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ImageSorcery
- python
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python
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6 months ago
First Indexed
2 months ago
Catalog Refreshed
Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
ImageSorcery MCP enables your AI assistant to perform advanced local image processing tasks using a collection of tools powered by computer vision models. Run everything locally to crop, resize, annotate, detect objects, extract text, and more without sending images to external servers.
How to use
Connect an MCP client to the ImageSorcery MCP server and start issuing image-processing commands. You can combine multiple tools to accomplish complex tasks such as detecting objects in an image, cropping regions, drawing annotations, removing backgrounds, or applying watermarks. Ensure you reference the local server endpoint so your client can relay commands and receive results.
How to install
Prerequisites: you need Python 3.10 or higher, and either pipx or a virtual environment setup with Python. You also need system libraries required by OpenCV (ffmpeg, libsm6, libxext6, libgl1-mesa-glx). If you plan to run the server locally, you should have a MCP client ready (e.g., Claude.app, Cline, or another MCP client).
Option 1 — Using pipx (recommended)
pipx install imagesorcery-mcp
imagesorcery-mcp --post-install
Option 2 — Manual virtual environment (Plan B)
python -m venv imagesorcery-mcp
source imagesorcery-mcp/bin/activate # Linux/macOS
# Windows: imagesorcery-mcp\Scripts\activate
pip install imagesorcery-mcp
imagesorcery-mcp --post-install
Tip: If you use a persistent environment, you will reference the executable path in your MCP client configuration, for example "/full/path/to/venv/bin/imagesorcery-mcp". For Windows, use the corresponding Scripts path.
Running the server
You can run ImageSorcery MCP in different modes. The standard local mode is STDIO and uses the default command: imagesorcery-mcp.
imagesorcery-mcp
If you prefer web-based deployments, you can use Streamable HTTP mode with a configurable host, port, and path, or use HTTP mode with an explicit endpoint. The HTTP mode endpoint example is shown below. Use the default STDIO mode for local development and testing.
imagesorcery-mcp --transport=streamable-http
# Or with custom host/port/path
imagesorcery-mcp --transport=streamable-http --host=0.0.0.0 --port=4200 --path=/mcp
# HTTP mode example endpoint (client connects here)
http://127.0.0.1:8000/mcp
Available tools
blur
Blurs specified areas of an image using OpenCV; supports rectangular or polygonal regions and can blur the background by inverting the area.
change_color
Changes the color palette of an image, enabling effects like sepia or grayscale.
config
View or update MCP configuration settings at runtime.
crop
Crop an image using OpenCV numpy slicing based on coordinates.
detect
Detect objects in an image using Ultralytics models; supports return of segmentation masks or polygons with a confidence threshold.
draw_arrows
Draw arrows on an image to indicate directions or highlights.
draw_circles
Draw circles on an image at specified centers and radii.
draw_lines
Draw lines on an image between two points with color and thickness.
draw_rectangles
Draw one or more rectangles on an image, with options for filled or outlined shapes.
draw_texts
Overlay text onto an image at specified positions.
fill
Fill areas of an image with a color or transparency; can also invert areas or remove backgrounds.
find
Find objects in an image based on a text description; can return segmentation masks or polygons.
get_metainfo
Retrieve metadata information for an image file.
ocr
Perform Optical Character Recognition on images to extract text using OCR models.
overlay
Overlay one image onto another with proper handling of transparency.
resize
Resize an image to specified dimensions.
rotate
Rotate an image by a given angle while preserving content.