Ever felt overwhelmed by the flood of tech buzzwords taking over your social media feeds? I certainly have. Recently, “MCP” has been trending everywhere, but most people (including myself initially) have no idea what it means or why it matters.
After diving deep into this topic with Guy Eaton, an expert in ML architecture, I’m excited to share what I’ve learned about Model Context Protocol in plain English.
Key Takeaways:
- MCPs solve a critical problem by creating a standard communication layer between LLMs and external tools
- This standardization will make AI assistants dramatically more capable and reliable
- The MCP ecosystem creates new opportunities for developers and businesses
What Are MCPs and Why Should You Care?
Have you ever asked ChatGPT to send an email for you, only to be told “Sorry, I can’t do that”? That’s because large language models (LLMs) by themselves are extremely limited.
They’re good at one thing: predicting what text should come next.
Let me put this in perspective.
An LLM without tools is like having a brilliant consultant who can only talk about things but never actually do anything for you.
The real magic happens when we connect LLMs to tools – like search engines, databases, or email services.
This is what makes AI assistants useful in the real world.
The Problem MCPs Are Solving
Today’s AI tools face a major challenge: connecting LLMs to external services is messy and complicated.
Think of it this way: each tool speaks a different “language.” One speaks Spanish, another Japanese, and another French. Getting them all to work together becomes a nightmare for developers.
This is why we don’t have Iron Man’s JARVIS yet – connecting all these tools in a cohesive way is incredibly difficult.
How Model Context Protocol Works
Model Context Protocol (MCP) is essentially a universal translator between LLMs and various tools.
It creates a standardized way for AI models to interact with external services.
The MCP Ecosystem Has Four Main Parts:
- MCP Client – The interface facing the LLM (examples include Tempo, Windsurf, Cursor)
- Protocol – The standardized communication method
- MCP Server – Translates between the protocol and external services
- Service – The actual tool (like a database, search engine, etc.)
What makes this brilliant is how Anthropic designed the system.
The MCP server is built by service providers themselves.
This means if you create a database service, you’re responsible for making it compatible with the MCP standard.
Anthropic essentially said: “We want our LLMs to be more capable, but it’s your job to make your services compatible.”
Why This Matters for Everyone
For regular users, MCPs mean AI assistants will become much more useful.
Instead of just answering questions, they’ll be able to:
- Update your spreadsheets
- Send emails on your behalf
- Create entries in databases
- Perform complex workflows across multiple services
And they’ll do this more reliably and with fewer errors than current solutions.
Current Limitations
It’s not all perfect yet. Setting up MCPs can be technically challenging:
- Installation requires downloading and configuring files
- There are still bugs to work out
- The standard might evolve further
But once these kinks are worked out, we’ll enter a new era of AI capability.
Business Opportunities in the MCP Space
When asked about business opportunities around MCPs, Guy Eaton shared two interesting perspectives:
For Technical Founders:
One idea is creating an “MCP App Store” – a platform where people can browse different MCP servers, easily install them, and get a specific URL to paste into their MCP client.
For Non-Technical Entrepreneurs:
The best strategy is to closely monitor how these standards develop.
Since we’re in early stages, it’s about watching and learning rather than jumping in immediately.
As Eaton put it: “This is one of those things where you sit and watch and observe. When the right thing at the right time happens, you strike.”
Real-World MCP Use Cases
Guy Eaton shared some practical applications of Model Context Protocol that show why it’s generating so much excitement.
Document Management and Analysis
An MCP-enabled assistant could:
- Access your document storage system (like Dropbox or Google Drive)
- Find specific files based on content, not just file names
- Extract information across multiple documents
- Update spreadsheets with data from meetings or conversations
- Create new documents using templates and information from various sources
For example, you ask your AI assistant: “Create a quarterly report using data from our financial spreadsheets and include the key points from last quarter’s team presentations.”
The MCP framework allows the AI to seamlessly access all these different systems.
Customer Support Automation
MCPs can transform customer service by:
- Connecting to your CRM system to access customer history
- Searching knowledge bases for accurate solutions
- Creating support tickets with precise categorization
- Following up with customers through email integration
- Updating inventory systems when issues relate to product availability
Rather than just answering questions generally, an MCP-enabled assistant can take actions like: “I’ve looked up your account, created a return authorization, and emailed you a shipping label.”
Development and Engineering
For developers, MCPs open up capabilities like:
- Connecting to GitHub to check code repositories
- Running tests on code snippets
- Accessing documentation across multiple services
- Managing deployment processes
- Monitoring system health and responding to alerts
A developer might ask their assistant: “Show me recent commits that changed the authentication module and check if they pass our security tests.” With MCP, the assistant can perform these actions instead of just describing how to do them.
Personal Productivity
For individuals, MCP enables:
- Calendar management that actually creates or reschedules appointments
- Email organization that files messages and drafts responses
- Travel booking that compares options and makes reservations
- Financial management that categorizes expenses and generates reports
- Health tracking that integrates with fitness apps and medical portals
Instead of telling you “Here’s how to book a flight,” an MCP-enabled assistant can say “I’ve booked your flight, added it to your calendar, and sent the itinerary to your team.”
Creative Workflows
For content creators, MCPs allow:
- Access to digital asset management systems
- Integration with editing tools
- Publishing to various platforms
- Analysis of audience engagement
- Research across specialized databases
A creator might say: “Find similar videos to my latest project, analyze which performed best, and schedule a post about my new content for the optimal time.”
Why These Use Cases Matter
The key difference with MCP is moving from AI that can only talk about doing things to AI that can actually do them. This fundamental shift means:
- Reduced context switching between different tools
- End-to-end workflows instead of fragmented assistance
- Persistent memory across different services
- Fewer handoffs between humans and AI
Each of these capabilities existed before in some form, but they required extensive custom integration work. MCP makes them accessible through a standardized approach, dramatically reducing the technical barriers.
As more services build MCP servers, we’ll see increasingly powerful combinations of tools working together through a single conversational interface – bringing us much closer to that JARVIS-like assistant we’ve been waiting for.
How MCPs Compare to Previous Tech Standards
Throughout tech history, new standards and protocols have created massive business opportunities:
| Protocol | What It Enabled |
|---|---|
| HTTP/HTTPS | The modern web |
| SMTP | Email communication |
| TCP/IP | The internet itself |
MCPs could have a similar impact on AI applications. They solve a fundamental problem that’s holding back more advanced AI assistants.
Final Thoughts
Model Context Protocol is simply a standard that allows AI models to communicate effectively with external tools and services.
While it may sound technical, its impact will be felt by everyone who uses AI tools.
We’re still in the early days, and the standards might evolve. But understanding MCPs gives you insight into why your AI assistants will suddenly become much more capable in the near future.
For those building in the AI space, keeping a close eye on these developments could reveal the next big opportunity.
Sometimes the most revolutionary advances aren’t flashy new technologies, but standards that help existing technologies work together seamlessly.
Please share your thoughts and experience about MCPs!!
