{"id":15274,"date":"2025-05-15T05:46:35","date_gmt":"2025-05-15T05:46:35","guid":{"rendered":"https:\/\/dmsretail.com\/RetailNews\/a-new-frontier-for-network-engineers\/"},"modified":"2025-05-15T05:46:35","modified_gmt":"2025-05-15T05:46:35","slug":"a-new-frontier-for-network-engineers","status":"publish","type":"post","link":"https:\/\/dmsretail.com\/RetailNews\/a-new-frontier-for-network-engineers\/","title":{"rendered":"A New Frontier for Network Engineers"},"content":{"rendered":"

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When you first hear about MCP \u2014 Model Context Protocol<\/strong>, it sounds like something built for hardcore AI researchers. But here\u2019s the reality: Network engineers and automation engineers<\/strong> are going to be some of the biggest users of it.<\/p>\n

In case you\u2019re wondering why: MCP is how you make Large Language Models (LLMs)<\/strong> understand your network, your topology, your standards, your world<\/em>.<\/p>\n

Without it? You\u2019re just getting generic ChatGPT answers.<\/p>\n

With it? You\u2019re creating Agentic AI<\/strong> that can configure, troubleshoot, and design networks with you.<\/p>\n

I have been talking to you \u2014 You! \u2026Yes, you!<\/em> \u2014 about network automation and adopting automation in your network engineering for years now. All in all, it\u2019s time to add another brick in *your* wall (of tech tools). In this AI Break, we\u2019ll explore an example that demonstrates the value of using MCP to master automation in today\u2019s AI world.<\/p>\n

Okay, so what is MCP?<\/strong><\/h2>\n

At its heart, Model Context Protocol<\/strong> is about injecting structured knowledge<\/strong> into an LLM at runtime \u2014 automatically and programmatically<\/strong>.<\/p>\n

Instead of manually pasting network diagrams or config templates into a chat window, MCP lets your tools tell the model:<\/p>\n

    \n
  • What devices are on the network<\/li>\n
  • What standards you use<\/li>\n
  • What technologies you prefer (OSPF over EIGRP, EVPN over VXLAN, whatever)<\/li>\n
  • What change control processes exist<\/li>\n<\/ul>\n

    All that context flows into the model, making its responses smarter, more aligned, and more useful for your environment.<\/p>\n

    Let\u2019s start with a basic, real-world example <\/strong><\/h3>\n

    Let\u2019s say you\u2019re building an LLM-based Network Assistant<\/strong> that helps generate configs. You don\u2019t want it suggesting RIP<\/strong> when your entire network runs OSPF and BGP<\/strong>.<\/p>\n

    With MCP, before you even ask the model for a config, you provide AI with the following context:<\/p>\n

    Look familiar? Yup, it\u2019s a JSON.<\/p>\n

    {
\n  \"network_standards\": {
\n \u00a0\u00a0 \"routing_protocols\": [\"OSPF\", \"BGP\"],
\n \u00a0\u00a0 \"preferred_encapsulation\": \"VXLAN\",
\n \u00a0\u00a0 \"security_policies\": {
\n \u00a0\u00a0\u00a0\u00a0 \"ssh_required\": true,
\n \u00a0\u00a0\u00a0\u00a0 \"telnet_disabled\": true
\n \u00a0\u00a0 }
\n  },
\n  \"topology\": {
\n \u00a0\u00a0 \"core_devices\": [\"core-sw1\", \"core-sw2\"],
\n \u00a0\u00a0 \"edge_devices\": [\"edge-fw1\", \"edge-fw2\"],
\n \u00a0\u00a0 \"site_layout\": \"hub and spoke\"
\n  }
\n}
\n<\/pre>\n
    Your assistant automatically<\/em> sends this context to the LLM using MCP, and then<\/strong> asks, \u201cGenerate a config to onboard a new site.\u201d<\/p>\n
    The model now answers in a way that fits your environment<\/strong>\u2014 not some random textbook response.<\/p>\n
    So, what skills do you need to use MCP?<\/strong><\/h2>\n
    Honestly, a lot of you already have most of what\u2019s needed:<\/p>\n
      \n
    • API Fundamentals. <\/strong>You\u2019ll be sending structured context (usually JSON) over API calls \u2014 just like RESTCONF, NETCONF, Catalyst Center, Or Meraki APIs.<\/li>\n
    • Understanding your network metadata.<\/strong> You need to know what matters: routing, VLANs, security, device types, and how to represent that as structured data.<\/li>\n
    • Python scripting.<\/strong> You\u2019ll probably use Python to collect this info dynamically (like via Nornir, Netmiko, or native APIs) and then package it into MCP calls.<\/li>\n
    • LLM basics. <\/strong>You need to understand how prompts and context windows work, and how bigger context equals smarter outputs.<\/li>\n<\/ul>\n
      The bottom line<\/strong><\/h3>\n
      MCP isn\u2019t some \u201cmaybe later\u201d thing for networkers.<\/p>\n
      It\u2019s becoming the bridge<\/strong> between your real-world network knowledge and AI\u2019s ability to help you faster, better, and more accurately.<\/p>\n
      Engineers who know how to feed real context into LLMs<\/strong> will dominate network design, troubleshooting, security auditing, and even full-stack automation.<\/p>\n
      Start now<\/strong>\u00a0<\/strong><\/h3>\n
        \n
      • Map your network standards.<\/li>\n
      • Package them as JSON.<\/li>\n
      • Play with sending that context into small AI workflows.<\/li>\n<\/ul>\n
        The best AI Agents are built by engineers who know their network\u2014and know how to teach it to their AI. Next, let\u2019s get hands-on with MCP!<\/p>\n
        Try it<\/strong><\/h2>\n
        For a fully working code and instructions to get started, check out my project on GitHub.<\/em><\/span><\/p>\n
        Create a real Model Context Protocol (MCP)<\/strong> server designed for network engineers<\/strong>.<\/p>\n
        This MCP app does the following:<\/strong><\/p>\n
          \n
        • Serve your network standards<\/strong> (routing protocols, security policies, etc.)<\/li>\n
        • Respond with device health<\/strong><\/li>\n
        • Connect to Claude Desktop<\/strong>, making your AI assistant aware of your real network environment<\/li>\n<\/ul>\n
          And it\u2019s as simple as:<\/strong><\/p>\n
            \n
          1. Import the MCP Python SDK
            <\/em><\/em>\n
            from<\/span> mcp.server.fastmcp import<\/span> FastMCP<\/span><\/pre>\n<\/li>\n
          2. Initialize the FastMCP server with a unique name
            <\/em><\/em>\n
            mcp =<\/span> FastMCP<\/span>(\"network-assistant\"<\/span>)<\/pre>\n<\/li>\n
          3. Define tools<\/strong>.
            Tools<\/strong> are a powerful primitive in the Model Context Protocol (MCP). They let your server expose real actions\u2014so the model can query systems, run logic, or kick off workflows. In our use case, we need to define \u2018network-standards\u2019 & \u2018device status\u2019 functions:\n
            @mcp<\/span>.tool<\/span>()<\/span>
\nasync def<\/span> get_network_standards<\/span>() -><\/span> dict[str, Any]:
\n\u00a0\u00a0\u00a0 \"\"\"Returns standard routing protocols, encapsulation, and security policies.\"\"\"<\/span>
\nreturn<\/span> NETWORK_STANDARDS<\/span><\/pre>\n<\/li>\n
          4. Run the server, and you are set!\n
            if<\/span> __name__ ==<\/span> \"__main__\"<\/span>:
\n \u00a0\u00a0 mcp.run<\/span>(transport=<\/span>\"stdio\"<\/span>)
\n<\/pre>\n<\/li>\n<\/ol>\n
            And if we look at it, this is what the LLM knows about your network before<\/em> you contextualized it:<\/p>\n
            \"\"
            \u00a0<\/p>\n
            And this is after<\/em> connecting the LLM to our Network:<\/p>\n
            \"\"
            Where network automation and AI truly collide<\/strong><\/h2>\n
            You\u2019re no longer scripting for the sake of scripting. And you don\u2019t just use AI for the sake of buzzwords. When you can blend live network state with LLM intelligence<\/strong>, you\u2019re building systems that think, adapt, and assist with <\/em>you\u2014not just for<\/em> you.<\/p>\n
            Start simple. Build one flow.
            Make your AI agent actually know your network<\/strong>. Because the future belongs to engineers who don\u2019t just automate\u2014they contextualize<\/strong>.<\/p>\n
            Welcome to the new frontier of Agentic AI!<\/p>\n
            Get started with AI<\/strong><\/h2>\n
            Learning Paths, courses, free tutorials, and more. Unlock the future of technology with artificial intelligence training in Cisco U. Explore AI learning and start building your skills today.<\/p>\n