Jellyfin Media Tier Scoring

Overview This script scans a Jellyfin movie library and assigns each movie folder to a quality tier from A to F based on metadata completeness and stream integrity.

It is designed for:

Large movie libraries Cross-platform use (Windows/Linux) Jellyfin-only environments (no filesystem or ffprobe dependency)

The output is a tiered audit showing library health and the most common issues per tier.

Features

Connects directly to Jellyfin API Evaluates each movie using consistent rules Assigns tiers A (best) to F (worst) Identifies top metadata issues per tier Provides library-wide overview Provides per-tier drilldown view

Requirements

Python 3.8+ requests library

Install dependency: pip install requests

Configuration

Edit these values in the script:

JELLYFIN_SERVER Example: http://localhost:8096

JELLYFIN_API_KEY Your Jellyfin API token

JELLYFIN_USER_ID Optional depending on server setup (can be empty if using global Items endpoint)

Usage

Full library scan: python script.py

Tier drilldown: python script.py --tier B

Valid tiers: A, B, C, D, E, F

Output format

Overview mode shows:

Tier A count: X top issues: none

Tier B count: X top issues:

issue_name (count, percentage)

Tiers with zero items are not displayed.

Scoring system

Each movie is evaluated across 5 categories:

Video integrity Audio stream language and default track Subtitle availability and language tags NFO metadata presence Artwork presence

Each category contributes to a total score which determines tier assignment.

Tier mapping: A = highest completeness F = lowest or corrupt/missing data

Design notes

No caching No persistent database No external media server dependencies beyond Jellyfin Deterministic evaluation logic Optimized for single-pass scanning

Limitations

NFO detection depends on Jellyfin metadata availability Language detection depends on embedded stream tags Artwork detection depends on Jellyfin image metadata No repair functionality included (read-only tool)