<?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/8f0515f24506419884490ad3da7b081f&quot; frameborder=&quot;0&quot; width=&quot;1730&quot; height=&quot;1297&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1297</height><width>1730</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1297</thumbnail_height><thumbnail_width>1730</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/8f0515f24506419884490ad3da7b081f-e1a02066ef7e8c04.gif</thumbnail_url><duration>196.867</duration><title>Introducing PostBeam MCP for Smarter Workflows</title><description>This Loom introduces the PostBeam MCP and explains how it enables AI tools to interact with PostBeam on the user’s behalf. The speaker recommends connecting PostBeam to either Cloud or ChatGPT and describes using the setup guide to connect the accounts. Examples include pulling LinkedIn analytics and telling the AI to use the word PostBeam in Cloud to draft posts, then editing and saving ideas in PostBeam from ChatGPT. The Loom also shows how MCP can run scheduled workflows across tools like Slack, Fellow, and Gmail to find topics from the past week and automatically save draft posts in PostBeam.</description></oembed>