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 Heart clusters and Meraki organizations.
For those who’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 reveals how a single AI shopper routes natural-language queries to the suitable instrument, retrieves responses from a number of domains, and helps you troubleshoot or report in your community extra effectively.
See MCP in Motion: Catalyst Heart 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 be taught:
- How the shopper connects to a number of MCP servers and discovers out there 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 primary 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 out there instrument definitions. Here’s what the complete workflow appears like:
- An AI shopper, powered by a big language mannequin, connects to a number of MCP servers.
- Every server supplies an inventory of instruments—both prebuilt runbooks or auto-generated APIs.
- A person asks a query; the AI shopper selects the suitable instrument, fills within the parameters, and sends the request.
- The instruments execute, return knowledge, and the AI responds to the person.
This permits asking a single query—similar 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 varieties 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 essential.
- Declarative instruments are auto-generated from YAML recordsdata and are perfect for read-heavy duties similar 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 eventualities 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 similar to BGP flaps
- Run compliance checks or accumulate telemetry throughout websites
- Apply guardrails for adjustments, making certain solely trusted runbooks are used for configuration actions
The MCP shopper learns from instrument utilization patterns and might recommend new instruments based mostly on frequent API calls.
How one can Get Began and What’s Subsequent
This demo supplies 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 varieties of instruments and workflows it helps
- How one can construction your individual instruments utilizing YAML or SDKs
Watch the complete replay:
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