banana-image-mcp 0.1.1

A production-ready MCP server for AI-powered image generation using Gemini 3 Pro Image and Gemini 2.5 Flash Image with intelligent model selection

Banana Image 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.

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: 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

Using uvx (Recommended):

uvx banana-image-mcp

Using pip:

pip install banana-image-mcp

Configuration

Claude Desktop

Add to your claude_desktop_config.json:

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

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": "banana-image",
     "command": "uvx",
     "args": ["banana-image-mcp"],
     "env": {
       "GEMINI_API_KEY": "your-gemini-api-key-here"
     }
   }
   

Cursor

Add to Cursor's MCP configuration:

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

Model Selection

Banana Image MCP supports two Gemini models with intelligent automatic selection:

Pro Model (Gemini 3 Pro Image)

• Quality: Professional-grade, production-ready
• Resolution: Up to 4K (3840px)
• Speed: ~5-8 seconds per image
• Special Features:
  • Google Search Grounding for accurate, contextual images
  • Advanced Reasoning with configurable thinking levels
  • Superior Text Rendering
• Best for: Production assets, marketing materials, professional photography

Flash Model (Gemini 2.5 Flash Image)

• Speed: Very fast (2-3 seconds)
• Resolution: Up to 1024px
• Quality: High quality for everyday use
• Best for: Rapid prototyping, iterations, drafts, sketches

Automatic Selection (Recommended)

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

Pro Model Selected When:

• Quality keywords: "4K", "professional", "production", "high-res"
• High resolution requested: resolution="4k"
• Google Search grounding enabled
• High thinking level requested

Flash Model Selected When:

• Speed keywords: "quick", "draft", "sketch", "rapid"
• High-volume batch generation
• Standard resolution requested

Usage Examples

# Automatic selection (recommended)
"Generate a professional 4K product photo"  # Pro model
"Quick sketch of a cat"                     # Flash model

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

# Pro model with all features
generate_image(
    prompt="Professional product photo",
    model_tier="pro",
    resolution="4k",
    thinking_level="HIGH",
    enable_grounding=True
)

# Aspect ratio control
generate_image(
    prompt="Cinematic landscape",
    aspect_ratio="21:9"  # Ultra-wide format
)

Aspect Ratio Control

Supported aspect ratios:

• 1:1 - Square (Instagram, profile pictures)
• 4:3 - Classic photo format
• 3:4 - Portrait orientation
• 16:9 - Widescreen (YouTube thumbnails)
• 9:16 - Mobile portrait (phone wallpapers)
• 21:9 - Ultra-wide cinematic
• 2:3, 3:2, 4:5, 5:4 - Various photo formats

Environment Variables

# Required
GEMINI_API_KEY=your-gemini-api-key-here

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

# Optional
IMAGE_OUTPUT_DIR=/path/to/image/directory  # Default: ~/banana-images
LOG_LEVEL=INFO                             # DEBUG, INFO, WARNING, ERROR

Development

# Clone repository
git clone https://github.com/zengwenliang416/banana-image-mcp.git
cd banana-image-mcp

# Install with uv
uv sync

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

# Run locally
uv run python -m banana_image_mcp.server

License

MIT License - see LICENSE for details.

Support

• Issues: GitHub Issues
• Author: Wenliang Zeng