From Question to Motion with MCP Servers -

Mannequin Context Protocol (MCP) servers present a brand new strategy to unify automation and observability throughout hybrid Cisco environments. They allow an AI consumer to robotically uncover and use instruments throughout a number of Catalyst Middle clusters and Meraki organizations.

In the event you’re inquisitive about 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 exhibits how a single AI consumer 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.

See MCP in Motion: Catalyst Middle and Meraki Integration

Within the video under, 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 based mostly 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, 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 connect with a number of MCP servers and dynamically uncover out there software 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 offers a listing of instruments—both prebuilt runbooks or auto-generated APIs.
A consumer asks a query; the AI consumer selects the suitable software, 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—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 forms of instruments supported by MCP servers:

Crucial instruments are predefined sequences written in Ansible, Terraform, or Python. They’re finest 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 similar to stock, occasion lookup, or compliance checks. Additionally they assist pagination with offset and restrict parameters.

Gabi shares examples of each varieties, demonstrating their use in actual eventualities 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 similar to BGP flaps
Run compliance checks or gather telemetry throughout websites
Apply guardrails for modifications, guaranteeing solely trusted runbooks are used for configuration actions

The MCP consumer learns from software utilization patterns and may counsel new instruments based mostly on frequent API calls.