[SAMPLE] From Requirements to Jira: Using AI and MCP Rovo to Build Your First Backlog.

How we go from requirements gathering to a Jira-ready backlog: story format, acceptance criteria, and using MCP Rovo to generate the initial set of stories.

The challenge: requirements to stories

Product and project teams often have clear goals and rough requirements, but turning them into a backlog that engineering can execute is slow and inconsistent. Stories end up vague, acceptance criteria are missing, and the link between “what we want” and “what we’ll build” is weak. We wanted a repeatable path from discovery to a first backlog—without losing nuance or ownership.

Our approach. We combine structured requirements (who, what, why, done when) with AI-assisted story writing. We use MCP Rovo to generate an initial set of stories in a consistent format, with acceptance criteria, then refine and prioritize in Jira. The result is a backlog that reads like a single team wrote it and that product and engineering can stand behind.

Step 1: Requirements gathering

We capture requirements in a simple template: problem statement, users, success criteria, and out-of-scope. We avoid long prose; we use bullets and examples. That structure becomes the input for the next step and stays as the “source of truth” in Confluence or Notion.

Step 2: Story format and acceptance criteria

We define a standard story format: As a [role], I want [capability] so that [outcome]. Acceptance criteria are Given/When/Then or bullet lists that are testable. Non-functional requirements (performance, security) go into separate stories or sub-tasks so they are visible and estimable.

Step 3: Using MCP Rovo for the initial backlog

We feed the requirements doc and the story template into MCP Rovo. We ask it to generate a first pass of stories: one capability per story, with clear acceptance criteria and a suggested priority. We treat this as a draft, not a final backlog. The product owner and tech lead then review, merge, split, and reorder. We often add edge cases and technical constraints that the model didn’t see.

Step 4: Into Jira

We import the refined stories into Jira (or your tool of choice). Epics and labels come from our requirements taxonomy. We keep the acceptance criteria in the story description so that QA and engineering share one definition of done. From here, sprint planning and estimation follow your normal process.

What we learned

AI-generated backlogs are a starting point. The value is speed and consistency: everyone gets a common structure and language. The human work—prioritization, technical reality-check, and product judgment—remains essential. MCP Rovo and a clear requirements format don’t replace product thinking; they make the handoff from “what we want” to “what we’ll build” faster and clearer.