Mannequin Context Protocol (MCP) servers present a brand new method to unify automation and observability throughout hybrid Cisco environments. They permit an AI consumer to mechanically uncover and use instruments throughout a number of Catalyst Heart clusters and Meraki organizations.
Should you’re interested by how this works, now’s the time to see it in motion.
On this new demo, Cisco Principal Technical Advertising and marketing Engineer Gabi Zapodeanu reveals how a single AI consumer routes natural-language queries to the appropriate instrument, retrieves responses from a number of domains, and helps you troubleshoot or report in your community extra effectively.
Watch the complete replay:
See MCP in Motion: Catalyst Heart and Meraki Integration
Within the video, 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—one for Catalyst Heart, one for Meraki—and discovers out there instruments from each.
- 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 session contains 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 hook up with a number of MCP servers and dynamically uncover out there instrument 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 supplies 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 knowledge, and the AI responds to the person.
This permits asking a single query—comparable 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 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 comparable to stock, occasion lookup, or compliance checks. Additionally they help pagination with offset and restrict parameters.
Gabi shares examples of each varieties, 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
- Determine root causes of points comparable to BGP flaps
- Run compliance checks or acquire telemetry throughout websites
- Apply guardrails for modifications, making certain solely trusted runbooks are used for configuration actions
The MCP consumer learns from instrument utilization patterns and may recommend new instruments based mostly on frequent API calls.
Learn how to Get Began and What’s Subsequent
This demo supplies a transparent, sensible introduction to MCP for anybody working in NetOps or DevOps. You’ll acquire a greater understanding of:
- Why MCP issues in the present day
- Learn how to join MCP to your Cisco platforms
- The varieties of instruments and workflows it helps
- Learn how to construction your individual instruments utilizing YAML or SDKs
Watch the complete session:
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