Mannequin Context Protocol (MCP) servers present a brand new strategy to unify automation and observability throughout hybrid Cisco environments. They allow an AI shopper to mechanically uncover and use instruments throughout a number of Catalyst Middle clusters and Meraki organizations.
In case you’re interested by how this works, now’s the time to see it in motion.
On this new demo, Cisco Principal Technical Advertising and marketing Engineer Gabi Zapodeanu reveals how a single AI shopper routes natural-language queries to the fitting software, retrieves responses from a number of domains, and helps you troubleshoot or report in your community extra effectively.
See MCP in Motion: Catalyst Middle and Meraki Integration
Within the video under, Gabi demonstrates how MCP servers allow an AI shopper to work together with instruments throughout a number of platforms. You’ll study:
- How the shopper connects to a number of MCP servers and discovers obtainable instruments.
- How these instruments are chosen and executed in actual time based mostly on person intent.
- How a single question can span clusters and organizations utilizing patterns like cluster = all.
The video consists of sensible walkthroughs of multi-cluster stock lookups, problem correlation throughout, and a BGP troubleshooting workflow constructed from fundamental instruments.
Understanding MCP Structure and Workflow
MCP makes use of a client-server protocol that permits an AI assistant to connect with a number of MCP servers and dynamically uncover obtainable software definitions. Here’s what the complete workflow seems to be like:
- An AI shopper, powered by a big language mannequin, connects to a number of MCP servers.
- Every server offers an inventory of instruments—both prebuilt runbooks or auto-generated APIs.
- A person asks a query; the AI shopper selects the suitable software, fills within the parameters, and sends the request.
- The instruments execute, return information, and the AI responds to the person.
This permits asking a single query—reminiscent of “The place is that this shopper related?”—and receiving solutions from a number of clusters and organizations.
Crucial Instruments vs. Declarative Instruments in MCP Servers
The demo explains two sorts of instruments supported by MCP servers:
- Crucial instruments are predefined sequences written in Ansible, Terraform, or Python. They’re finest fitted to write duties the place guardrails and strict execution order are necessary.
- Declarative instruments are auto-generated from YAML information and are perfect for read-heavy duties reminiscent of stock, occasion lookup, or compliance checks. Additionally they assist pagination with offset and restrict parameters.
Gabi shares examples of each sorts, demonstrating their use in actual situations like firmware checks and cross-domain shopper discovery.
Troubleshooting and Compliance Utilizing Generative AI Flows
Past single-tool calls, MCP helps multi-step workflows. These generative AI flows allow you to:
- Correlate occasions
- Establish root causes of points reminiscent of BGP flaps
- Run compliance checks or gather telemetry throughout websites
- Apply guardrails for adjustments, guaranteeing solely trusted runbooks are used for configuration actions
The MCP shopper learns from software utilization patterns and might recommend new instruments based mostly on frequent API calls.
Methods to Get Began and What’s Subsequent
This demo offers a transparent, sensible introduction to MCP for anybody working in NetOps or DevOps. You’ll achieve a greater understanding of:
- Why MCP issues as we speak
- Methods to join MCP to your Cisco platforms
- The sorts of instruments and workflows it helps
- Methods to construction your individual instruments utilizing YAML or SDKs
Watch the complete replay:
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