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Artin0123-gemini-image-mcp-server

Artin0123-gemini-image-mcp-server

Remote
github.com/Artin0123Media & Design2Updated 55w ago
Repository

About Artin0123-gemini-image-mcp-server

Artin0123-gemini-image-mcp-server is a remote visual analysis server for MCP clients, using Gemini to process image URLs and YouTube videos. Images are limited to 16 MB each, while videos are streamed through the Gemini API.

Key features

  1. 01Analyze one or more image URLs
  2. 02Analyze YouTube videos
  3. 03Configure Gemini API key and model
  4. 0416 MB limit per image

Use cases

  1. 01Extract visual details from product images
  2. 02Summarize YouTube video content

MCP support

The MCP server provides image and YouTube video analysis tools to clients. This remote version handles URL content; local images and videos require the local version.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://server.smithery.ai/@Artin0123/gemini-image-mcp-server/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://server.smithery.ai/@Artin0123/gemini-image-mcp-server/mcpAuth required

Tools

Details

From the project README

image-mcp-server-gemini

smithery badge

This is remote server, use local version for local images and videos.

Features

  • Analyze one or more image URLs with a single tool call.
  • Analyze YouTube videos without downloading files locally.
  • Supply an API key and optionally override the Gemini model via environment variables.
  • File size limit: Images are limited to 16 MB to ensure fast processing.
  • YouTube videos: No size limit as they are streamed directly by Gemini API.

Installation

Installing via Smithery

Install the server in Claude Desktop:

npx -y @smithery/cli install @Artin0123/gemini-image-mcp-server --client claude
Manual Installation
# Clone the repository
git clone https://github.com/Artin0123/gemini-vision-mcp.git
cd gemini-vision-mcp

# Install dependencies
npm install

# Compile TypeScript to dist/
npm run build

Configuration

Create a Gemini API key in Google AI Studio and provide GEMINI_API_KEY to the server.

{
  "mcpServers": {
    "gemini-media": {
      "command": "node",
      "args": ["/absolute/path/to/gemini-vision-mcp/dist/index.js"],
      "env": {
        "GEMINI_API_KEY": "your_api_key_here",
        "GEMINI_MODEL": "models/gemini-flash-lite-latest"
      }
    }
  }
}

If no key is supplied, the server can still start (handy for automated scans), but any tool invocation will return a configuration error until a valid API key is configured.

Model override

The server defaults to models/gemini-flash-lite-latest. Override it by either:

Setting the GEMINI_MODEL environment variable, or Providing modelName in the Smithery/SDK configuration schema.

Available tools

  • analyze_image: Analyze one or more image URLs. Maximum file size: 16 MB per image.
  • analyze_youtube_video: Analyze a YouTube video from URL. No size limit.

Image URLs are downloaded and processed with a 16 MB size limit to ensure fast response times. Files exceeding this limit will result in an error message indicating the actual file size.

YouTube videos are streamed directly by Gemini API without downloading, so there is no size restriction.

Prompt examples
Please analyze this product photo: https://teimg-bgr.pages.dev/file/mvYT6KeF.webp
Extract the main talking points from this clip: https://www.youtube.com/watch?v=dQw4w9WgXcQ

Development

npm install
npm test
npm run build

The test suite exercises URL forwarding, MIME handling, and configuration fallbacks.

License

MIT

Comments

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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.