Topaz MCP

Community Media and design

AI image enhancement (upscaling, denoising, sharpening) via the Topaz Labs API, with models such as Standard V2, Wonder 2, Bloom and Recover 3.

Install

The installation steps are in the documentation below. Documentation

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. @topazlabs/mcp
  2. See It in Action
  3. Quick Start
  4. Compatibility
  5. Tool
  6. Environment Variables
  7. Architecture
  8. Development
  9. Limitations
  10. Contributing
  11. License

@topazlabs/mcp

AI image enhancement for LLMs. An MCP server that lets Claude and other AI assistants enhance images using Topaz Labs AI models.

Topaz Labs makes AI-powered image and video enhancement software used by photographers, filmmakers, and developers worldwide.

See It in Action

BeforeAfter

Enhanced with the Wonder 2 model. View interactive comparison →

Quick Start

1. Get an API key

Sign up at developer.topazlabs.com.

2. Add to your MCP client

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "Topaz Labs": {
      "command": "npx",
      "args": ["-y", "@topazlabs/mcp"],
      "env": {
        "TOPAZ_API_KEY": "<your-api-key>"
      }
    }
  }
}
3. Enhance images

You: "Enhance ~/Desktop/photo.jpg with Wonder 2"

Claude: I'll enhance your photo using Topaz Labs' Wonder 2 model.

\[calls image-enhance tool\]

Note: This tool requires a file path or URL — it cannot process images uploaded directly in the chat. It works best with Claude Cowork, which has file system access. It does not work in Claude Chat mode (no file system access).

Here's your enhanced image -- upscaled and sharpened with improved detail and clarity.

Compatibility

This server uses stdio transport, supported by all major MCP desktop clients:

ClientStatus
Claude Cowork✅ (recommended — has file system access)
Claude Desktop (Chat)⚠️ Requires user to provide file paths manually
Cursor✅
Windsurf✅
VS Code (Copilot)✅
Cline✅
Claude Code✅
Zed✅

HTTP streaming transport (for ChatGPT, remote agents, web/mobile clients) is not yet supported but may be added in a future release.

Tool

image-enhance

Send an image, get back an enhanced version. Defaults to Standard V2 and auto-upscales to 2K.

image: "~/Photos/photo.jpg"     # Local path or URL (required)
size: { scale: 2 }              # Optional: exact dims, single dim, or scale factor
model: "Standard V2"            # Optional: defaults to Standard V2

Size options: { width, height } for exact, { width } or { height } for proportional, { scale } for a multiplier. If omitted, images under 2K are auto-upscaled to 2K width.

Models:

ModelTypeBest for
Standard V2standardGeneral photos (default)
Low Resolution V2standardTiny images, thumbnails
CGIstandardDigital art, renders, illustrations
High Fidelity V2standardHigh-quality sources needing upscale
RedefinegenerativeCreative reinterpretation
Recover 3generativeLow-res images, old photos
Standard MAXgenerativeMaximum quality, slower
Wonder 2generativeNature photography, portraits, fine detail

Environment Variables

VariableRequiredDescription
TOPAZ_API_KEYYesTopaz Labs API key

Architecture

src/
  api/        -- Topaz API client, types, model definitions
  lib/        -- Path security, URL fetching utilities
  tools/      -- enhance tool handler
  server.ts   -- MCP server setup and tool registration
  main.ts     -- Entry point (stdio transport)
test/         -- Vitest test suite

Development

npm install
npm run build
npm test

Limitations

  • Image only -- video enhancement is not yet supported
  • Paid API key required -- sign up at developer.topazlabs.com
  • Cloud processing -- images are sent to the Topaz Labs API for enhancement

Contributing

Found a bug or have a feature request? Open an issue on GitHub.

License

MIT -- Topaz Labs

Frequently asked questions

What is Topaz MCP?

AI image enhancement (upscaling, denoising, sharpening) via the Topaz Labs API, with models such as Standard V2, Wonder 2, Bloom and Recover 3.

How do I install Topaz MCP?

Follow the installation steps in its documentation, shown on this page and in its repository.

Is Topaz MCP open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains Topaz MCP?

It is published by TopazLabs. AI.tn lists it but does not maintain or endorse it.

From the community awesome-mcp-servers lists and the project's GitHub repository. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.