Testing Inline Image Preview: Animal Transformations and Spatial Awareness

My answer: Directly at the camera (frontal)
User feedback: "NO IT'S NOT GAH"
Correction: Actually facing LEFT

Lesson learned: I was making hasty assumptions rather than carefully analyzing the actual orientation. Even in compressed previews, spatial details should be clear if examined properly.

Round 4: Giraffe Edit Test

Edited the hippo to a giraffe without specifying direction in the prompt.

My initial analysis: Body facing forward, head turned left
After seeing full resolution: Entirely facing LEFT - body, neck, and head all oriented leftward

Key realization: The inline preview (resized to ~512px, under 98KB) was sufficient to determine direction - I just wasn't being careful enough in my analysis.

Round 5: Hot Air Balloons - Counting Exercise

Generated image with prompt: "three hot air balloons floating over a mountain landscape at sunset"

Careful count:

1. Left: Large balloon with colorful geometric pattern (red, yellow, blue squares/diamonds)
2. Center: Smaller rainbow-striped balloon
3. Right: Dark checkered/crosshatch pattern balloon

Result: ✅ Correctly counted 3 balloons, accurately described each one's pattern and position

Key Takeaways

1. Inline Preview Quality

• Compressed to ~512px long edge
• Optimized to stay under 98KB
• Still sufficient for spatial reasoning and counting

2. The Real Issue

The compression wasn't the problem - my hasty visual analysis was. Even compressed previews contain enough detail for:

• Directional orientation
• Object counting
• Pattern recognition
• Spatial relationships

3. Importance of Methodical Analysis

When describing images, I need to:

• Take time to carefully examine all elements
• Verify spatial relationships systematically
• Avoid quick assumptions
• Cross-check observations

4. The "both" Parameter Works Perfectly

The response: "both" parameter successfully provides:

• Full resolution resource links for the user
• Compressed inline previews for AI visual analysis
• Optimal balance of quality and context window usage

Technical Details

Tools used:

• mcp__image-mcp-local__create - Text-to-image generation
• mcp__image-mcp-local__edit - Image-to-image editing
• response: "both" - Enables inline preview

Models:

• Fal Nano Banana (fal-ai/nano-banana) for generation
• Fal Nano Banana Edit for transformations

Compression specs:

• Long edge: ~256-512px depending on aspect ratio
• File size: Under 98KB
• Format: JPEG

Conclusion

Inline image preview in image-mcp-local works excellently. The compressed previews provide sufficient visual information for detailed analysis, spatial reasoning, and object counting. The key is careful, methodical examination rather than rushing to conclusions.

The journey from puppy → hippo → giraffe → hot air balloons demonstrated both the capabilities of the system and the importance of careful visual analysis in AI image comprehension.