nanobanana-mcp-server

zhongweili/nanobanana-mcp-server

3.5

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Nano Banana MCP Server is a production-ready server that leverages Google's Gemini 2.5 Flash Image model to provide AI-powered image generation capabilities.

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
nanobanana-mcp-server MCP server

✨ 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-mcp-server@latest

Option 3: Using pip

pip install nanobanana-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-mcp-server@latest"],
      "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-mcp-server@latest"],
      "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-mcp-server@latest

# 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
)
  1. Directory path - Use auto-generated filename in a specific directory:
generate_image(
    prompt="Product photo",
    output_path="/path/to/products/"  # Trailing slash indicates directory
)
  1. 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/zhongweili/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 for details.

🆘 Support