My First Impressions of MCP Servers: Why They Matter
My First Impressions of MCP Servers: Why They Matter
For the last few months, I’ve been diving deeper into AI tools and the emerging Model Context Protocol (MCP). As a software engineer with a background in kitchens and client work, I’m fascinated not just by the tech, but by how we adapt as humans to these new tools.
This post is my first reflection on MCP servers — what they are, why they’re different, and where I think this is going.
What is MCP, in plain terms?
Most AI systems today work like this:
- You send a prompt.
- The AI responds in text.
- You copy and paste, run, or adapt the output manually.
MCP introduces a shift. Instead of treating the AI like a chatbot, it acts more like a networked teammate with access to tools and context through a defined protocol.
In practice, that means:
- AI can request and use resources (files, APIs, databases) instead of being stuck in isolation.
- You can extend capabilities through MCP servers, plugging in functionality.
- It’s less “ask and receive” → more “collaborate and delegate.”
Why does this matter?
- Reduced friction — less copying, pasting, and re-explaining.
- Safer integrations — clear boundaries for what AI can/can’t access.
- Composable tools — developers can build MCP servers like Lego blocks.
For businesses, this could mean AI isn’t just a helper that generates text — it’s an operator that interacts with systems directly.
My first impressions
- As an engineer: it feels closer to building distributed systems than just “AI apps.”
- As a learner: it lowers the barrier to trying things out without rebuilding infra.
- As a human at work: it sparks big questions — what does collaboration look like when your teammate is an AI running on an MCP server?
Where the future of work comes in
Tools shape how we work. Just like chefs adapt to new recipes or engineers to new frameworks, MCP may become the new kitchen for AI collaboration.
I think we’ll look back at this era as the moment when AI shifted from “a tool you query” → to “a colleague you work with.”
And that’s exciting — but it also means we’ll need to rethink skills, workflows, and even trust in new ways.
Wrapping up
This is just the beginning. I plan to share more notes as I experiment with MCP servers — including practical examples, code snippets, and where I see the pitfalls.
👉 If you’re exploring AI in your business or want to experiment with how it can integrate into your systems, get in touch. I’d love to chat.
Have you played with MCP yet? I’d love to hear your impressions — reach out on LinkedIn or Twitter.