community-automations/mongodb-recommender

Research & Intel

PublicClaude subagent

MongoDB Recommender

Genre filters give people the same blockbusters everyone already saw. This recommender reads what a viewer actually describes and finds titles that fit the mood.

sonnet4 daysMongoDB AtlasAtlas SearchOpenAI EmbeddingsNode.js
ClaudeClaude
ROI for
README.md

Why this subagent

Genre filters give people the same blockbusters everyone already saw. This recommender reads what a viewer actually describes and finds titles that fit the mood.

Movie documents stay in MongoDB Atlas, with embeddings indexed next to them through Atlas Search. A request runs a hybrid search that blends vector similarity with rating and genre filters, then returns ranked picks, each with a one-line reason pulled from the synopsis.

How it runs

    • Read

      Used at step 01 to kick off the pipeline.

    • Write

      Used at step 01 to kick off the pipeline.

    • WebFetch

      Used at step 01 to kick off the pipeline.

    • WebSearch

      Used at step 01 to kick off the pipeline.

Sample output

json
// Sample output
// (generated when the pipeline finishes)

Given a user's described taste, return ranked movie recommendations from MongoDB Atlas with a one-line reason per title.

Unlock the rest

The full agent definition, install snippet, and starter task are gated for community members.

Members get the full `.md` agent file, the npm / pnpm install one-liners, a starter prompt that we've tuned against real runs, and the open-source repo when this automation ships there. One email, magic link, done.