Mannequin Context Protocol (MCP) servers present a brand new method 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.
When you’re inquisitive about 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 consumer routes natural-language queries to the suitable device, 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 obtainable instruments.
- How these instruments are chosen and executed in actual time primarily based 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, subject 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 connect with a number of MCP servers and dynamically uncover obtainable device definitions. Here’s what the total workflow seems like:
- An AI consumer, 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 consumer selects the suitable device, fills within the parameters, and sends the request.
- The instruments execute, return information, and the AI responds to the person.
This allows asking a single query—resembling “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 sorts 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 information and are perfect for read-heavy duties resembling stock, occasion lookup, or compliance checks. In addition they help pagination with offset and restrict parameters.
Gabi shares examples of each varieties, demonstrating their use in actual eventualities 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 resembling BGP flaps
- Run compliance checks or accumulate telemetry throughout websites
- Apply guardrails for modifications, making certain solely trusted runbooks are used for configuration actions
The MCP consumer learns from device utilization patterns and may counsel new instruments primarily based on frequent API calls.
Easy 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 at this time
- Easy methods to join MCP to your Cisco platforms
- The sorts of instruments and workflows it helps
- Easy methods to construction your individual instruments utilizing YAML or SDKs
Watch the total replay:
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