What does the `gemini_remove_watermark` tool do?
It removes the Gemini AI watermark from a given image file and saves the cleaned result to a specified output path.
@Yezery
Basic information
MCP server for removing Gemini watermarks
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"gemini-watermark-remove": {
"command": "npx",
"args": [
"-y",
"gemini-watermark-remover-mcp"
]
}
}
}Removes the Gemini AI watermark from generated images. Args: - input_path: Path to the input image file (PNG, JPG, WEBP) - output_path: Path where the processed image will be saved (PNG, JPG, WEBP) - response_format: 'markdown' or 'json' (default: 'markdown') - jpeg_quality: JPEG output quality 1-100 (default: 95) - webp_quality: WEBP output quality 1-100 (default: 80) - png_compression_level: PNG compression level 0-9 (default: 6) Output formats: - PNG: Configurable compression (0-9) - JPG: Configurable quality (1-100) - WEBP: Configurable quality (1-100)
Gemini Watermark Remover is an MCP server that removes Gemini AI watermarks from generated images. It provides a single tool, gemini_remove_watermark, for use in MCP-compatible clients like Claude Desktop and OpenCode.
Install and run via npx (npx -y gemini-watermark-remover-mcp) or build from source with Node.js >=22 and npm. Configure the gemini_remove_watermark tool by providing required parameters input_path and output_path, with optional settings for output format, JPEG/WebP quality, and PNG compression level.
It removes the Gemini AI watermark from a given image file and saves the cleaned result to a specified output path.
Node.js version 22 or higher and npm are required. The server runs locally with no external dependencies.
Add a JSON entry to `mcpServers` with command `npx` and args `["-y", "gemini-watermark-remover-mcp"]`. For local development, use `node` with the path to the built `dist/index.js`.
Input formats: PNG, JPG, WEBP. Output format matches input by default, with adjustable quality/compression settings.
The server runs locally on the user’s machine via stdio transport. No authentication or API keys are needed.

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