AI Image Generation - Claude Skill + MCP

AI-powered image generation using Google Gemini or OpenAI (gpt-image-2), integrated with Claude Code.

Features

• Generate images from text prompts using Google Gemini or OpenAI (gpt-image-2)
• Dual-provider support: model name routes the request automatically (gpt-image*/dall-e* → OpenAI, everything else → Gemini)
• Proactive Claude skill suggests images for websites, presentations, and more
• Two execution modes: CLI script (skill-only) or MCP server (protocol-based)
• Configurable aspect ratios (1:1, 16:9, 9:16, etc.)
• Multiple model support (quality vs speed) across both providers
• Optional reference images to guide generation (up to 5, PNG/JPEG/WebP) on both providers
• Images saved to disk within the configured output directory, with file paths returned
• MCP server speaks the MCP 2026-07-28 spec, with backward compatibility for older MCP clients

Prerequisites

• Google Gemini API key (Get one here) and/or an OpenAI API key (Get one here) — at least one is required
• Node.js 20+ (only for manual installation)

Installation

Quick Install (Claude Code Plugin)

The plugin installs skill + CLI + MCP server in one step—no separate configuration needed.

# Add the marketplace
/plugin marketplace add guinacio/claude-image-gen

# Install the plugin
/plugin install media-pipeline@media-pipeline-marketplace

Or install directly from GitHub:

/plugin install guinacio/claude-image-gen

Once installed:

• Skill uses the bundled CLI script (no MCP overhead)
• MCP server is also available for direct tool calls

Tip: Since the skill runs the CLI directly, you can disable the MCP server in Claude Code's MCP list to reduce startup overhead. The skill will continue to work without it.

---

Quick Install (Claude Desktop Extension)

For Claude Desktop users, install the pre-built extension:

1. Download media-pipeline.mcpb from Releases
2. Open Claude Desktop
3. Go to Settings → Extensions → Advanced settings
4. Click Install Extension and select the .mcpb file
5. Enter your Gemini and/or OpenAI API key when prompted (at least one is required)

---

Manual Installation

For developers who want to customize or build from source:

1. Build the MCP Server

cd mcp-server
npm install
npm run bundle

2. Use the Standalone CLI

cd mcp-server
GEMINI_API_KEY=your-api-key-here node build/cli.bundle.js \
  --prompt "Landing page hero image for a fintech startup" \
  --aspect-ratio "16:9"

The CLI routes to Gemini or OpenAI based on the model name and returns structured JSON on stdout. It does not require the MCP server layer. Any custom output path must still remain inside the configured output directory.

3. Add to Claude Code

Option A: Using MCP server

claude mcp add --transport stdio media-pipeline \
  --env GEMINI_API_KEY=your-api-key-here \
  -- node /path/to/claude-image-gen/mcp-server/build/bundle.js

The -- separates Claude CLI flags from the server command.

Option B: Manual config

Add to your Claude Code config (~/.claude.json):

{
  "mcpServers": {
    "media-pipeline": {
      "command": "node",
      "args": ["/path/to/claude-image-gen/mcp-server/build/bundle.js"],
      "env": {
        "GEMINI_API_KEY": "${GEMINI_API_KEY}",
        "GEMINI_DEFAULT_MODEL": "${GEMINI_DEFAULT_MODEL:-gemini-3-pro-image-preview}",
        "OPENAI_API_KEY": "${OPENAI_API_KEY}",
        "OPENAI_DEFAULT_MODEL": "${OPENAI_DEFAULT_MODEL:-gpt-image-2}",
        "IMAGE_PROVIDER": "${IMAGE_PROVIDER:-gemini}",
        "IMAGE_OUTPUT_DIR": "${IMAGE_OUTPUT_DIR:-./generated-images}",
        "GEMINI_REQUEST_TIMEOUT_MS": "${GEMINI_REQUEST_TIMEOUT_MS:-60000}",
        "MEDIA_PIPELINE_LOG_LEVEL": "${MEDIA_PIPELINE_LOG_LEVEL:-info}"
      }
    }
  }
}

The ${VAR:-default} syntax uses environment variables with fallback defaults.

4. Install Skill Manually (Optional)

If not using the plugin:

cp -r skills/image-generation ~/.claude/skills/

4. Build Extension from Source (Optional)

To create your own .mcpb extension for Claude Desktop:

cd mcp-server
npm install -g @anthropic-ai/mcpb
npm run pack:mcpb

This creates mcp-server/media-pipeline.mcpb using bundled runtime entry points for both the MCP server and the standalone CLI.

Usage

Direct Tool Usage

Use create_asset to create a hero image for a tech startup website

With the Skill

The skill will proactively suggest image generation when:

• Building websites with hero sections
• Creating presentations
• Working with placeholder images
• Developing marketing materials

Configuration

Environment Variables

Variable	Required	Default	Description
GEMINI_API_KEY	At least one of GEMINI_API_KEY / OPENAI_API_KEY	-	Your Gemini API key
GEMINI_DEFAULT_MODEL	No	gemini-3-pro-image-preview	Default Gemini model to use
OPENAI_API_KEY	At least one of GEMINI_API_KEY / OPENAI_API_KEY	-	Your OpenAI API key
OPENAI_DEFAULT_MODEL	No	gpt-image-2	Default OpenAI model to use
IMAGE_PROVIDER	No	gemini	Provider (gemini or openai) used when a request omits model
IMAGE_OUTPUT_DIR	No	~/generated-images	Where to save images
GEMINI_REQUEST_TIMEOUT_MS	No	60000	Request timeout, applies to both Gemini and OpenAI requests
MEDIA_PIPELINE_LOG_LEVEL	No	info	Stderr logging level

Providers

The server is dual-provider: it routes each request to Google Gemini or OpenAI (gpt-image-2) automatically based on the model name — models starting with gpt-image or dall-e go to OpenAI, everything else goes to Gemini. When a request omits model entirely, IMAGE_PROVIDER selects which provider's default model is used.

• Aspect ratios on OpenAI: OpenAI image models only support 1024x1024, 1536x1024, and 1024x1536. Requested aspect ratios are mapped to the nearest supported size, and if the mapping isn't exact the response includes a warning describing the substitution.
• Reference images: supported on both providers — pass up to 5 reference image paths to guide generation.

Models

Gemini models are fetched dynamically from the Gemini API at runtime; the CLI and MCP tool validate Gemini model choices against the current image-capable model list, and GEMINI_DEFAULT_MODEL is used when available. For OpenAI, gpt-image-2 is the default model (OPENAI_DEFAULT_MODEL); any gpt-image*/dall-e* model name routes to OpenAI.

Aspect Ratios

Ratio	Best For
1:1	Social media, thumbnails
16:9	Hero images, presentations
9:16	Mobile stories, vertical banners
4:3	Blog posts, general web
3:2	Photography-style images

On OpenAI models, aspect ratios other than 1:1/3:2/2:3-equivalent are mapped to the nearest supported size (see Providers).

Prompt Tips

Use this formula for effective prompts:

[Style] [Subject] [Composition] [Context/Atmosphere]

Example:

Minimalist 3D illustration of abstract geometric shapes floating in space,
soft gradient background from deep purple to electric blue, subtle glow effects,
modern professional aesthetic, wide composition for website header

See skills/image-generation/references/prompt-crafting.md for advanced techniques.

Architecture

Two Execution Modes

CLI Mode (Default) - Used by the skill:

Claude → Skill → Bash → bundled CLI → Gemini API / OpenAI API

• No MCP protocol overhead
• Skill runs bundled CLI directly
• All dependencies bundled in a single file

MCP Mode (Optional) - For direct tool calls:

Claude → MCP Tool → bundled MCP server → Gemini API / OpenAI API

• Speaks the MCP 2026-07-28 spec, with backward-compatible fallback for older MCP clients
• Useful for non-skill workflows
• Extension package only needs bundled entry points

Abstract MCP Naming

The MCP server uses intentionally abstract naming (media-pipeline / create_asset) rather than image-specific names (gemini-image-gen / generate_image).

Why? When tool names directly match intent (e.g., "I need to generate an image" → generate_image), AI assistants tend to call the MCP tool directly, bypassing the skill layer. By using generic names:

• The skill (image-generation) becomes the semantically obvious choice for image tasks
• The MCP tool doesn't immediately register as the solution
• The skill's prompt optimization and aspect ratio selection are properly utilized

This is a form of prompt engineering for tool selection—making the abstraction layer the natural choice while the underlying implementation has a name that doesn't invite direct use.

Project Structure

claude-image-gen/
├── .claude-plugin/       # Plugin configuration
│   ├── plugin.json       # Plugin manifest
│   └── marketplace.json  # Marketplace distribution
├── mcp-server/           # Server and CLI implementation
│   ├── src/
│   │   ├── index.ts      # MCP server entry point
│   │   ├── cli.ts        # CLI entry point (skill uses this)
│   │   ├── gemini-client.ts
│   │   ├── openai-client.ts
│   │   ├── provider.ts   # Routes requests to Gemini or OpenAI by model name
│   │   ├── image-storage.ts
│   │   └── types.ts
│   ├── build/
│   │   ├── bundle.js     # Bundled MCP server
│   │   └── cli.bundle.js # Bundled CLI (all deps included)
│   ├── .mcpbignore       # Package only the runtime files needed by the bundle
│   ├── manifest.json     # MCPB extension manifest
│   ├── icon.png          # Extension icon
│   ├── package.json
│   └── tsconfig.json
├── skills/               # Claude skills
│   └── image-generation/
│       ├── SKILL.md      # Skill instructions (uses CLI)
│       └── references/
├── .mcp.json            # MCP configuration
└── README.md

License

MIT