Video Quality MCP Server

An MCP (Model Context Protocol) Server for video quality analysis and transcoding effect comparison.

Features

• 📹 Video Metadata Analysis - Extract encoding parameters, resolution, frame rate, etc.

• 🎬 GOP/Frame Structure Analysis - Analyze keyframe distribution and GOP structure

• 📊 Quality Metrics Comparison - Calculate objective metrics like PSNR, SSIM, VMAF

• 🔍 Artifact Analysis - Detect blur, blocking, ringing, banding, dark detail loss

• 📝 Transcode Summary - Generate LLM-friendly transcoding quality assessment reports

Installation

Running

Running as MCP Server

The server communicates with clients via stdio protocol.

Configuration in Cursor

Add the following to your Cursor MCP configuration file:

Tools

1. analyze_video_metadata

Parse video file metadata and encoding parameters.

Input:

• path (string): Path to video file

Output:

• Container format, duration, file size, bitrate

• Video codec, profile, level, resolution, frame rate, pixel format

2. analyze_gop_structure

Analyze video GOP structure and frame type distribution.

Input:

• path (string): Path to video file

Output:

• I/P/B frame distribution statistics

• GOP average/min/max length

• Keyframe timestamp list

3. compare_quality_metrics

Compare quality metrics between two video files.

Input:

• reference (string): Path to reference video

• distorted (string): Path to video to evaluate

Output:

• PSNR (Y/U/V components)

• SSIM score

• VMAF score

4. analyze_artifacts

Analyze video artifacts and perceptual quality proxy metrics.

Input:

• target (string): Path to target video

• reference (string, optional): Path to reference video (optional)

Output:

• Single stream mode: Artifact type scores

• Comparison mode: Artifact change delta values

• Risk summary and likely causes

5. summarize_transcode_comparison

Generate comprehensive transcoding effect assessment report.

Input:

• source (string): Path to source video

• transcoded (string): Path to transcoded video

Output:

• Quality change verdict

• VMAF delta and bitrate savings

• Key issues list

• Encoding parameter optimization recommendations

Technical Implementation

• FFmpeg/ffprobe Wrapper - Unified command-line interface

• No Deep Learning Dependencies - Uses traditional image processing and signal analysis methods

• Structured Output - All tools return standard JSON format

• Error Handling - Clear error message return mechanism

Requirements

• Python 3.10+

• FFmpeg (must be installed and configured in PATH)

• Python packages listed in requirements.txt

Notes

• Ensure FFmpeg is properly installed with VMAF support

• Large file analysis may take a long time

• All paths should preferably use absolute paths

Documentation

For Chinese documentation, see README.zh.md.