, MCP can be used for various business applications, including email management, content creation, project management, sales assistance, and workflow automation. MCP provides a standardized way to integrate AI models with these business applications, enhancing their capabilities and making them more efficient.

5. Is it possible to deploy MCP servers on the cloud?

Yes, MCP servers can be deployed on cloud platforms like DigitalOcean’s App Platform or Droplets, allowing for remote access and scalability. This makes it easier to manage and scale the server, and provides more flexibility in terms of access and usage.

6. What are the hardware requirements for running MCP locally?

Running MCP locally requires a computer with sufficient processing power and memory, as AI solutions can be resource-intensive. The exact requirements may vary depending on the specific tools and data sources being used, but in general, a computer with a decent amount of RAM and a modern CPU should be sufficient.

7. Are there any limitations to using MCP?

While MCP is powerful, it requires a client to function, and local deployments may face hardware limitations. Additionally, understanding the technical setup can be challenging for non-technical users. However, with the right resources and support, these limitations can be overcome, and the benefits of using MCP can be fully realized.

Conclusion

In conclusion, MCP servers may seem technical and geared towards AI development and coding. However, they can be an awesome use case for business owners who want to utilize AI features in their workspace. With a dedicated client connected to their internal networks and secure in AI tooling, MCP servers can prevent potential issues. If you’re a business owner, consider using organized AI.