Nano Banana MCP Server 🍌

A production-ready Model Context Protocol (MCP) server that provides AI-powered image generation capabilities through Google's Gemini models with intelligent model selection.

⭐ NEW: Gemini 3 Pro Image Support! 🚀

Now featuring Nano Banana Pro - Google's latest and most powerful image generation model:

• 🏆 Professional 4K Quality: Generate stunning images up to 3840px resolution

• 🌐 Google Search Grounding: Access real-world knowledge for factually accurate images

• 🧠 Advanced Reasoning: Configurable thinking levels for complex compositions

• 🎯 Superior Text Rendering: Crystal-clear text in images at high resolution

• 🎨 Enhanced Understanding: Better context comprehension for complex prompts

✨ Features

• 🎨 Multi-Model AI Image Generation: Intelligent selection between Flash (speed) and Pro (quality) models

• ⚡ Gemini 2.5 Flash Image: Fast generation (1024px) for rapid prototyping

• 🏆 Gemini 3 Pro Image: High-quality up to 4K with Google Search grounding

• 🤖 Smart Model Selection: Automatically chooses optimal model based on your prompt

• 📐 Aspect Ratio Control ⭐ NEW: Specify output dimensions (1:1, 16:9, 9:16, 21:9, and more)

• 📋 Smart Templates: Pre-built prompt templates for photography, design, and editing

• 📁 File Management: Upload and manage files via Gemini Files API

• 🔍 Resource Discovery: Browse templates and file metadata through MCP resources

• 🛡️ Production Ready: Comprehensive error handling, logging, and validation

• ⚡ High Performance: Optimized architecture with intelligent caching

🚀 Quick Start

Prerequisites

1. Google Gemini API Key - Get one free here

2. Python 3.11+ (for development only)

Installation

Option 1: From MCP Registry (Recommended) This server is available in the Model Context Protocol Registry. Search for "nanobanana" or use the MCP name below with your MCP client.

mcp-name: io.github.zhongweili/nanobanana-mcp-server

Option 2: Using uvx

uvx nanobanana-pro-mcp-server

Option 3: Using pip

pip install nanobanana-pro-mcp-server

🔧 Configuration

Authentication Methods

Nano Banana supports two authentication methods via NANOBANANA_AUTH_METHOD:

1. API Key (api_key): Uses GEMINI_API_KEY. Best for local development and simple deployments.

2. Vertex AI ADC (vertex_ai): Uses Google Cloud Application Default Credentials. Best for production on Google Cloud (Cloud Run, GKE, GCE).

3. Automatic (auto): Defaults to API Key if present, otherwise tries Vertex AI.

1. API Key Authentication (Default)

Set GEMINI_API_KEY environment variable.

2. Vertex AI Authentication (Google Cloud)

Required environment variables:

• NANOBANANA_AUTH_METHOD=vertex_ai (or auto)

• GCP_PROJECT_ID=your-project-id

• GCP_REGION=us-central1 (default)

Prerequisites:

• Enable Vertex AI API: gcloud services enable aiplatform.googleapis.com

• Grant IAM Role: roles/aiplatform.user to the service account.

Claude Desktop

Option 1: Using Published Server (Recommended)

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "nanobanana": {
      "command": "uvx",
      "args": ["nanobanana-pro-mcp-server"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Option 2: Using Local Source (Development)

If you are running from source code, point to your local installation:

{
  "mcpServers": {
    "nanobanana-local": {
      "command": "uv",
      "args": ["run", "python", "-m", "nanobanana_mcp_server.server"],
      "cwd": "/absolute/path/to/nanobanana-mcp-server",
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Option 3: Using Vertex AI (ADC)

To authenticate with Google Cloud Application Default Credentials (instead of an API Key):

{
  "mcpServers": {
    "nanobanana-adc": {
      "command": "uvx",
      "args": ["nanobanana-mcp-server@latest"],
      "env": {
        "NANOBANANA_AUTH_METHOD": "vertex_ai",
        "GCP_PROJECT_ID": "your-project-id",
        "GCP_REGION": "us-central1"
      }
    }
  }
}

Configuration file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

Claude Code (VS Code Extension)

Install and configure in VS Code:

1. Install the Claude Code extension

2. Open Command Palette (Cmd/Ctrl + Shift + P)

3. Run "Claude Code: Add MCP Server"

4. Configure:

   {
     "name": "nanobanana",
     "command": "uvx",
     "args": ["nanobanana-mcp-server@latest"],
     "env": {
       "GEMINI_API_KEY": "your-gemini-api-key-here"
     }
   }

Cursor

Add to Cursor's MCP configuration:

{
  "mcpServers": {
    "nanobanana": {
      "command": "uvx",
      "args": ["nanobanana-pro-mcp-server"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Continue.dev (VS Code/JetBrains)

Add to your config.json:

{
  "mcpServers": [
    {
      "name": "nanobanana",
      "command": "uvx",
      "args": ["nanobanana-mcp-server@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  ]
}

Open WebUI

Configure in Open WebUI settings:

{
  "mcp_servers": {
    "nanobanana": {
      "command": ["uvx", "nanobanana-mcp-server@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key-here"
      }
    }
  }
}

Gemini CLI / Generic MCP Client

# Set environment variable
export GEMINI_API_KEY="your-gemini-api-key-here"

# Run server in stdio mode
uvx nanobanana-pro-mcp-server

# Or with pip installation
python -m nanobanana_mcp_server.server

🤖 Model Selection

Nano Banana supports two Gemini models with intelligent automatic selection:

🏆 Pro Model - Nano Banana Pro (Gemini 3 Pro Image) ⭐ NEW!

Google's latest and most advanced image generation model

• Quality: Professional-grade, production-ready

• Resolution: Up to 4K (3840px) - highest available

• Speed: ~5-8 seconds per image

• Special Features:

  • 🌐 Google Search Grounding: Leverages real-world knowledge for accurate, contextual images

  • 🧠 Advanced Reasoning: Configurable thinking levels (LOW/HIGH) for complex compositions

  • 📐 Media Resolution Control: Fine-tune vision processing detail (LOW/MEDIUM/HIGH/AUTO)

  • 📝 Superior Text Rendering: Exceptional clarity for text-in-image generation

  • 🎨 Enhanced Context Understanding: Better interpretation of complex, narrative prompts

• Best for: Production assets, marketing materials, professional photography, high-fidelity outputs, images requiring text, factual accuracy

• Cost: Higher per image (premium quality)

⚡ Flash Model (Gemini 2.5 Flash Image)

Fast, reliable model for rapid iteration

• Speed: Very fast (2-3 seconds)

• Resolution: Up to 1024px

• Quality: High quality for everyday use

• Best for: Rapid prototyping, iterations, high-volume generation, drafts, sketches

• Cost: Lower per image

🤖 Automatic Selection (Recommended)

By default, the server uses AUTO mode which intelligently analyzes your prompt and requirements:

Pro Model Selected When:

• Quality keywords detected: "4K", "professional", "production", "high-res", "HD"

• High resolution requested: resolution="4k" or resolution="high"

• Google Search grounding enabled: enable_grounding=True

• High thinking level requested: thinking_level="HIGH"

• Multi-image conditioning with multiple input images

Flash Model Selected When:

• Speed keywords detected: "quick", "draft", "sketch", "rapid"

• High-volume batch generation: n > 2

• Standard or lower resolution requested

• No special Pro features required

Usage Examples

# Automatic selection (recommended)
"Generate a professional 4K product photo"  # → Pro model (quality keywords + 4K)
"Quick sketch of a cat"                     # → Flash model (speed keyword)
"Create a diagram with clear text labels"   # → Pro model (text rendering)
"Draft mockup for website hero section"     # → Flash model (draft keyword)

# Explicit model selection
generate_image(
    prompt="A scenic landscape",
    model_tier="flash"  # Force Flash model for speed
)

# Leverage Nano Banana Pro features
generate_image(
    prompt="Professional product photo of vintage camera on wooden desk",
    model_tier="pro",              # Use Pro model
    resolution="4k",               # 4K resolution (Pro-only)
    thinking_level="HIGH",         # Enhanced reasoning
    enable_grounding=True,         # Use Google Search for accuracy
    media_resolution="HIGH"        # High-detail vision processing
)

# Pro model for high-quality text rendering
generate_image(
    prompt="Infographic showing 2024 market statistics with clear labels",
    model_tier="pro",              # Pro excels at text rendering
    resolution="4k"                # Maximum clarity for text
)

# Control aspect ratio for different formats ⭐ NEW!
generate_image(
    prompt="Cinematic landscape at sunset",
    aspect_ratio="21:9"            # Ultra-wide cinematic format
)

generate_image(
    prompt="Instagram post about coffee",
    aspect_ratio="1:1"             # Square format for social media
)

generate_image(
    prompt="YouTube thumbnail design",
    aspect_ratio="16:9"            # Standard video format
)

generate_image(
    prompt="Mobile wallpaper of mountain vista",
    aspect_ratio="9:16"            # Portrait format for phones
)

📐 Aspect Ratio Control

Control the output image dimensions with the aspect_ratio parameter:

Supported Aspect Ratios:

• 1:1 - Square (Instagram, profile pictures)

• 4:3 - Classic photo format

• 3:4 - Portrait orientation

• 16:9 - Widescreen (YouTube thumbnails, presentations)

• 9:16 - Mobile portrait (phone wallpapers, stories)

• 21:9 - Ultra-wide cinematic

• 2:3, 3:2, 4:5, 5:4 - Various photo formats

# Examples for different use cases
generate_image(
    prompt="Product showcase for e-commerce",
    aspect_ratio="3:4",    # Portrait format, good for product pages
    model_tier="pro"
)

generate_image(
    prompt="Social media banner for Facebook",
    aspect_ratio="16:9"    # Landscape banner format
)

Note: Aspect ratio works with both Flash and Pro models. For best results with specific aspect ratios at high resolution, use the Pro model with resolution="4k".

📁 Output Path Control ⭐ NEW!

Control where generated images are saved with the output_path parameter:

Three modes of operation:

1. Specific file path - Save to an exact file location:

generate_image(
    prompt="A beautiful sunset",
    output_path="/path/to/sunset.png"  # Exact file location
)

2. Directory path - Use auto-generated filename in a specific directory:

generate_image(
    prompt="Product photo",
    output_path="/path/to/products/"  # Trailing slash indicates directory
)

3. Default location - Uses IMAGE_OUTPUT_DIR or ~/nanobanana-images:

generate_image(
    prompt="Random image"
    # output_path defaults to None
)

Multiple images (n > 1): When generating multiple images with a file path, images are automatically numbered:

• First image: /path/to/image.png

• Second image: /path/to/image_2.png

• Third image: /path/to/image_3.png

Precedence Rules:

1. output_path parameter (if provided) - highest priority

2. IMAGE_OUTPUT_DIR environment variable

3. ~/nanobanana-images (default fallback)

# Save to specific location with Pro model
generate_image(
    prompt="Professional headshot",
    model_tier="pro",
    output_path="/Users/me/photos/headshot.png"
)

# Save multiple images to a directory
generate_image(
    prompt="Product variations",
    n=4,
    output_path="/path/to/products/"  # Each gets unique filename
)

⚙️ Environment Variables

Configuration options:

# Authentication (Required)
# Method 1: API Key
GEMINI_API_KEY=your-gemini-api-key-here

# Method 2: Vertex AI (Google Cloud)
NANOBANANA_AUTH_METHOD=vertex_ai
GCP_PROJECT_ID=your-project-id
GCP_REGION=us-central1

# Model Selection (optional)
NANOBANANA_MODEL=auto  # Options: flash, pro, auto (default: auto)

# Optional
IMAGE_OUTPUT_DIR=/path/to/image/directory  # Default: ~/nanobanana-images
LOG_LEVEL=INFO                             # DEBUG, INFO, WARNING, ERROR
LOG_FORMAT=standard                        # standard, json, detailed

🐛 Troubleshooting

Common Issues

"GEMINI_API_KEY not set"

• Add your API key to the MCP server configuration in your client

• Get a free API key at Google AI Studio

"Server failed to start"

• Ensure you're using the latest version: uvx nanobanana-mcp-server@latest

• Check that your client supports MCP (Claude Desktop 0.10.0+)

"Permission denied" errors

• The server creates images in ~/nanobanana-images by default

• Ensure write permissions to your home directory

Development Setup

For local development:

# Clone repository
git clone https://github.com/qso/nanobanana-mcp-server.git
cd nanobanana-mcp-server

# Install with uv
uv sync

# Set environment
export GEMINI_API_KEY=your-api-key-here

# Run locally
uv run python -m nanobanana_mcp_server.server

📄 License

MIT License - see LICENSE for details.

🆘 Support

• Issues: GitHub Issues

• Discussions: GitHub Discussions