Which integrations are available for this server?

Generates images using Google Gemini's image models (Nano Banana Pro, Flash, Imagen 3.0) with support for photorealism, up to 4K resolution, reference images for character/style consistency, real-time data via Google Search grounding, and conversational history for iterative refinement. Generates images using OpenAI's GPT-Image-1 model, optimized for text-heavy images like menus, infographics, comics, and diagrams with excellent text rendering capabilities.

How do I use imagen-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@imagen-mcp create a logo for a coffee shop called 'Morning Brew' with a minimalist design" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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imagen-mcp

by michaeljabbour

Overview Schema Related Servers Score Discussions

Python

Hybrid

imagen-mcp

A Model Context Protocol (MCP) server for intelligent multi-provider image generation.

Python 3.10+ License: MIT

Quick Start

1. Get an API key (at least one):

Provider	Get a key at	Environment variable
OpenAI	platform.openai.com/api-keys	`OPENAI_API_KEY`
Google Gemini	aistudio.google.com/apikey	`GEMINI_API_KEY`

Having both keys gives you the best results — the server automatically picks the right provider for each prompt. With only one key, prompts better suited for the other provider will still work, but you'll see a fallback notice.

2. Add to your MCP client (pick one):

claude mcp add -s user imagen \
  -e OPENAI_API_KEY=sk-... \
  -e GEMINI_API_KEY=AI... \
  -- npx -y imagen-mcp

Verify it's registered:

claude mcp list

Reference: Claude Code MCP docs

Edit the config file:

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

{
  "mcpServers": {
    "imagen": {
      "command": "npx",
      "args": ["-y", "imagen-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GEMINI_API_KEY": "AI..."
      }
    }
  }
}

Restart Claude Desktop (Cmd+Q, then reopen) after editing.

Reference: Claude Desktop MCP docs

Option A — CLI command:

codex mcp add imagen -- npx -y imagen-mcp

Option B — edit ~/.codex/config.toml directly:

[mcp_servers.imagen]
command = "npx"
args = ["-y", "imagen-mcp"]

[mcp_servers.imagen.env]
OPENAI_API_KEY = "sk-..."
GEMINI_API_KEY = "AI..."

Reference: Codex MCP docs

Edit ~/.gemini/settings.json:

{
  "mcpServers": {
    "imagen": {
      "command": "npx",
      "args": ["-y", "imagen-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GEMINI_API_KEY": "AI..."
      }
    }
  }
}

Reference: Gemini CLI MCP docs

Setting	Value
Command	`npx`
Args	`["-y", "imagen-mcp"]`
Environment	`OPENAI_API_KEY` and/or `GEMINI_API_KEY`

3. Generate an image — ask your AI assistant:

"Generate a professional headshot with studio lighting"

That's it. The server picks the best provider automatically.

Features

Auto Provider Selection — analyzes prompts to choose the best provider
Multi-Provider Support — OpenAI GPT-Image-1 and Google Gemini
Reference Images — up to 14 images for character/style consistency (Gemini)
Real-time Data — Google Search grounding for current info (Gemini)
Conversational Refinement — iteratively refine images with context
High Resolution — up to 4K output (Gemini)
Fallback Notices — clear warnings when a prompt would benefit from a provider you haven't configured

How Auto-Selection Works

The server analyzes your prompt and routes it to the best provider:

"Create a menu card for an Italian restaurant"  -> OpenAI (text rendering)
"Professional headshot with studio lighting"    -> Gemini (photorealism)
"Infographic about climate change"              -> OpenAI (diagram + text)
"Product shot of perfume on marble"             -> Gemini (product photography)

What if the best provider isn't configured? The server falls back to whatever you have and tells you:

Provider Fallback: Gemini would be better for this prompt (Photorealistic content), but it's not configured. Using OpenAI instead. Set GEMINI_API_KEY for better results.

You can always override auto-selection with the provider parameter:

generate_image(prompt="...", provider="openai")
generate_image(prompt="...", provider="gemini")

Provider Comparison

Feature	OpenAI GPT-Image-1	Gemini Nano Banana Pro
Text Rendering	Excellent	Good
Photorealism	Good	Excellent
Speed	~60s	~15s
Max Resolution	1536x1024	4K
Sizes	3 options	1K, 2K, 4K
Aspect Ratios	3	10
Reference Images	No	Yes (up to 14)
Real-time Data	No	Yes (Google Search)

Use OpenAI for: text-heavy images, menus, infographics, comics, diagrams

Use Gemini for: portraits, product photography, 4K output, reference images

MCP Tools

Tool	Description
`generate_image`	Main tool with auto provider selection
`conversational_image`	Multi-turn refinement with dialogue and history
`list_conversations`	List active conversations and their history
`list_providers`	Show available providers and capabilities
`list_gemini_models`	Query available Gemini image models

Output Location

Images are saved to ~/Downloads/images/{provider}/ by default (openai/ or gemini/ subdirectories).

Customize with:

# Save to a specific directory (auto-generated filename)
generate_image(prompt="...", output_path="~/Desktop/logos/")

# Save to a specific file
generate_image(prompt="...", output_path="~/Desktop/logos/my-logo.png")

Set OUTPUT_DIR to change the base directory globally. Logs go to {OUTPUT_DIR}/logs/.

Gemini-Specific Features

# High resolution
generate_image(prompt="...", size="4K")

# Specific model
generate_image(prompt="...", gemini_model="gemini-2.0-flash-exp-image-generation")

# Reference images for style/character consistency (base64 encoded)
generate_image(prompt="...", reference_images=["base64..."])

# Real-time data via Google Search
generate_image(prompt="Current weather in NYC", enable_google_search=True)

Available Models

OpenAI

Model ID	Description
`gpt-image-1`	Dedicated image generation model (default)
`gpt-5-image`	GPT-5 with image generation capabilities
`gpt-5.1`	Latest reasoning model (conversation orchestration)

Gemini

Model ID	Description
`gemini-3-pro-image-preview`	Nano Banana Pro - highest quality (default)
`gemini-2.0-flash-exp-image-generation`	Fast experimental
`imagen-3.0-generate-002`	Alternative image model

Architecture

flowchart TB
    subgraph Clients["MCP Clients"]
        CD[Claude Desktop]
        CC[Claude Code CLI]
        GC[Gemini CLI]
        CX[Codex CLI]
    end

    subgraph Server["imagen-mcp Server"]
        MCP[MCP Protocol Layer]

        subgraph Tools["MCP Tools"]
            GI[generate_image]
            CI[conversational_image]
            LP[list_providers]
            LM[list_gemini_models]
        end

        subgraph Core["Core Components"]
            PS[Provider Selector]
            PR[Provider Registry]
        end

        subgraph Providers["Image Providers"]
            OAI[OpenAI Provider<br/>GPT-Image-1]
            GEM[Gemini Provider<br/>Nano Banana Pro]
        end
    end

    subgraph APIs["External APIs"]
        OAPI[OpenAI API]
        GAPI[Google Gemini API]
    end

    subgraph Storage["Local Storage"]
        DL[~/Downloads/images/]
    end

    CD & CC & GC & CX --> MCP
    MCP --> Tools
    GI & CI --> PS
    PS --> PR
    PR --> OAI & GEM
    OAI --> OAPI
    GEM --> GAPI
    OAI & GEM --> DL

Environment Variables

Variable	Description	Required
`OPENAI_API_KEY`	OpenAI API key	At least one API key
`GEMINI_API_KEY`	Google Gemini API key	is required
`GOOGLE_API_KEY`	Alias for `GEMINI_API_KEY`
`OUTPUT_DIR`	Base directory for saved images	No (default: `~/Downloads/images/`)
`DEFAULT_PROVIDER`	Force a default provider	No (default: `auto`)
`DEFAULT_OPENAI_SIZE`	Default OpenAI image size	No (default: `1024x1024`)
`DEFAULT_GEMINI_SIZE`	Default Gemini image size	No (default: `2K`)
`ENABLE_GOOGLE_SEARCH`	Enable Google Search grounding	No (default: `false`)
`IMAGEN_MCP_LOG_DIR`	Log directory override	No
`IMAGEN_MCP_LOG_LEVEL`	Log level (DEBUG, INFO, etc.)	No
`IMAGEN_MCP_LOG_PROMPTS`	Log full prompts	No (default: `false`)

Troubleshooting

"No providers available" You need at least one API key. Set OPENAI_API_KEY or GEMINI_API_KEY in your MCP client config (see Quick Start above).

Images generate but quality isn't great for portraits/products You're probably missing GEMINI_API_KEY. The server fell back to OpenAI and showed a warning. Add a Gemini key for better photorealistic results.

Images generate but text looks bad You're probably missing OPENAI_API_KEY. Add an OpenAI key for better text rendering.

"npx: command not found" Install Node.js (which includes npx): nodejs.org

Where are my images saved? Default: ~/Downloads/images/openai/ or ~/Downloads/images/gemini/. Check the tool output for the exact path. Set OUTPUT_DIR to change this.

How do I check which providers are active? Use the list_providers tool, or run:

python3 -c "from src.providers import get_provider_registry; print(get_provider_registry().list_providers())"

Development

# Clone and install
git clone https://github.com/michaeljabbour/imagen-mcp.git
cd imagen-mcp
pip install -r requirements.txt

# Run tests
pip install pytest pytest-asyncio
pytest tests/ -v

# Verify server loads
python3 -c "from src.server import mcp; print('Server loads')"

# Check Claude Desktop logs (macOS)
tail -f ~/Library/Logs/Claude/mcp-server-imagen.log

Project Structure

imagen-mcp/
├── src/
│   ├── server.py              # MCP entry point
│   ├── config/
│   │   ├── constants.py       # Provider constants
│   │   └── settings.py        # Environment configuration
│   ├── providers/
│   │   ├── base.py            # Abstract provider interface
│   │   ├── openai_provider.py # OpenAI implementation
│   │   ├── gemini_provider.py # Gemini implementation
│   │   ├── selector.py        # Auto-selection logic
│   │   └── registry.py        # Provider factory
│   └── models/
│       └── input_models.py    # Pydantic input models
├── tests/
│   ├── test_selector.py       # Provider selection tests
│   ├── test_providers.py      # Provider unit tests
│   └── test_server.py         # Server integration tests
├── .github/
│   └── workflows/
│       └── ci.yml             # GitHub Actions CI
├── run.sh                     # Wrapper script for MCP clients
├── requirements.txt
├── CLAUDE.md
└── README.md

Requirements

mcp>=1.16.0
fastmcp>=2.12.5
pydantic>=2.12.3
httpx>=0.24.0
google-genai>=1.52.0
pillow>=10.4.0

License

MIT

Sources

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Resources

GitHub Repository

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