Understanding MCP (Model Context Protocol) — A Simple Guide from a DevOps Engineer
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Understanding MCP (Model Context Protocol) — A Simple Guide from a DevOps Engineer

A few weeks ago, I came across something called MCP — the Model Context Protocol — while diving deeper into how LLMs (like ChatGPT) understand and interact with different kinds of information.

I thought, “Okay, another AI buzzword?” But when I actually took the time to learn what it is, it clicked — and it’s something I wish more engineers (especially folks in DevOps, product, and platform teams) understood.

So, here’s my take on what MCP is, in plain language, with some examples and thoughts on why it matters.

So, what is MCP?

MCP (Model Context Protocol) is a standard way to define what the model knows, how it behaves, and what tools or memory it can use — all inside a single structured message.

Think of MCP as the instruction manual + context + toolbox all packed together and handed to a large language model (LLM) like GPT-4.

It’s not a new language or API. It’s more like a shared format that makes it easier for systems and models to talk in a consistent and rich way.

A Real-World Analogy

Let’s say you hire a consultant. You don’t just throw them into a project and hope for the best. You:

  • Give them background on the company (context)

  • Explain the goals and rules (instructions)

  • Hand them access to the tools they’ll need (memory, APIs)

MCP is how we do that — but for an AI model instead of a human.

It’s one file (JSON-based) that says:

“Hey model, here’s who you are, what you know, what you can do, and what this conversation is about.”

What’s Inside an MCP Message?

Here’s a simplified breakdown:

1. System Message

This is like setting the personality or behavior:

"system_message": "You are a helpful DevOps assistant who speaks clearly and explains things with real-world analogies."

2. Files (Context)

You can attach documents or structured data for the model to use:

"files": [
  { "name": "deployment_pipeline.md", "content": "..." }
]

3. Tool Definitions

The model can be told about tools it can use — like calling an API or running a script:

"tools": [
  { "name": "terraform_checker", "description": "Validates Terraform files for errors." }
]

4. Memory or Context Threads

The model can carry past conversations forward — not just as text, but as structured memory:

"context": [
  { "type": "message", "content": "User previously asked about AKS deployment best practices." }
]

Why It Matters (Especially for Devs, DevOps, and Product Teams)

I used to think of ChatGPT or other LLMs as just “chatbots with good memory.” But MCP flips that.

Now, I can:

  • Customize how an LLM responds to my team (e.g. in docs, Slackbots, dashboards)

  • Feed in files and docs with structure instead of copy-pasting

  • Make the model act like a context-aware assistant, not just a smart autocomplete

It’s a building block toward more personalized, powerful, and tool-augmented AI agents.

Imagine This Use Case:

Let’s say I’m building a DevOps assistant for my team that:

  • Knows our infra stack (Terraform, Helm, AKS)

  • Has access to our documentation

  • Can explain policies and run terraform validate

With MCP, I can:

  • Define the model persona (“You are an SRE coach for our company”) ✅

  • Feed it context from our README.md, aks_policies.yaml ✅

  • Let it call out to a terraform_linter tool when asked ✅

Now that’s not just chat — that’s a context-rich agent.

Thoughts!

MCP might sound like a niche protocol for AI nerds, but it’s really a bridge between:

  • How humans think

  • How tools behave

  • And how models can help

If you’re building internal tooling, experimenting with LLMs, or thinking about AI copilots in your team — understanding MCP is your entry ticket into building smarter systems that don’t just guess, but know.