Mannequin Context Protocol (MCP) servers present a brand new strategy to unify automation and observability throughout hybrid Cisco environments. They allow 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 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 under, 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 contains 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 permits an AI assistant to hook up with a number of MCP servers and dynamically uncover obtainable instrument definitions. Here’s what the total workflow appears 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 instrument, 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—equivalent to “The place is that this consumer related?”—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 necessary.
- 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 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 equivalent to BGP flaps
- Run compliance checks or acquire 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.
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 immediately
- join MCP to your Cisco platforms
- The forms of instruments and workflows it helps
- construction your individual instruments utilizing YAML or SDKs
Watch the total replay:
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