内容
# 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
<a href="https://glama.ai/mcp/servers/@ifmelate/mcp-image-extractor">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@ifmelate/mcp-image-extractor/badge" alt="Image Extractor MCP server" />
</a>
How it looks in Cursor:
<img width="687" alt="image" src="https://github.com/user-attachments/assets/8954dbbd-7e7a-4f27-82a7-b251bd3c5af2" />
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:
```json
{
"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:
```json
{
"mcpServers": {
"image-extractor": {
"command": "node",
"args": ["/full/path/to/mcp-image-extractor/dist/index.js"],
"disabled": false
}
}
}
```
### Manual Installation
```bash
# Clone and install
git clone https://github.com/ifmelate/mcp-image-extractor.git
cd mcp-image-extractor
npm install
npm run build
npm link
```
This will make the `mcp-image-extractor` command available globally.
Then configure in `.cursor/mcp.json`:
```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.
## 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 data
- `mime_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.jpg
```
Claude 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.jpg
```
Claude will automatically use the `extract_image_from_url` tool to fetch and analyze the image content.
## Docker
Build and run with Docker:
```bash
docker build -t mcp-image-extractor .
docker run -p 8000:8000 mcp-image-extractor
```
## License
MIT
MCP 市场