Nano Banana 2 - AI Image Generation

Generate stunning 4K images, edit photos, and create graphics with perfect text rendering using Google's latest Gemini 3.1 Flash 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 3.1 Flash Image 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 2.0 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 3.1 / 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 2
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 2 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