Image Generation via MCP

AI image generation skill via MCP. Use Gemini models or compatible services to generate high-quality images for marketing, UI, and presentations.

When to use this skill

Marketing assets

Hero images, banners, social media content

UI/UX design

Placeholder images, icons, illustrations

Presentations

Slide backgrounds, product visualizations

Brand consistency

Generate images based on a style guide Instructions Step 1: Configure MCP Environment

Check MCP server configuration

claude mcp list

Check Gemini CLI availability

gemini-cli must be installed

Required setup

:

Model name (gemini-2.5-flash, gemini-3-pro, etc.)

API key reference (stored as an environment variable)

Output directory

Step 2: Define the Prompt

Write a structured prompt:

**

Subject

**

[main subject]

**

Style

**

[style - minimal, illustration, photoreal, 3D, etc.]

**

Lighting

**

[lighting - natural, studio, golden hour, etc.]

**

Mood

**

[mood - calm, dynamic, professional, etc.]

**

Composition

**

[composition - centered, rule of thirds, etc.]

**

Aspect Ratio

**

[ratio - 16:9, 1:1, 9:16]

**

Brand Colors

**

[brand color constraints] Step 3: Choose the Model Model Use case Notes gemini-3-pro-image High quality Complex compositions, detail gemini-2.5-flash-image Fast iteration Prototyping, testing gemini-2.5-pro-image Balanced Quality/speed balance Step 4: Generate and Review

Generate 2-4 variants

ask-gemini "Create a serene mountain landscape at sunset, wide 16:9, minimal style, soft gradients in brand blue #2563EB"

Iterate by changing a single variable

ask-gemini

"Same prompt but with warm orange tones"

Review checklist

:

Brand fit

Composition clarity

Ratio correctness

Text readability (if text is included)

Step 5: Deliverables

Final deliverables:

Final image files

Prompt metadata record

Model, ratio, usage notes

{

"prompt"

:

"serene mountain landscape at sunset..."

,

"model"

:

"gemini-3-pro-image"

,

"aspect_ratio"

:

"16:9"

,

"style"

:

"minimal"

,

"brand_colors"

:

[

"#2563EB"

]

,

"output_file"

:

"hero-image-v1.png"

,

"timestamp"

:

"2026-01-21T10:30:00Z"

}

Examples

Example 1: Hero Image

Prompt

:

Create a serene mountain landscape at sunset,

wide 16:9, minimal style, soft gradients in brand blue #2563EB.

Focus on clean lines and modern aesthetic.

Expected output

:

16:9 hero image

Prompt parameters saved

2-3 variants for selection

Example 2: Product Thumbnail

Prompt

:

Generate a 1:1 thumbnail of a futuristic dashboard UI

with clean interface, soft lighting, and professional feel.

Include subtle glow effects and dark theme.

Expected output

:

1:1 square image

Low visual noise

App store ready

Example 3: Social Media Banner

Prompt

:

Create a LinkedIn banner (1584x396) for a SaaS startup.

Modern gradient background with abstract geometric shapes.

Colors: #6366F1 to #8B5CF6.

Leave space for text overlay on the left side.

Expected output

:

LinkedIn-optimized dimensions

Safe zone for text

Brand-aligned colors

Best practices

Specify ratio early

Prevent unintended crops

Use style anchors

Maintain consistent aesthetics

Iterate with constraints

Change only one variable at a time

Track prompts

Ensure reproducibility

Batch similar requests

Create a consistent style set

Common pitfalls

Vague prompts

Specify concrete style and composition

Ignoring size constraints

Check target channel dimension requirements

Overly complex scenes

Simplify for clarity

Troubleshooting

Issue: Outputs are inconsistent

Cause

Missing stable style constraints

Solution

Add style references and a fixed palette

Issue: Wrong aspect ratio

Cause

Ratio not specified or an unsupported ratio

Solution

Provide an exact ratio and regenerate

Issue: Brand mismatch

Cause

Color codes not specified

Solution

Specify brand colors via HEX codes Output format

Image Generation Report

Request

**

Prompt

**

[full prompt]

**

Model

**

[model used]

**

Ratio

**

[aspect ratio]

Output Files 1. filename-v1.png - [description] 2. filename-v2.png - [variant description]

Metadata

Generated: [timestamp]

Iterations: [count]

Selected: [final choice]

Usage Notes

[Any notes for implementation]

Multi-Agent Workflow

Validation & Retrospectives

Round 1 (Orchestrator)

Prompt completeness, ratio correctness

Round 2 (Analyst)

Style consistency, brand alignment

Round 3 (Executor)

Validate output filenames, delivery checklist

Agent Roles

Agent

Role

Claude

Prompt structuring, quality verification

Gemini

Run image generation

Codex

File management, batch processing

Metadata

Version

Current Version

1.0.0

Last Updated

2026-01-21

Compatible Platforms

Claude, ChatGPT, Gemini, Codex