Nano Banana

Nano Banana Pro - AI Image Generation

Generate stunning 4K images, edit photos, and create graphics with perfect text rendering using Google's latest Gemini 3 Pro Image model via MCP.

When to Use

Invoke when user:

• Asks to "generate an image" or "create a picture"
• Wants to "edit this photo" or "modify this image"
• Needs graphics with text (logos, infographics, diagrams)
• Requests "consistent characters" across multiple images
• Says "visualize this" or "make me a [visual thing]"

Prerequisites

1. Gemini API Key

Get a free API key from Google AI Studio:

1. Sign in with Google account
2. Click "Get API Key" → "Create API Key"
3. Copy and save securely

2. MCP Server Setup

Recommended: NanoBanana-MCP (uses Gemini 3 Pro for highest quality)

# Quick install via Claude Code CLI
claude mcp add nano-banana --env GEMINI_API_KEY=your-key-here -- npx -y nanobanana-mcp

Or add to ~/.claude/settings.json manually:

{
  "mcpServers": {
    "nano-banana": {
      "command": "npx",
      "args": ["-y", "nanobanana-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Alternative: Nano-Banana-MCP by ConechoAI (Gemini 2.5 Flash - faster, lower cost)

{
  "mcpServers": {
    "nano-banana": {
      "command": "npx",
      "args": ["nano-banana-mcp"],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

Once MCP is configured, these tools become available:

Core Tools

Model Options

Image Size (Gemini 3 only)

Advanced Features

Prompting Best Practices

Structure Your Prompts

[Subject] + [Style] + [Details] + [Technical Specs]

Example:

"A cozy coffee shop interior, watercolor illustration style, warm lighting, wooden furniture, steaming cup on table, 4K resolution, soft morning light through windows"

For Best Results

1. Be Specific - Include colors, materials, lighting, mood
2. Specify Style - "photorealistic", "oil painting", "3D render", "anime"
3. Add Context - Time of day, weather, setting
4. Request Resolution - "4K", "high resolution", "detailed"

Precision Mode (JSON Prompting)

For high-stakes work requiring exact reproducibility, use structured JSON schemas.

When to Activate

Trigger phrases:

• "I need exact control over..."
• "Create a product shot for [brand]..."
• "Generate a UI mockup..."
• "Make an infographic showing..."
• "I want to iterate on just the lighting..."
• "A/B test different versions..."

Three Schema Types

The Translator Workflow

1. Describe - User explains what they want in plain English
2. Clarify - Claude asks targeted questions for missing fields
3. Generate - Claude outputs structured JSON schema
4. Review - User checks key fields match intent
5. Render - JSON converts to precise prompt for Nano Banana Pro
6. Iterate - Modify specific fields, re-render (scoped changes)

Example: Product Shot

User: "I need a hero shot for Aurora Lime seltzer"

Claude asks: "For the Aurora Lime hero shot:

1. Can size? (12oz standard?)
2. Props? (lime slices, ice, condensation?)
3. Background style? (solid color, gradient, bokeh?)
4. Lighting mood? (bright/refreshing or moody/premium?)"

Result: Structured JSON with exact specifications that can be iterated field-by-field.

Scoped Edits (The Key Unlock)

JSON enables changing ONE thing without regenerating everything:

Reference Docs

• references/json-prompting.md - Full JSON prompting guide
• references/translator-prompt.md - Translator system prompt
• references/schemas/ - Template schemas for each type
• references/examples-json.md - Filled-out examples

Text in Images

Nano Banana Pro excels at text rendering:

"A vintage movie poster for 'COSMIC ADVENTURE' with bold retro typography, starfield background, astronaut silhouette, 1970s sci-fi aesthetic"

Character Consistency

For consistent characters across images:

1. Generate initial character with detailed description
2. Use history:0 reference in subsequent prompts
3. Describe scene changes while referencing original

First: "A young woman with red curly hair, freckles, green eyes, wearing a blue jacket"
Then: "The same woman from history:0, now sitting at a café, reading a book"

Workflow Examples

Basic Image Generation

User: "Create an image of a futuristic city at sunset"

Claude uses: gemini_generate_image
Prompt: "Futuristic cityscape at golden hour sunset, towering glass skyscrapers with holographic advertisements, flying vehicles, warm orange and purple sky, photorealistic, 4K resolution, cinematic lighting"

Photo Editing

User: "Edit this photo to make it look like winter"

Claude uses: gemini_edit_image
Input: [user's image path]
Instructions: "Transform to winter scene: add snow on ground and surfaces, frost on windows, visible breath, overcast sky, cool blue color grading"

Iterative Refinement

User: "Make the lighting warmer"

Claude uses: continue_editing
Instructions: "Adjust lighting to warmer tones, add golden hour glow, enhance orange/yellow highlights, softer shadows"

Output Management

Images save to: ~/Documents/nanobanana_generated/

Naming format: generated-[timestamp]-[id].png

Security Notes

• API keys stored locally in environment variables
• Never committed to version control
• Images processed locally, not stored on external servers
• Use .env files for key management in projects

Model Comparison

Troubleshooting

Integration

Works well with:

• Artifacts Builder - Generate images for HTML artifacts
• Process Mapper - Create diagram visuals
• Research to Essay - Add illustrations to content

References

• references/prompting-guide.md - Detailed prompting techniques
• references/examples.md - Sample prompts by category