read-media

name	read-media
description	Analyze media files (images, video, audio) using AI vision and audio models with a critical-first lens. Use for output-first verification — render your deliverable, then read_media to see what it actually looks like. Distinguishes fundamental issues from surface-level fixes.

Read Media — Critical-First Analysis

Analyze media files using read_media from the media-tools MCP server. This is the primary tool for output-first verification — experiencing your work as a user would, not just reading code.

Critical-First Philosophy

The vision model is instructed to be a critical reviewer by default:

"You are reviewing this work critically. Be honest about what you see — name specific problems, explain their impact, and distinguish between issues that need a fundamental rethink vs issues that are easy fixes. Don't sugarcoat."

When no prompt is provided, the default is:

"Analyze this {media_type}. What works, what's broken, and what would a demanding user complain about? Be specific and critical."

Responses include a foundation_sound assessment that flags when the approach itself needs rethinking vs. when incremental fixes are sufficient. When foundation_sound: false, treat this as a signal to consider TRANSFORMATIVE changes rather than more polish.

You don't need to add "be critical" to your prompts — it's the default. Focus your prompt on what to look at.

Quick Start

# Analyze a screenshot
read_media(prompt="What flaws or layout issues do you see?", file_paths=["screenshot.png"])

# Compare before and after
read_media(prompt="Compare these two versions — what improved and what regressed?",
           file_paths=["v1.png", "v2.png"])

# Check a video
read_media(prompt="Is the animation smooth? Any glitches?", file_paths=["recording.mp4"])

# Verify audio output
read_media(prompt="Is the speech clear and natural?", file_paths=["output.mp3"])