From Query to Action with MCP Servers

[ad_1] 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.

Should you’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 reveals how a single AI shopper routes natural-language queries to the proper software, 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 shopper to work together with instruments throughout a number of platforms. You'll be taught:

How the shopper connects to a number of MCP servers—one for Catalyst Heart, one for Meraki—and discovers accessible 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 consists of 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 hook up with a number of MCP servers and dynamically uncover accessible software definitions. Here's what the complete workflow appears like:

An AI shopper, powered by a big language mannequin, connects to a number of MCP servers. Every server supplies a listing of instruments—both prebuilt runbooks or auto-generated APIs. A person asks a query; the AI shopper 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—akin to “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 sorts of instruments supported by MCP servers:

Crucial instruments are predefined sequences written in Ansible, Terraform, or Python. They're finest fitted to write duties the place guardrails and strict execution order are vital. Declarative instruments are auto-generated from YAML information and are perfect for read-heavy duties akin 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 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 Establish root causes of points akin 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 shopper learns from software utilization patterns and might recommend new instruments based mostly on frequent API calls.

Get Began and What’s Subsequent

This demo supplies 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 join MCP to your Cisco platforms The sorts of instruments and workflows it helps construction your individual instruments utilizing YAML or SDKs