by shinpr
Gemini Image Generator uses AI to create images with prompt enhancement, character consistency, and multi-image blending
Connects Google's Gemini 2.5 Flash model to generate high-quality images from simple text prompts, with automatic prompt enhancement to add missing visual details.
Gemini Image Generator is a community-built MCP server published by shinpr that provides AI assistants with tools and capabilities via the Model Context Protocol. Gemini Image Generator uses AI to create images with prompt enhancement, character consistency, and multi-image blending It is categorized under ai ml.
You can install Gemini Image Generator in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Gemini Image Generator is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated Gemini Image Generator against two servers with overlapping tools; this profile had the clearer scope statement.
Gemini Image Generator has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We evaluated Gemini Image Generator against two servers with overlapping tools; this profile had the clearer scope statement.
Useful MCP listing: Gemini Image Generator is the kind of server we cite when onboarding engineers to host + tool permissions.
Gemini Image Generator is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Gemini Image Generator is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: Gemini Image Generator surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Gemini Image Generator reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend Gemini Image Generator for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Gemini Image Generator reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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AI image generation and editing MCP server for Cursor, Claude Code, Codex, and any MCP-compatible tool — powered by Nano Banana 2 and Nano Banana Pro (Google Gemini).
An MCP server that turns simple text prompts into high-quality images. Unlike a simple API wrapper, this server automatically enhances your prompt and configures sensible defaults for generation — you don't need to learn prompt engineering or tune settings. Just describe what you want.
You: "cat on a roof"
↓
Your AI assistant infers context
(purpose, style, mood, resolution...)
↓
MCP optimizes your prompt
(adds lighting, composition, atmosphere, artistic details)
↓
Image generation with smart defaults
(grounding, consistency, resolution — all configured automatically)
↓
High-quality image, zero effort
Your AI assistant interprets your intent — the style, purpose, and context behind your request. The MCP focuses on output quality by refining the prompt to meet a structured visual clarity standard and selecting appropriate generation settings. You just describe what you want.
The prompt optimizer uses a Subject–Context–Style framework (powered by Gemini 2.5 Flash) to fill in missing visual details — subject characteristics, environment, lighting, camera work — while preserving your original intent. It doesn't blindly add details: prompts that already meet the quality standard are left largely intact.
Example — what the optimizer does to a short prompt:
Input: "cat on a roof"
After optimization: "A sleek, midnight black cat, perched with poised elegance on the apex of a weathered, terracotta tile roof. Its emerald eyes, narrowed slightly, reflect the warm glow of a setting sun. Each individual tile is distinct, showing subtle variations in color and texture, with patches of moss clinging to the crevices. The cat's fur is sharply defined, catching the golden hour light, highlighting its sleek contours. In the background, the silhouettes of distant, old-world city buildings with ornate spires are softly blurred, bathed in a gradient of fiery orange, soft pink, and deep violet twilight. A gentle, ethereal mist begins to rise from the alleyways below, adding a touch of mystery. The composition is a medium shot, taken from a slightly low angle, emphasizing the cat's commanding presence against the vast sky. Photorealistic style, captured with a prime lens, wide aperture to create a beautiful bokeh, enhancing the depth of field."
This project also provides a standalone Agent Skill (SKILL.md) that teaches AI assistants to write better image generation prompts — no MCP server or API key required.
Note: This skill does not generate images itself. It teaches your AI assistant to write better prompts for tools that already have built-in image generation (e.g., Cursor's native image generation).
Based on the Subject-Context-Style framework, covering prompt structure, visual details (lighting, textures, camera angles), advanced techniques (character consistency, composition), and image editing. Works with any image model (Gemini, GPT Image, Flux, Stable Diffusion, Midjourney, etc.).
npx mcp-image skills install --path <target-directory>
The skill will be placed at <path>/image-generation/SKILL.md. Specify the skills directory for your AI tool:
# Cursor
npx mcp-image skills install --path ~/.cursor/skills
# Codex
npx mcp-image skills install --path ~/.codex/skills
# Claude Code
npx mcp-image skills install --path ~/.claude/skills
| MCP Server | Agent Skill | |
|---|---|---|
| Use when | Your AI tool does not have built-in image generation | Your AI tool already generates images natively |
| Requires | Gemini API key | Nothing |
| What it does | Generates images via Gemini API with automatic prompt optimization | Teaches the AI to write better prompts |
| Works with | MCP-compatible tools (Cursor, Claude Code, Codex, etc.) | Any tool supporting the Agent Skills open standard |
Get your API key from Google AI Studio
Add to ~/.codex/config.toml:
[mcp_servers.mcp-image]
command = "npx"
args = ["-y", "mcp-image"]
[mcp_servers.mcp-image.env]
GEMINI_API_KEY = "your_gemini_api_key_here"
IMAGE_OUTPUT_DIR = "/absolute/path/to/images"
Add to your Cursor settings:
~/.cursor/mcp.json.cursor/mcp.json in your project root{
"mcpServers": {
"mcp-image": {
"command": "npx",
"args": ["-y", "mcp-image"],
"env": {
"GEMINI_API_KEY": "your_gemini_api_key_here",
"IMAGE_OUTPUT_DIR": "/absolute/path/to/images"
}
}
}
}
Run in your project directory to enable for that project:
cd /path/to/your/project
claude mcp add mcp-image --env GEMINI_API_KEY=your-api-key --env IMAGE_OUTPUT_DIR=/absolute/path/to/images -- npx -y mcp-image
Or add globally for all projects:
claude mcp add mcp-image --scope user --env GEMINI_API_KEY=your-api-key --env IMAGE_OUTPUT_DIR=/absolute/path/to/images -- npx -y mcp-image
⚠️ Security Note: Never commit your API key to version control. Keep it secure and use environment-specific configuration.
📁 Path Requirements:
IMAGE_OUTPUT_DIR must be an absolute path (e.g., /Users/username/images, not ./images)./output in the current working directory if not specifiedChoose the right balance of speed, quality, and cost:
| Preset | Model | Best for | Speed |
|---|---|---|---|
fast (default) | Nano Banana 2 (Gemini 3.1 Flash Image) | Quick iterations, drafts, high-volume generation | ~30–40s |
balanced | Nano Banana 2 + Thinking | Production images, good quality with reasonable speed | Medium |
quality | Nano Banana Pro (Gemini 3 Pro Image) | Final deliverables, maximum fidelity, critical visuals | Slow |
Set the default via IMAGE_QUALITY environment variable:
IMAGE_QUALITY=fast # (default) Fastest generation
IMAGE_QUALITY=balanced # Enhanced thinking for better quality
IMAGE_QUALITY=quality # Maximum quality output
To override per-request, just tell your AI assistant (e.g., "generate in high quality" or "use balanced quality"). The assistant will pass the appropriate quality parameter automatically.
Codex:
[mcp_servers.mcp-image.env]
GEMINI_API_KEY = "your_gemini_api_key_here"
IMAGE_QUALITY = "balanced"
Cursor:
Add "IMAGE_QUALITY": "balanced" to the env section in your config.
Claude Code:
claude mcp add mcp-image --env GEMINI_API_KEY=your-api-key --env IMAGE_QUALITY=balanced --env IMAGE_OUTPUT_DIR=/absolute/path/to/images -- npx -y mcp-image
Set SKIP_PROMPT_ENHANCEMENT=true to disable automatic prompt optimization and send your prompts directly to the image generator. Useful when you need full control over the exact prompt wording.
Once configured, just describe what you want in natural language:
"Generate a serene mountain landscape at sunset with a lake reflection"
Your prompt is automatically enhanced with rich details about lighting, materials, composition, and atmosphere.
"Edit this image to make the person face right"
(with inputImagePath: "/path/to/image.jpg")
Character Consistency:
"Generate a portrait of a medieval knight, maintaining character consistency for future variations"
(with maintai
---
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.