Mannequin Context Protocol (MCP) servers present a brand new strategy to unify automation and observability throughout hybrid Cisco environments. They permit an AI shopper to robotically uncover and use instruments throughout a number of Catalyst Middle clusters and Meraki organizations.
When you’re interested in 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 exhibits 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.
Watch the complete replay:
See MCP in Motion: Catalyst Middle and Meraki Integration
Within the video, 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—one for Catalyst Middle, one for Meraki—and discovers obtainable instruments from each.
- How these instruments are chosen and executed in actual time primarily based on consumer intent.
- How a single question can span clusters and organizations utilizing patterns like cluster = all.
The session consists of sensible walkthroughs of multi-cluster stock lookups, difficulty 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 hook up with a number of MCP servers and dynamically uncover obtainable software definitions. Here’s what the complete workflow appears to be like like:
- An AI shopper, powered by a big language mannequin, connects to a number of MCP servers.
- Every server gives an inventory of instruments—both prebuilt runbooks or auto-generated APIs.
- A consumer asks a query; the AI shopper selects the suitable software, fills within the parameters, and sends the request.
- The instruments execute, return knowledge, and the AI responds to the consumer.
This allows asking a single query—equivalent to “The place is that this shopper linked?”—and receiving solutions from a number of clusters and organizations.
Crucial Instruments vs. Declarative Instruments in MCP Servers
The demo explains two forms 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 vital.
- Declarative instruments are auto-generated from YAML recordsdata and are perfect for read-heavy duties equivalent to 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
- Determine root causes of points equivalent to BGP flaps
- Run compliance checks or acquire telemetry throughout websites
- Apply guardrails for adjustments, making certain solely trusted runbooks are used for configuration actions
The MCP shopper learns from software utilization patterns and might recommend new instruments primarily based on frequent API calls.
How one can Get Began and What’s Subsequent
This demo gives a transparent, sensible introduction to MCP for anybody working in NetOps or DevOps. You’ll achieve a greater understanding of:
- Why MCP issues at present
- How one can join MCP to your Cisco platforms
- The forms of instruments and workflows it helps
- How one can construction your individual instruments utilizing YAML or SDKs
Watch the complete session:
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