OpenRouter Nano Banana Pro Assets Generator

A powerful asset generation solution that leverages OpenRouter's Nano Banana Pro (Google Gemini 3 Pro Image Preview) for professional web asset generation and editing.

Available as both a Model Context Protocol (MCP) server and a GitHub Copilot Agent Skill (following the official GitHub specification).

Overview

This project enables AI assistants to generate high-quality web assets, asset packs, and perform advanced image editing while maintaining brand consistency. It uses Nano Banana Pro, Google's most advanced image-generation and editing model built on Gemini 3 Pro.

Two Ways to Use

1. GitHub Copilot Agent Skill - Official GitHub Copilot agent skill that loads automatically when you need asset generation

2. MCP Server - A full Model Context Protocol server for MCP-compatible clients

Key Features

• 🎨 Professional Asset Generation: Create icons, banners, backgrounds, UI elements, and more

• 📦 Asset Pack Creation: Generate cohesive sets of related assets with consistent branding

• ✏️ Advanced Image Editing: Fine-grained controls for lighting, focus, localized edits, and transformations

• 🎯 Brand Consistency: Maintain visual identity across multiple assets with identity preservation

• 🖼️ Multi-Modal Input: Support for reference images, color palettes, and logo files

• 📐 Flexible Formats: Multiple aspect ratios (1:1, 16:9, 9:16, 21:9, etc.) and resolutions (up to 4K)

• 🌐 Web-Optimized: Assets designed specifically for web development workflows

Related MCP server: openai-gpt-image-mcp

🔑 Required: OpenRouter API Key

This skill requires an OpenRouter API key to function. Get yours at openrouter.ai.

For GitHub Copilot coding agent: Add your API key as a repository secret named OPENROUTER_API_KEY.
→ Setup Instructions

For local development: Set the OPENROUTER_API_KEY environment variable.
→ Setup Instructions

Quick Start

Choose your preferred method:

Option 1: GitHub Copilot Agent Skill (Recommended)

The GitHub Copilot Agent Skill is automatically loaded by GitHub Copilot when you need asset generation.

For GitHub Copilot Coding Agent

1. Add your API key to the copilot environment:

   • Go to: Settings → Environments → copilot

   • Under "Environment secrets," click Add environment secret

   • Name: OPENROUTER_API_KEY

   • Secret: Your OpenRouter API key from openrouter.ai

   📖 GitHub Copilot Environment Setup →

2. Use with GitHub Copilot coding agent:

   Just ask naturally when working on a PR or in your repository:

   "Generate a modern home icon for my website"
   "Create a hero banner with blue and purple gradient"
   "I need a social media kit for my brand"

   Copilot automatically:

   • Loads your API key from the copilot environment

   • Uses the skill in .github/skills/nano-banana-assets/

   • Generates your assets!

For Local Development / VS Code

1. Set your API key locally:

   # Run the interactive setup script
   ./setup-api-key.sh
   
   # Or manually set it in your shell
   export OPENROUTER_API_KEY="your-api-key-here"

   📖 Local API Key Setup →

2. Test the skill:

   cd .github/skills/nano-banana-assets/scripts
   python generate_asset.py "Test icon" "1:1" "512x512"

📖 Full Agent Skill Documentation →
📖 Copilot Usage Guide →

Option 2: MCP Server

The MCP Server provides dedicated tools for MCP-compatible clients like Claude Desktop.

1. Set your API key:

   export OPENROUTER_API_KEY="your-api-key-here"

2. Install dependencies:

   npm install
   npm run build

3. Set your API key:

   export OPENROUTER_API_KEY="your-api-key-here"

4. Configure MCP client (e.g., Claude Desktop):

   MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   Windows: %APPDATA%/Claude/claude_desktop_config.json

   {
     "mcpServers": {
       "nano-banana-assets": {
         "command": "node",
         "args": ["/absolute/path/to/openrouter-nano-banana-mcp/dist/index.js"],
         "env": {
           "OPENROUTER_API_KEY": "your-api-key-here"
         }
       }
     }
   }

Get your API key from OpenRouter.

Comparison: Agent Skill vs MCP Server

Use Agent Skill when:

• You want maximum portability

• You're using multiple AI platforms

• You prefer minimal setup

Use MCP Server when:

• You're already using MCP clients

• You want structured tool calls

• You prefer the MCP ecosystem

Both provide the same capabilities and quality!

Available Capabilities

1. generate_asset

Generate a single web asset with full control over style, format, and branding.

Parameters:

• prompt (required): Detailed description of the asset

• aspectRatio: "1:1", "16:9", "9:16", "4:3", "21:9", etc.

• resolution: "1920x1080", "2K", "4K", etc.

• referenceImages: Array of image URLs for style guidance (max 5)

• colorPalette: Array of hex color codes

• logoFile: Logo URL or base64 data

• editInstructions: Optional editing instructions

Example:

Generate a hero banner with:
- Prompt: "Modern tech startup hero banner with gradient background"
- Aspect Ratio: 16:9
- Resolution: 1920x1080
- Color Palette: ["#667EEA", "#764BA2"]

2. generate_asset_pack

Create a complete set of related assets with consistent branding.

Parameters:

• description (required): Overall purpose of the asset pack

• assetTypes (required): Array of asset types to generate

• brandGuidelines: Brand style requirements

• referenceImages: Reference images for visual style

• colorPalette: Brand color palette

• logoFile: Brand logo

• aspectRatio: Default aspect ratio

• resolution: Default resolution

Example:

Generate an asset pack with:
- Description: "Social media kit for eco-friendly brand"
- Asset Types: ["instagram-post", "facebook-cover", "twitter-header", "icon-set"]
- Brand Guidelines: "Minimalist, nature-inspired, clean aesthetics"
- Color Palette: ["#2ECC71", "#27AE60", "#F1C40F"]

3. edit_asset

Edit existing assets with advanced controls.

Parameters:

• sourceImage (required): Image URL or base64 data

• editInstructions (required): Detailed editing instructions

• preserveElements: Elements to keep unchanged

• aspectRatio: Target aspect ratio

• resolution: Target resolution

Example:

Edit an asset with:
- Source Image: [URL or base64]
- Edit Instructions: "Adjust lighting to be warmer, increase contrast, soften shadows"
- Preserve Elements: ["logo", "text"]

4. ensure_brand_consistency

Analyze assets for brand consistency and get recommendations.

Parameters:

• assets (required): Array of asset URLs to analyze

• brandGuidelines (required): Brand guidelines

• referenceImages: Brand reference images

• colorPalette: Official brand colors

• logoFile: Official logo

Example:

Check consistency of:
- Assets: [multiple URLs]
- Brand Guidelines: "Modern, professional, use primary blue #667EEA"
- Color Palette: ["#667EEA", "#764BA2", "#FFFFFF"]

Model Capabilities

Nano Banana Pro (via OpenRouter) offers:

• Advanced Multimodal Reasoning: Understands context from reference images, logos, and descriptions

• High-Fidelity Visual Synthesis: Professional-quality outputs suitable for production

• Text Rendering: Industry-leading text placement in images with multilingual support

• Identity Preservation: Consistent styling across multiple assets (up to 5 subject references)

• Fine-Grained Controls: Localized edits, lighting adjustments, focus control, camera transformations

• Flexible Output: Support for multiple aspect ratios (1:1, 16:9, 9:16, 21:9, etc.)

• Search Grounding: Real-time information integration for context-rich graphics

• Base64 Image Output: Generated images are returned as base64-encoded data URLs

How It Works

The server uses OpenRouter's image generation API with the following key features:

1. Modalities: Requests specify ["image", "text"] to enable image generation

2. Image Configuration: Aspect ratios are passed via image_config.aspect_ratio

3. Reference Images: Input images are provided as URLs or base64 data for style guidance

4. Response Format: Generated images are returned in the message.images array as base64 data URLs

5. Multiple Outputs: The model can generate multiple variations or asset types in a single request

Use Cases

• Web Development: Generate UI components, icons, backgrounds, and layouts

• Brand Identity: Create consistent asset packs for marketing materials

• Product Design: Visualize products and create mockups

• Content Creation: Social media graphics, blog headers, and promotional materials

• Prototyping: Quick generation of design concepts and variations

• Asset Management: Edit and refine existing assets while maintaining brand consistency

Technical Details

MCP Server

• Model: google/gemini-3-pro-image-preview via OpenRouter

• API Endpoint: https://openrouter.ai/api/v1/chat/completions

• Protocol: Model Context Protocol (MCP)

• Transport: stdio

• Runtime: Node.js with TypeScript

• Image Format: Base64-encoded data URLs (PNG)

• Modalities: ["image", "text"] for image generation capabilities

Agent Skill

• Standard: Agent Skills specification v1.0

• Format: SKILL.md with YAML frontmatter + Markdown instructions

• Compatibility: All Agent Skills-compatible platforms

• Scripts: Bash and Python helper scripts included

• Documentation: Comprehensive references and templates

• Requirements: OpenRouter API key, internet access

API Implementation

The server implements OpenRouter's multimodal image generation API:

{
  model: "google/gemini-3-pro-image-preview",
  messages: [{ role: "user", content: [...] }],
  modalities: ["image", "text"],
  image_config: {
    aspect_ratio: "16:9"  // Configurable
  }
}

Response format:

{
  choices: [{
    message: {
      content: "...",  // Text description
      images: [{       // Generated images
        image_url: { url: "data:image/png;base64,..." }
      }]
    }
  }]
}

Development

MCP Server Development

# Install dependencies
npm install

# Build
npm run build

# Watch mode for development
npm run watch

# Test
npm run test

Agent Skill Development

The Agent Skill is ready to use - no build step required! To modify:

1. Edit nano-banana-assets-skill/SKILL.md for instructions

2. Update scripts in nano-banana-assets-skill/scripts/ as needed

3. Add reference materials to nano-banana-assets-skill/references/

4. Test with a compatible AI agent

Project Structure

.
├── src/                           # MCP server source code
│   └── index.ts                   # Main server implementation
├── dist/                          # Built MCP server
│   └── index.js                   # Compiled server
├── nano-banana-assets-skill/      # Agent Skill (portable)
│   ├── SKILL.md                   # Main skill definition
│   ├── README.md                  # Skill documentation
│   ├── scripts/                   # Helper scripts
│   │   ├── generate_asset.sh      # Bash helper
│   │   └── generate_asset.py      # Python helper
│   ├── references/                # Reference docs
│   │   ├── api-reference.md       # API documentation
│   │   └── prompt-templates.md    # Prompt examples
│   └── assets/                    # Example assets
├── README.md                      # This file
├── EXAMPLES.md                    # Usage examples
├── CONFIG.md                      # Configuration guide
├── QUICKSTART.md                  # Quick start guide
└── package.json                   # npm package config

Requirements

For MCP Server

• Node.js 18 or higher

• OpenRouter API key

• MCP-compatible client (e.g., Claude Desktop)

For Agent Skill

• OpenRouter API key

• Agent Skills-compatible AI platform

• Internet access

• (Optional) Python 3.10+ for Python scripts

Documentation

• Agent Skill Documentation - Complete guide to using the Agent Skill

• Quick Start Guide - Get started quickly with MCP server

• Examples - Detailed usage examples

• Configuration - Configuration options

• API Reference - API documentation

License

MIT

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Acknowledgments

• Built with Model Context Protocol SDK

• Follows Agent Skills specification

• Powered by OpenRouter

• Uses Google's Nano Banana Pro (Gemini 3 Pro Image Preview)