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 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 best 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 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, concern correlation throughout, and a BGP troubleshooting workflow constructed from fundamental 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 software definitions. Here’s what the complete workflow appears to be like 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 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 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 kinds 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 essential.
- 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 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 equivalent to BGP flaps
- Run compliance checks or accumulate telemetry throughout websites
- Apply guardrails for adjustments, guaranteeing solely trusted runbooks are used for configuration actions
The MCP consumer learns from software utilization patterns and may recommend new instruments primarily based on frequent API calls.
The way 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 right now
- The way to join MCP to your Cisco platforms
- The kinds of instruments and workflows it helps
- The way to construction your individual instruments utilizing YAML or SDKs
Watch the complete replay:
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