Offers programmatic access to a MongoDB movie database, enabling efficient querying and analysis.
This Python-based FastMCP server provides a robust set of tools for interacting with the `sample_mflix` movie dataset on MongoDB Atlas. It's designed to offer structured, programmatic access to movie information, making it ideal for integration with large language models, AI agents, or other systems requiring comprehensive movie search, counting, and rating calculations directly from a database.
Key Features
01Flexible data retrieval with options to specify fields and control sorting.
02Quickly count movies matching various criteria.
03LLM-friendly API designed for easy consumption by large language models.
04Calculate average IMDb, Metacritic, or Rotten Tomatoes ratings for filtered movie sets.
05Comprehensive movie search by title, genre, actors, directors, year, and ratings.
060 GitHub stars
Use Cases
01Providing programmatic database access for custom movie analysis applications.
02Enabling LLMs to query and analyze movie data from MongoDB.
03Powering AI agents with structured access to film information.
