Ansible Development Tools MCP Server
Solution overview
An AI agent can draft an Ansible playbook in seconds that looks like production quality, but that doesn't prove the playbook works. This lab arms you with the Ansible Dev Tools MCP Server which exposes tools you can use to close that gap.
The Model Context Protocol (MCP) gives an AI agent a standard way to call real tools instead of answering from memory. In this lab, you connect an agent to the Ansible Dev Tools MCP server, then learn to harness it for the the Ansible role creation lifecycle: scaffold, lint, best-practice check, outcome test, and publish. Each step calls the real binary already staged on your jumpbox, ansible-creator, ansible-lint, ansible-navigator, and returns its output. When the agent reports the repo lints clean, that claim traces to an actual ansible-lint execution you can inspect, not a guess.
You'll put the workflow to work building netbox_to_nac, a role that renders a Cisco Network as Code data model for a VXLAN/EVPN fabric, sourced from NetBox and checked against the pipeline's schema before anything reaches the network. Along the way you'll resolve a lint findings, close a best-practice gaps by moving hardcoded fabric values into a NetBox query, confirm the rendered output is correct and idempotent, and publish the validated role as an AAP job template.
The job template you publish here becomes the first stage of the Network as Code pipeline that later paths in the AI-Powered Infrastructure Automation with MCP series launch under a governed approval gate and remediate automatically inside an event-driven loop. This lab is where that trusted artifact gets built, and where you learn the pattern behind it: AI accelerates the draft, and lint and test gates decide what earns the right to run in production.