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
• Opt-in specialized workflows for narrower production pipelines, without adding them to every installation
• 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
• Inpainting with a PNG mask, plus background and outputFormat control, on OpenAI models
• 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 the core image-generation skill + CLI + MCP server in one step—no separate configuration needed. Specialized workflows are intentionally opt-in.

# 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:

• Core skill uses the bundled CLI script (no MCP overhead)
• MCP server is also available for direct tool calls
• Specialized workflows are not auto-installed; add only the ones you need

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}",
        "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/

#### 5. Install a Specialized Workflow (Optional)

Specialized workflows live outside skills/, so the plugin does not discover or install them automatically. The current character-reference workflow is for dressing Blender character renders and preparing garments for image-to-3D tools.

From a cloned repository:

python -m pip install -r optional-workflows/character-reference-sheets/requirements.txt
cp -r optional-workflows/character-reference-sheets ~/.claude/skills/

Built a specialized workflow of your own? See Contributing a Specialized Workflow.

#### 6. 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.

The packed extension is committed to the repo and served from the Releases page, so it must not fall behind source. pack:mcpb records a hash of every packed file in mcp-server/mcpb-contents.json, and npm test recomputes them — change the source without repacking and the suite fails. The archive itself is not byte-reproducible (zip stores timestamps), which is why the inputs are hashed rather than the .mcpb.

#### Versioning

mcp-server/package.json is the single source of truth for the version. mcp-server/manifest.json, mcp-server/package-lock.json, .claude-plugin/plugin.json and .claude-plugin/marketplace.json are all derived from it — never edit their version fields by hand.

To bump:

cd mcp-server && npm version minor --no-git-tag-version

npm version updates package.json and the lockfile, then the version lifecycle script propagates it to the remaining manifests and stages them. npm run sync:version does the same propagation on its own, and npm run check:version reports drift without writing. Both npm test and npm run pack:mcpb fail on drift, so a release cannot ship with mismatched manifests.

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

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.
• `mask`, `background`, `outputFormat`: OpenAI models only — Gemini models reject them.
• mask takes an absolute path to a PNG whose transparent areas are the region the model repaints; everything else is preserved from the base image. It requires referenceImages, and OpenAI documents the mask as needing an alpha channel and the same dimensions as the base image. Checked locally: the file really is a PNG, it is under the API's 4MB mask limit (masks are capped well below reference images, whose limit here is 20MB), and it is not fully opaque — an opaque mask marks nothing, so it is rejected before the request is sent. Left to the API: the dimension match, and whether a PNG carrying transparency in a tRNS chunk instead of an alpha channel is accepted — that case is sent with a warning rather than blocked.
• background (auto, transparent, opaque) chooses how the background is handled. transparent needs an alpha-capable outputFormat, so png is selected automatically when outputFormat is omitted. gpt-image-2 rejects transparent, and that combination is refused before the request is sent.
• outputFormat (png, jpeg, webp) sets the encoding of the returned image; jpeg cannot carry transparency.

Apart from the gpt-image-2/transparent case above, these three options have not been verified against a live API — when a model refuses one, the API's own reason is returned.

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. When discovery fails, the built-in fallbacks are gemini-3-pro-image and gemini-3.1-flash-lite-image. For OpenAI, gpt-image-2 is the default model (OPENAI_DEFAULT_MODEL); any gpt-image*/dall-e* model name routes to OpenAI.

Aspect Ratios

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/               # Core skills installed with the plugin
│   └── image-generation/
│       ├── SKILL.md      # Skill instructions (uses CLI)
│       └── references/
├── optional-workflows/   # Specialized skills installed explicitly
│   └── character-reference-sheets/
│       ├── SKILL.md
│       ├── references/
│       ├── requirements.txt
│       └── scripts/
├── .mcp.json            # MCP configuration
└── README.md

Contributing a Specialized Workflow

optional-workflows/ exists for exactly this: workflows that are too specialized to belong in every installation, but general enough that other people would benefit from them. If you have built one, a PR adding it to that folder is welcome.

The bar is that middle ground. character-reference-sheets is the reference example — it is narrow (dressing Blender character renders for image-to-3D tools) yet reusable by anyone doing that work. Something tied to your own file layout, employer, or one-off project is probably too specific; something that helps everyone generating images probably belongs in the core skills/image-generation skill instead.

What a workflow needs:

optional-workflows/<your-workflow>/
├── SKILL.md          # required: frontmatter with name + description, then the instructions
├── references/       # optional: deeper documentation the skill points to
├── scripts/          # optional: helper scripts
└── requirements.txt  # optional: only if scripts have dependencies

A few things that make a workflow good rather than merely present:

• *Write a `description` that says when not to use it.* Every skill in this repo competes for the same trigger space — character-reference-sheets ends its description by deferring website and banner imagery to the core skill. Without that, skills fire over each other.
• Say what you actually verified. If a parameter's behaviour is an assumption rather than something you tested against the live API, write that down. The same discipline applies across mcp-server/ and it is why the tool schemas distinguish verified claims from deferred ones.
• Respect that generations cost money. Workflows that generate in a loop, or retry silently, spend the user's API credit without asking.
• Keep it self-contained. Reference paths relative to the workflow folder, since users copy the directory to ~/.claude/skills/.

Adding a workflow does not need a version bump — nothing in optional-workflows/ ships in the plugin or the .mcpb. Open a PR against master describing what the workflow is for and what you ran it against.

Bug reports and fixes to the core skill, CLI, or MCP server are equally welcome — those do go through the versioning flow, and npm test must pass.

License

MIT