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 the event 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 exhibits how a single AI consumer routes natural-language queries to the suitable software, 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 study:
- How the consumer connects to a number of MCP servers and discovers accessible instruments.
- How these instruments are chosen and executed in actual time based mostly 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, 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 allows an AI assistant to connect with a number of MCP servers and dynamically uncover accessible software definitions. Here’s what the complete workflow seems to be 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 consumer asks a query; the AI consumer selects the suitable software, fills within the parameters, and sends the request.
- The instruments execute, return information, and the AI responds to the consumer.
This allows asking a single query—comparable 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 forms 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 information and are perfect for read-heavy duties comparable to stock, occasion lookup, or compliance checks. In addition they help pagination with offset and restrict parameters.
Gabi shares examples of each sorts, 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 comparable to BGP flaps
- Run compliance checks or acquire 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 may counsel new instruments based mostly on frequent API calls.
How one can 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 present
- How one can join MCP to your Cisco platforms
- The forms of instruments and workflows it helps
- How one can construction your personal instruments utilizing YAML or SDKs
Watch the complete replay:
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