<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/0c124aeaa86643a9babf600c6bc45c38&quot; frameborder=&quot;0&quot; width=&quot;2056&quot; height=&quot;1542&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1542</height><width>2056</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1542</thumbnail_height><thumbnail_width>2056</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/0c124aeaa86643a9babf600c6bc45c38-3be9d67417719384.gif</thumbnail_url><duration>374.275</duration><title>Enhancing AI Context: Teamwork Graph MCP Tools in Atlassian Rovo Server</title><description>In this demo, I introduced two new tools we&apos;ve added to the Atlassian Rovo MCP server: GetTeamworkGraphContext and GetTeamworkGraphObject. These tools are designed to enhance our AI agent&apos;s ability to provide context around work items, particularly in scenarios where the answers have been vague. By using GetTeamworkGraphContext, we can identify all connected objects to a specific Jira work item, while GetTeamworkGraphObject allows us to retrieve detailed data for those objects. The goal is to first discover related items and then return the essential details. I encourage you to explore these tools to improve your interactions with our AI.</description></oembed>