If you've been following the evolution of Cisco Modeling Labs (CML), you know we're always looking for ways to make network simulation more accessible, more powerful, and frankly, more fun. Today, I'm excited to share something that brings all those elements together in a way that might just change how you think about building and testing network topologies.

We've just released a Model Context Protocol (MCP) server for CML, and if you haven't heard of MCP yet, you're in for a treat. Think of it as a bridge that lets AI assistants like Claude, VSCode, Cursor, and LM Studio directly interact with your CML environment. What does that mean in practice? It means you can literally talk to your lab environment and watch it come to life.

Creating a network topology in CML using natural language conversation with LM Studio

What is MCP?

The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI applications to interact with external tools and services. It's like giving your AI assistant a set of hands to actually do things rather than just having them tell you how to do them (check out Kareem's blog for a primer on MCP). For CML, this means your AI can create labs, add nodes, configure devices, start topologies, and even run commands on virtual devices-all through natural language conversation.

Architecture diagram showing how MCP connects AI assistants to Cisco Modeling Labs

Getting up and running

One of the coolest aspects of the CML MCP server is how easy it is to get started. You can be up and running in just a few minutes. The most straightforward approach uses uvx, which downloads and runs the server in a standalone environment. Just add a few lines to your favorite MCP client's configuration, provide your CML server credentials, and you're ready to go.

For those who want the full experience, including the ability to execute CLI commands on devices running in your CML labs, you can install the PyATS-enabled version as a Docker container.

So, what can you actually do?

This is where things get interesting. Let me give you a real example of what a conversation with an MCP-enabled AI assistant might look like:

"Create a new CML lab called 'OSPF Study Lab'. Add two IOL routers, connect them through an unmanaged switch, and configure OSPF between them using the 192.0.2.0/24 subnet."

That's it. Your AI assistant will work with the MCP server to create the lab, add the nodes, connect them, and configure OSPF. Then you'll have a working topology ready to start. Want to validate that OSPF is working? Just ask to check the neighboring relationships. Need to add an annotation to document the topology? Tell the AI assistant what you want and where you want it.

The MCP server supports a comprehensive set of operations:

• Creating and managing lab topologies
• Adding nodes and connecting them with interfaces and links
• Applying link conditioning (bandwidth, latency, jitter, packet loss)
• Configuring devices with startup configurations
• Starting, stopping, and wiping labs and individual nodes
• Executing CLI commands on running devices through PyATS
• Adding visual annotations to document your topologies
• Managing CML users and groups

Example of a complete network topology created with CML MCP, featuring multiple device types and visual annotations

Why this matters for learning and testing

If you're studying for CCNA, CCNP, or CCIE certifications, the CML MCP server can dramatically speed up your lab creation process. Instead of clicking through the UI to build a topology, you can describe what you want to learn or test, and the AI will build it for you. Want to test a specific OSPF scenario? Describe it. Need to validate BGP route preference? Just ask.

For network engineers testing configurations or troubleshooting scenarios, the ability to quickly spin up complex topologies through conversation is a game-changer. You can focus on the networking concepts and configurations rather than the mechanics of building the lab environment.

The power of automation meets conversational AI

What makes the MCP server particularly powerful is how it brings together CML's automation capabilities with the natural language understanding of modern AI. Behind the scenes, it's using the CML API and PyATS to do the heavy lifting, but you don't need to know Python or YAML (or even have to use your mouse!) to benefit from it. You just need to be able to describe what you want.

The server is built using FastMCP 2.0, which provides a robust framework for implementing MCP servers. It's open source and available on GitHub, so if you're curious about how it works or want to contribute enhancements, submit an issue or create a pull request.

Looking ahead

We're at the beginning of something really exciting here. The intersection of AI agents and network simulation opens up possibilities we're only starting to explore. Imagine describing a complex enterprise network topology with multiple sites, VPNs, and redundancy requirements (and having it built automatically) or asking your AI assistant to help troubleshoot why a particular protocol isn't working as expected, with the ability to actually check the configurations and states of your virtual devices.

The CML MCP server is in minutes. Whether you're using the free version of CML or have a subscription, this tool can enhance your workflow and make network simulation more accessible than ever.

If you're looking to start labbing the easy way, the CML MCP server is definitely worth checking out. Head over to the GitHub repository, follow the getting started guide, and see what you can build with just a conversation.

Happy labbing!

 

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