A New Frontier for Network Engineers

When you first hear about MCP — Model Context Protocol, it feels like one thing constructed for hardcore AI researchers. But right here’s the fact: Network engineers and automation engineers are going to be a few of the greatest customers of it.

In case you’re questioning why: MCP is the way you make Large Language Models (LLMs) perceive your community, your topology, your requirements, your world.

Without it? You’re simply getting generic ChatGPT solutions.

With it? You’re creating Agentic AI that may configure, troubleshoot, and design networks with you.

I’ve been speaking to you — You! …Yes, you! — about community automation and adopting automation in your community engineering for years now. All in all, it’s time so as to add one other brick in *your* wall (of tech instruments). In this AI Break, we’ll discover an instance that demonstrates the worth of utilizing MCP to grasp automation in right this moment’s AI world.

Okay, so what’s MCP?

At its coronary heart, Model Context Protocol is about injecting structured data into an LLM at runtime — mechanically and programmatically.

Instead of manually pasting community diagrams or config templates right into a chat window, MCP lets your instruments inform the mannequin:

• What gadgets are on the community
• What requirements you employ
• What applied sciences you favor (OSPF over EIGRP, EVPN over VXLAN, no matter)
• What change management processes exist

All that context flows into the mannequin, making its responses smarter, extra aligned, and extra helpful on your surroundings.

Let’s begin with a fundamental, real-world instance

Let’s say you’re constructing an LLM-based Network Assistant that helps generate configs. You don’t need it suggesting RIP when your total community runs OSPF and BGP.

With MCP, earlier than you even ask the mannequin for a config, you present AI with the next context:

Look acquainted? Yup, it’s a JSON.

{
  "network_standards": {
    "routing_protocols": ["OSPF", "BGP"],
    "preferred_encapsulation": "VXLAN",
    "security_policies": {
      "ssh_required": true,
      "telnet_disabled": true
    }
  },
  "topology": {
    "core_devices": ["core-sw1", "core-sw2"],
    "edge_devices": ["edge-fw1", "edge-fw2"],
    "site_layout": "hub and spoke"
  }
}

Your assistant mechanically sends this context to the LLM utilizing MCP, and then asks, “Generate a config to onboard a new site.”

The mannequin now solutions in a approach that matches your surroundings— not some random textbook response.

So, what expertise do it’s essential to use MCP?

Honestly, a whole lot of you have already got most of what’s wanted:

• API Fundamentals. You’ll be sending structured context (normally JSON) over API calls — identical to RESTCONF, NETCONF, Catalyst Center, Or Meraki APIs.
• Understanding your community metadata. You have to know what issues: routing, VLANs, safety, gadget varieties, and the best way to characterize that as structured information.
• Python scripting. You’ll most likely use Python to gather this data dynamically (like by way of Nornir, Netmiko, or native APIs) after which package deal it into MCP calls.
• LLM fundamentals. You want to grasp how prompts and context home windows work, and the way greater context equals smarter outputs.

The backside line

MCP isn’t some “maybe later” factor for networkers.

It’s turning into the bridge between your real-world community data and AI’s capacity that will help you quicker, higher, and extra precisely.

Engineers who know the best way to feed actual context into LLMs will dominate community design, troubleshooting, safety auditing, and even full-stack automation.

Start now 

• Map your community requirements.
• Package them as JSON.
• Play with sending that context into small AI workflows.

The finest AI Agents are constructed by engineers who know their community—and know the best way to educate it to their AI. Next, let’s get hands-on with MCP!

Try it

For a completely working code and directions to get began, take a look at my challenge on GitHub.

Create a actual Model Context Protocol (MCP) server designed for community engineers.

This MCP app does the next:

• Serve your community requirements (routing protocols, safety insurance policies, and many others.)
• Respond with gadget well being
• Connect to Claude Desktop, making your AI assistant conscious of your actual community surroundings

And it’s so simple as:

1. Import the MCP Python SDK
   

   from mcp.server.fastmcp import FastMCP

2. Initialize the FastMCP server with a singular identify
   

   mcp = FastMCP("network-assistant")

3. Define instruments.
   Tools are a robust primitive within the Model Context Protocol (MCP). They let your server expose actual actions—so the mannequin can question techniques, run logic, or kick off workflows. In our use case, we have to outline ‘network-standards’ & ‘device status’ features:

   @mcp.device()
   async def get_network_standards() -> dict[str, Any]:
       """Returns commonplace routing protocols, encapsulation, and safety insurance policies."""
   return NETWORK_STANDARDS

4. Run the server, and you might be set!

   if __name__ == "__main__":
       mcp.run(transport="stdio")
   

And if we have a look at it, that is what the LLM is aware of about your community earlier than you contextualized it:

And that is after connecting the LLM to our Network:

Where community automation and AI really collide

You’re now not scripting for the sake of scripting. And you don’t simply use AI for the sake of buzzwords. When you’ll be able to mix stay community state with LLM intelligence, you’re constructing techniques that suppose, adapt, and help with you—not simply for you.

Start easy. Build one movement.
Make your AI agent truly know your community. Because the longer term belongs to engineers who don’t simply automate—they contextualize.

Welcome to the brand new frontier of Agentic AI!

Get began with AI

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