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nanobanana-mcp-server

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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: Nano Banana 2 — Gemini 3.1 Flash Image! 🍌🚀

Nano Banana 2 (gemini-3.1-flash-image-preview) is now the default model — delivering Pro-level quality at Flash speed:

  • 🍌 Flash Speed + 4K Quality: Up to 3840px at Gemini 2.5 Flash latency

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

  • 🎯 Subject Consistency: Up to 5 characters and 14 objects per scene

  • ✍️ Precision Text Rendering: Crystal-clear text placement in images

  • 🏆 Gemini 3 Pro Image still available for maximum reasoning depth

Related MCP server: Gemini MCP Server for Claude Desktop

✨ Features

  • 🎨 Multi-Model AI Image Generation: Three Gemini models with intelligent automatic selection

  • 🍌 Gemini 3.1 Flash Image (NB2): Default model — 4K resolution at Flash speed with grounding

  • 🏆 Gemini 3 Pro Image: Maximum reasoning depth for the most complex compositions

  • ⚡ Gemini 2.5 Flash Image: Legacy Flash model for high-volume rapid prototyping

  • 🤖 Smart Model Selection: Automatically routes to NB2 or Pro 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 three Gemini models with intelligent automatic selection:

🍌 NB2 — Nano Banana 2 (Gemini 3.1 Flash Image) ⭐ DEFAULT

Flash speed with Pro-level quality — the best of both worlds

  • Quality: Production-ready 4K output

  • Resolution: Up to 4K (3840px)

  • Speed: ~2-4 seconds per image (Flash-class latency)

  • Special Features:

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

    • 🎯 Subject Consistency: Up to 5 characters and 14 objects per scene

    • ✍️ Precision Text Rendering: Clear, well-placed text in images

  • Best for: Almost everything — production assets, marketing, photography, text overlays

  • model_tier: "nb2" (or "auto" — NB2 is the auto default)

🏆 Pro Model — Nano Banana Pro (Gemini 3 Pro Image)

Maximum reasoning depth for the most demanding compositions

  • Quality: Highest available

  • Resolution: Up to 4K (3840px)

  • Speed: ~5-8 seconds per image

  • Special Features:

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

    • 🌐 Google Search Grounding: Real-world knowledge integration

    • 📐 Media Resolution Control: Fine-tune vision processing detail

  • Best for: Complex narrative scenes, intricate compositions, maximum reasoning required

  • model_tier: "pro"

⚡ Flash Model (Gemini 2.5 Flash Image)

Legacy model for high-volume rapid iteration

  • Speed: Very fast (2-3 seconds)

  • Resolution: Up to 1024px

  • Best for: High-volume generation, quick drafts where 4K is not needed

  • model_tier: "flash"

🤖 Automatic Selection (Recommended)

By default, the server uses AUTO mode which routes to NB2 unless Pro's deeper reasoning is clearly needed:

Pro Model Selected When:

  • Strong quality keywords: "4K", "professional", "production", "high-res", "HD"

  • High thinking level requested: thinking_level="HIGH"

  • Multi-image conditioning with multiple input images

NB2 Model Selected When (default):

  • Standard requests, everyday image generation

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

  • High-volume batch generation (n > 2)

Usage Examples

# Automatic selection (recommended) — routes to NB2 by default "A cat sitting on a windowsill" # → NB2 (default) "Quick sketch of a cat" # → NB2 (speed keyword, NB2 is fast enough) "Professional 4K product photo" # → Pro (strong quality keywords) # Explicit NB2 selection generate_image( prompt="Product photo on white background", model_tier="nb2", # Nano Banana 2 (Flash speed + 4K) resolution="4k", enable_grounding=True ) # Leverage Nano Banana Pro for complex reasoning generate_image( prompt="Cinematic scene: three characters in a tense standoff at dusk", model_tier="pro", # Pro for deep reasoning resolution="4k", thinking_level="HIGH", # Enhanced reasoning enable_grounding=True ) # Legacy Flash for high-volume drafts generate_image( prompt="Simple icon", model_tier="flash" # Fast 1024px generation ) # 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, nb2, pro, auto (default: auto → nb2) # Optional IMAGE_OUTPUT_DIR=/path/to/image/directory # Default: ~/nanobanana-images GEMINI_BASE_URL=https://custom-api.example.com # Custom API endpoint (for proxies/gateways) 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 LICENSE for details.

🆘 Support

Install Server
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security – no known vulnerabilities
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