media-pipeline

Gemini Image Generation - Claude Skill + MCP

AI-powered image generation using Google Gemini, integrated with Claude Code.

Features

• Generate images from text prompts using Gemini AI
• 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)
• Images saved to disk with file paths returned

Prerequisites

• Google Gemini API key (Get one here)
• Node.js 18+ (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.

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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 API key when prompted

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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 runs directly against Gemini and returns structured JSON on stdout. It does not require the MCP server layer.

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}",
        "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	Yes	-	Your Gemini API key
GEMINI_DEFAULT_MODEL	No	gemini-3-pro-image-preview	Default model to use
IMAGE_OUTPUT_DIR	No	./generated-images	Where to save images
GEMINI_REQUEST_TIMEOUT_MS	No	60000	Timeout for Gemini requests
MEDIA_PIPELINE_LOG_LEVEL	No	info	Stderr logging level

Models

Available image models are fetched dynamically from the Gemini API at runtime. The CLI and MCP tool validate model choices against the current image-capable model list, and GEMINI_DEFAULT_MODEL is used when available.

Aspect Ratios