Mannequin Context Protocol (MCP) servers present a brand new method to unify automation and observability throughout hybrid Cisco environments. They allow an AI consumer to robotically uncover and use instruments throughout a number of Catalyst Heart clusters and Meraki organizations.
In case you’re interested by how this works, now’s the time to see it in motion.
On this new demo, Cisco Principal Technical Advertising Engineer Gabi Zapodeanu exhibits how a single AI consumer routes natural-language queries to the precise software, 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 beneath, Gabi demonstrates how MCP servers allow an AI consumer to work together with instruments throughout a number of platforms. You’ll study:
- 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 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, concern 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 out there software 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 a listing of instruments—both prebuilt runbooks or auto-generated APIs.
- A person asks a query; the AI consumer selects the suitable software, fills within the parameters, and sends the request.
- The instruments execute, return knowledge, and the AI responds to the person.
This allows asking a single query—corresponding 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 suited 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 corresponding 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
- Determine root causes of points corresponding to BGP flaps
- Run compliance checks or accumulate telemetry throughout websites
- Apply guardrails for modifications, guaranteeing solely trusted runbooks are used for configuration actions
The MCP consumer learns from software utilization patterns and might recommend new instruments based mostly 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 acquire a greater understanding of:
- Why MCP issues immediately
- join MCP to your Cisco platforms
- The varieties of instruments and workflows it helps
- construction your personal instruments utilizing YAML or SDKs
Watch the total replay:
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