Many media server enthusiasts like me spend more time managing our libraries than actually consuming the content within them. I recently looked into whether artificial intelligence could streamline this process, specifically within the Plex ecosystem.
My goal was to see if AI could handle the organizational tasks that usually require manual input, such as curating specific story arcs or generating recommendations based on viewing history.
The bridge between the AI and the media server is a Model Context Protocol (MCP) server. MCPs in this context connect an AI desktop client—such as OpenAI’s Codex, Claude Desktop, or Gemini—to a local Plex instance. Setting this up requires a Plex token, which is accessible through the XML info of any media item in the library. Once the token is dropped into the configuration screen, the AI can interact with the server’s data through plain English commands. I demo this in the video.
One practical test involved creating a playlist for a specific narrative arc within Star Trek: Deep Space Nine. Rather than manually selecting episodes that contribute to the “Dominion War” storyline, I provided the AI with a text file containing an episode list sourced from a Reddit discussion. The system generated a sequential playlist in approximately ninety seconds. Beyond simple lists, the AI can also aggregate data from the web. I asked it to research the highest-rated episodes of Star Trek: The Next Generation across multiple sources and build a playlist based on those aggregate ratings. While this research-heavy task took several minutes, it resulted in a curated selection based on external critical consensus without any manual sorting or list creation on my part.
The utility extends to music as well. I requested a smart playlist for 1980s pop music. By defining the decade and genre in a simple prompt, the AI configured the metadata filters within Plex to include tracks from 1980 to 1989 categorized as pop or rock. Because it was set up as a smart playlist, it automatically updates whenever new tracks fitting those criteria are added to the library in the future. This replaces the need to navigate through various menu layers to set up complex filtering rules.
Analysis of viewing habits is another area where the AI provides utility. By accessing watch history, the system can identify content that has been overlooked. For instance, I asked it to find episodes of a specific children’s show, Bluey, that had the lowest view counts to find episodes my children might have missed. It can also cross-reference a watch list with a viewing history to suggest new series. When I requested science fiction recommendations, the AI suggested several shows I had not yet watched, such as Farscape and Babylon 5. It then added these items to my Plex watch list upon request and identified which streaming services currently host them.
For those interested in more advanced automation, the AI can be used to monitor upcoming releases of TV shows and movies. I currently use a routine that scans my watch list every morning and sends an email notification when new episodes of my tracked shows are scheduled to return to streaming services or broadcast.
By using an AI as an intermediary, the technical burden of database management is replaced by a conversational interface. It’s a definite time saver and fun to play with!