Summary
A worked query surface over a dataset you can load in one click.
It handles the thing that breaks most model-driven database calls: list arguments arriving as strings. It targets sample_mflix specifically, so treat it as a way to query that dataset rather than as a general MongoDB client.
What it is
A FastMCP server over the sample_mflix movie dataset in MongoDB. Searches take title, genre, actors, directors, writers, year and rating thresholds, and let you name which fields come back and how results are sorted.
What you get
- Movies found by title, genre, actor, director, writer, year or rating threshold
- The returned fields chosen with projection_fields, and ordering set with sort_by and sort_order_asc
- Movies matching criteria counted rather than listed
- Average IMDb, Metacritic or Rotten Tomatoes ratings computed over a filtered set
- Stringified list arguments handled, which is the form models tend to emit
Requirements
Python 3.7 or higher, a MongoDB instance, and the sample_mflix database with its movies collection loaded — Atlas ships it under Load Sample Dataset. The client launches the script with the MongoDB connection string as an argument.