About Muse AI Image MCP
Muse AI Image MCP is a read-only MCP server for AI clients seeking information about Muse AI Image’s image and video workflows, styles, pricing, and official links. It provides site context, not image generation or media output.
Key features
- 01Image generation styles and presets
- 02Pricing and official-link information
- 03FAQ and workflow context
- 04Read-only access; no media generation
Use cases
- 01Find a suitable image-generation style
- 02Review pricing and official resources
- 03Explain Muse AI Image workflows to an AI client
MCP support
Exposes Muse AI Image styles, pricing, FAQ, workflow information, and official links to MCP clients through tools, resources, and prompts. It does not call the generation service or return media.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMuse AI Image MCP Server
Muse AI Image: Free Online Generator for Pictures & Video
A Model Context Protocol server that exposes the canonical Muse AI Image knowledge surface — image generation workflows and styles, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.
Official website: https://museaiimage.com
🎨 About Muse AI Image
Muse AI Image is an online creative workspace for generating photoreal images and short clips with the Muse Image and Muse Video models. The public product surface advertises free credits, no waitlist, and commercial-use licensing language.
This MCP exposes the site's image and video workflow, style, pricing, and official-link context as read-only information. It does not call the generation service, consume credits, return media, or make an independent legal determination about commercial rights.
Use the live site for current model availability, credit rules, pricing, and the terms that apply to a particular output.
Tools
list_styles
Return the canonical list of image-generation styles or presets the site exposes. (Muse AI Image)
Input: no parameters. Returns: text/markdown.
get_pricing
Return the canonical pricing entry point for Muse AI Image.
Input: no parameters. Returns: text/markdown.
get_official_links
Return the canonical list of official links for Muse AI Image (website, support, docs when available).
Input: no parameters. Returns: text/markdown.
Resources
site://museaiimage/styles— Supported image-generation styles and presets.site://museaiimage/pricing— Canonical pricing entry point.site://museaiimage/faq— Short FAQ generated from public site metadata.site://museaiimage/links— Canonical URLs to share with users.
Prompts
tell_me_about_museaiimage
Summarize what the site is, who it's for, and how it works. — Muse AI Image
try_image_style_museaiimage
Recommend a starting image-generation style for a stated goal. — Muse AI Image
Installation
Install via Smithery
npx -y @smithery/cli install museaiimage-mcp --client claude
(Replace claude with cursor, windsurf, or continue for those clients.)
Install from source
git clone https://github.com/rocnubie/museaiimage-mcp.git
cd museaiimage-mcp
pnpm install
Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):
{
"mcpServers": {
"museaiimage-mcp": {
"command": "node",
"args": [
"/absolute/path/to/museaiimage-mcp/src/index.mjs"
]
}
}
}
Debug with MCP Inspector
npx @modelcontextprotocol/inspector node src/index.mjs
Official Links
- Website: https://museaiimage.com
- Pricing: https://museaiimage.com/pricing
- Support: [email protected]
Development
pnpm install
pnpm start # run the server over stdio
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.