| 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"])
