Mannequin Context Protocol (MCP) servers present a brand new strategy to unify automation and observability throughout hybrid Cisco environments. They allow an AI shopper to routinely uncover and use instruments throughout a number of Catalyst Heart clusters and Meraki organizations.
For those who’re inquisitive about 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 shopper routes natural-language queries to the best instrument, 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 shopper to work together with instruments throughout a number of platforms. You’ll study:
- How the shopper connects to a number of MCP servers and discovers obtainable instruments.
- How these instruments are chosen and executed in actual time primarily based 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, difficulty 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 connect with a number of MCP servers and dynamically uncover obtainable instrument definitions. Here’s what the total workflow seems to be like:
- An AI shopper, powered by a big language mannequin, connects to a number of MCP servers.
- Every server gives a listing of instruments—both prebuilt runbooks or auto-generated APIs.
- A consumer asks a query; the AI shopper selects the suitable instrument, 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—reminiscent of “The place is that this shopper related?”—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 fitted 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 reminiscent of stock, occasion lookup, or compliance checks. Additionally they help pagination with offset and restrict parameters.
Gabi shares examples of each sorts, demonstrating their use in actual situations like firmware checks and cross-domain shopper 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 reminiscent of 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 shopper learns from instrument utilization patterns and may recommend new instruments primarily based on frequent API calls.
How one can 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 at present
- How one can join MCP to your Cisco platforms
- The varieties of instruments and workflows it helps
- How one can construction your personal instruments utilizing YAML or SDKs
Watch the total replay:
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