What does MCP Image Extractor do?
It extracts images from local files, URLs, or base64 data and converts them to base64 so that an LLM (such as Claude) can analyze the visual content.
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Basic information
MCP server which allow LLM in agent mode to analyze image whenever it needs
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"image-extractor": {
"command": "node",
"args": [
"/full/path/to/mcp-image-extractor/dist/index.js"
],
"disabled": false
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCP Image Extractor is a Model Context Protocol server that extracts images from local files, URLs, or base64-encoded data and converts them to base64 for analysis by AI assistants. It is designed for developers who need to pass visual information to large language models (LLMs) within MCP-compatible tools like Cursor.
Install the server via npx in your .cursor/mcp.json configuration file (recommended), or clone the repository and build locally. Once configured, the server exposes three tools (extract_image_from_file, extract_image_from_url, extract_image_from_base64) that AI assistants can call automatically when instructed to analyze an image.
It extracts images from local files, URLs, or base64 data and converts them to base64 so that an LLM (such as Claude) can analyze the visual content.
The easiest method is to add a `npx -y mcp-image-extractor` command to your `.cursor/mcp.json` file. Alternatively, clone the repository, run `npm install && npm run build && npm link`, then reference the global command or the built file directly.
It provides three tools: `extract_image_from_file` (accepts a `file_path`), `extract_image_from_url` (accepts a `url`), and `extract_image_from_base64` (accepts `base64` data and an optional `mime_type`).
All extracted images are automatically resized to a maximum of 512×512 pixels to limit the base64 output size and optimize context‑window usage in LLM analysis.
If you see this error, try the local path installation method (pointing to the built `index.js` file) instead of the npx approach, or ensure the path to the executable is correct.
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