多提供商图像生成 MCP 服务

imagen

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

Quick Start

This project was renamed from imagen-mcp to imagen (michaeljabbour/imagen-mcp -> michaeljabbour/imagen; the old GitHub URL redirects). The MCP server executable (imagen-mcp) and its imagen_mcp Python package name are unchanged -- only the project/distribution name and the smart-tool CLI (imagen-mcp-smart -> imagen) changed.

Version 0.5.0 is released as the immutable v0.5.0 Git tag and is not published on PyPI. Install it from that tag or a local checkout, then run the canonical module or console script:

git clone https://github.com/michaeljabbour/imagen.git
cd imagen
python3 -m pip install .
python -m imagen_mcp
# equivalent after installation: imagen-mcp

For a reproducible VCS install, use the release tag:

python3 -m pip install \
  "imagen @ git+https://github.com/michaeljabbour/imagen.git@v0.5.0"

The historical python -m src.server entry point remains available for compatibility in 0.4.x.

See CHANGELOG.md for release details and migration notes.

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

Having both keys lets auto-selection use either provider. With only one key, soft preferences may fall back with a notice. An unavailable explicit provider pin fails closed; in auto mode, Gemini-only requirements such as reference images and Google Search grounding also fail closed rather than silently dropping the requested capability.

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

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

claude mcp list

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

codex mcp add imagen -- imagen-mcp

[mcp_servers.imagen]
command = "imagen-mcp"

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

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

3. Generate an image — ask your AI assistant:

"Generate a professional headshot with studio lighting"

That's it. The server picks a provider automatically and uses Gemini 3.1 Flash Image when Gemini is selected unless you explicitly request another Gemini model.

Features

• Auto Provider Selection — analyzes prompts to choose the best provider
• Multi-Provider Support — OpenAI gpt-image-2 and Google Gemini 3 Image
• 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")

Explicit provider pins never fall back. In auto mode, hard Gemini requirements (reference images, Google Search grounding, and recognized real-time-data requests) also fail if Gemini is unavailable. Soft quality preferences may fall back to the configured provider and include a notice.

Provider Comparison

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

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

For gpt-image-2, both edges must be multiples of 16 and no larger than 3840px, the long-to-short ratio must be at most 3:1, and total pixels must be between 655,360 and 8,294,400. Outputs above 2560x1440 are experimental. gpt-image-2 does not support transparent backgrounds; use an opaque output and a downstream background-removal step.

MCP Tools

All tools advertise MCP tool annotations (read-only / open-world hints) so clients can reason about their side effects.

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-3.1-flash-image")

# 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)

Search grounding requires Markdown output and a client that renders the returned Google Search Suggestions HTML plus associated source links. JSON output fails before calling Gemini because escaped HTML is not a compliant rendered surface.

Available Models

OpenAI

Gemini

Retired *-preview IDs are rejected with an actionable GA migration message; explicit model pins never silently change. Gemini 3.1 Flash Lite Image outputs include SynthID and C2PA provenance metadata; downstream transforms should preserve that metadata when the file format and processing pipeline allow it.

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-2]
            GEM[Gemini Provider<br/>Gemini 3.1 Flash Image]
        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

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.

"imagen-mcp: command not found" Ensure the Python environment used by your MCP client has imagen-mcp installed from a local checkout or pinned VCS revision and its scripts directory is on PATH. As a fallback, configure the command as python with args -m, imagen_mcp.

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 imagen_mcp.providers import get_provider_registry; print(get_provider_registry().list_providers())"

Development

# Clone and install (runtime + dev tooling)
git clone https://github.com/michaeljabbour/imagen.git
cd imagen
pip install -e ".[dev]"          # or: uv sync --extra dev

# Install pre-commit hooks (ruff + mypy)
pre-commit install

# Run the full quality gate (same as CI)
ruff format --check imagen_mcp/ src/ tests/
ruff check imagen_mcp/ src/ tests/
mypy imagen_mcp/ src/
pytest --cov=src --cov-fail-under=80

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

# Run over HTTP instead of stdio
IMAGEN_MCP_TRANSPORT=streamable-http imagen-mcp

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

Project Structure

imagen/
├── imagen_mcp/               # Canonical package namespace + module runner
├── src/
│   ├── server.py              # Implementation + legacy import path
│   ├── 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>=1.26.0,<2
pydantic>=2.12.3
httpx>=0.28.0
google-genai>=2.8.0
pillow>=11.0.0

License

MIT

Sources

• Claude Code MCP Documentation
• Gemini CLI MCP Documentation
• Codex MCP Documentation
• Model Context Protocol