MCP Image Extractor
The MCP Image Extractor server extracts and converts images to base64 for LLM analysis.
Extract images from various sources:
Local files (PNG, JPG, GIF, WebP) using
extract_image_from_filetoolURLs using
extract_image_from_urltoolProcess existing base64-encoded images using
extract_image_from_base64tool
Automatic optimization: Resizes images to maximum 512x512 pixels to optimize context window usage
Integration features: Compatible with Cursor for automated image extraction and supports Docker for containerized deployment
Backward compatibility: Supports legacy parameters for existing implementations
Supports containerized deployment of the MCP image extraction service through Docker, allowing for isolated and portable execution environments.
Provides installation directly from GitHub repository, with specific instructions for cloning and building from the GitHub source.
Enables installation and management through npm, with support for npm link to make the command globally available.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Image Extractor extract the image from https://example.com/screenshot.png"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Image Extractor
MCP server for extracting and converting images to base64 for LLM analysis.
This MCP server provides tools for AI assistants to:
Extract images from local files
Extract images from URLs
Process base64-encoded images
How it looks in Cursor:
Suitable cases:
analyze playwright test results: screenshots
Installation
Recommended: Using npx in mcp.json (Easiest)
The recommended way to install this MCP server is using npx directly in your .cursor/mcp.json file:
{
"mcpServers": {
"image-extractor": {
"command": "npx",
"args": [
"-y",
"mcp-image-extractor"
]
}
}
}This approach:
Automatically installs the latest version
Does not require global installation
Works reliably across different environments
Alternative: Local Path Installation
If you prefer to use a local installation of the package, you can clone the repository and point to the built files:
{
"mcpServers": {
"image-extractor": {
"command": "node",
"args": ["/full/path/to/mcp-image-extractor/dist/index.js"],
"disabled": false
}
}
}Manual Installation
# Clone and install
git clone https://github.com/ifmelate/mcp-image-extractor.git
cd mcp-image-extractor
npm install
npm run build
npm linkThis will make the mcp-image-extractor command available globally.
Then configure in .cursor/mcp.json:
{
"mcpServers": {
"image-extractor": {
"command": "mcp-image-extractor",
"disabled": false
}
}
}Troubleshooting for Cursor Users: If you see "Failed to create client" error, try the local path installation method above or ensure you're using the correct path to the executable.
Related MCP server: MCP URL Fetcher
Available Tools
extract_image_from_file
Extracts an image from a local file and converts it to base64.
Parameters:
file_path(required): Path to the local image file
Note: All images are automatically resized to optimal dimensions (max 512x512) for LLM analysis to limit the size of the base64 output and optimize context window usage.
extract_image_from_url
Extracts an image from a URL and converts it to base64.
Parameters:
url(required): URL of the image to extract
Note: All images are automatically resized to optimal dimensions (max 512x512) for LLM analysis to limit the size of the base64 output and optimize context window usage.
extract_image_from_base64
Processes a base64-encoded image for LLM analysis.
Parameters:
base64(required): Base64-encoded image datamime_type(optional, default: "image/png"): MIME type of the image
Note: All images are automatically resized to optimal dimensions (max 512x512) for LLM analysis to limit the size of the base64 output and optimize context window usage.
Example Usage
Here's an example of how to use the tools from Claude:
Please extract the image from this local file: images/photo.jpgClaude will automatically use the extract_image_from_file tool to load and analyze the image content.
Please extract the image from this URL: https://example.com/image.jpgClaude will automatically use the extract_image_from_url tool to fetch and analyze the image content.
Docker
Build and run with Docker:
docker build -t mcp-image-extractor .
docker run -p 8000:8000 mcp-image-extractorLicense
MIT
Available Tools
3 toolsextract_image_from_base64A
Extract and analyze images from base64-encoded data. Ideal for processing screenshots from clipboard, dynamically generated images, or images embedded in applications without requiring file system access.
Inputs: base64 (required), max_height, max_width, mime_type, +1 more.
extract_image_from_fileA
Extract and analyze images from local file paths. Supports visual content understanding, OCR text extraction, and object recognition for screenshots, photos, diagrams, and documents.
Inputs: file_path (required), max_height, max_width, resize.
extract_image_from_urlA
Extract and analyze images from web URLs. Perfect for analyzing web screenshots, online photos, diagrams, or any image accessible via HTTP/HTTPS for visual content analysis and text extraction.
Inputs: url (required), max_height, max_width, resize.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose based on the source of the image: base64-encoded data, local file paths, and web URLs. The descriptions reinforce this by specifying different use cases (e.g., clipboard screenshots, local files, online images), leaving no ambiguity for an agent to misselect.
All tool names follow a consistent verb_noun pattern with 'extract_image_from_' as a prefix, followed by the source type (base64, file, url). This predictable naming scheme makes it easy for agents to understand and navigate the tool set without confusion.
With 3 tools, the server is well-scoped for its purpose of extracting images from different sources. Each tool earns its place by covering a distinct input method (base64, file, URL), providing a complete set for the domain without being overly sparse or bloated.
The tool surface is complete for the domain of image extraction, covering all major input sources: base64 data, local files, and web URLs. There are no obvious gaps, as these three methods encompass the typical ways images are accessed in applications, ensuring agents can handle various scenarios without dead ends.
Maintenance
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