Mannequin Context Protocol (MCP) servers present a brand new strategy to unify automation and observability throughout hybrid Cisco environments. They permit an AI consumer to routinely uncover and use instruments throughout a number of Catalyst Middle clusters and Meraki organizations.
In case 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 reveals how a single AI consumer routes natural-language queries to the fitting instrument, 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 beneath, Gabi demonstrates how MCP servers allow an AI consumer to work together with instruments throughout a number of platforms. You’ll be taught:
- How the consumer connects to a number of MCP servers and discovers out there instruments.
- 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 video contains sensible walkthroughs of multi-cluster stock lookups, difficulty 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 allows an AI assistant to hook up with a number of MCP servers and dynamically uncover out there instrument definitions. Here’s what the total workflow seems to be like:
- An AI consumer, 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 consumer selects the suitable instrument, 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—similar to “The place is that this consumer 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 greatest 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 similar to stock, occasion lookup, or compliance checks. In addition 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 consumer 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 gather telemetry throughout websites
- Apply guardrails for adjustments, guaranteeing solely trusted runbooks are used for configuration actions
The MCP consumer learns from instrument utilization patterns and might recommend new instruments primarily based on frequent API calls.
Tips on how to 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 the moment
- Tips on how to join MCP to your Cisco platforms
- The varieties of instruments and workflows it helps
- Tips on how to construction your individual instruments utilizing YAML or SDKs
Watch the total replay:
Subscribe to Cisco DevNet on YouTube to get notified when new demos go dwell.
