From Question to Motion with MCP Servers

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 routinely uncover and use instruments throughout a number of Catalyst Heart clusters and Meraki organizations.

When you’re interested in 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 proper device, 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 be taught:

• 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 consists of sensible walkthroughs of multi-cluster stock lookups, challenge 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 out there device definitions. Here’s what the total workflow appears to be like like:

1. An AI consumer, powered by a big language mannequin, connects to a number of MCP servers.
2. Every server gives an inventory of instruments—both prebuilt runbooks or auto-generated APIs.
3. A person asks a query; the AI consumer selects the suitable device, fills within the parameters, and sends the request.
4. The instruments execute, return knowledge, and the AI responds to the person.

This allows asking a single query—similar 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 vital.
• Declarative instruments are auto-generated from YAML recordsdata and are perfect for read-heavy duties similar 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
• Establish root causes of points similar to BGP flaps
• Run compliance checks or gather telemetry throughout websites
• Apply guardrails for adjustments, guaranteeing solely trusted runbooks are used for configuration actions

The MCP consumer learns from device utilization patterns and might counsel new instruments primarily based on frequent API calls.

How you 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 acquire a greater understanding of:

• Why MCP issues as we speak
• How you can join MCP to your Cisco platforms
• The kinds of instruments and workflows it helps
• How you can construction your individual instruments utilizing YAML or SDKs

Watch the total replay:

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