Imagesorcery Mcp

🪄 ImageSorcery MCP

ComputerVision-based 🪄 sorcery of local image recognition and editing tools for AI assistants

Official website: imagesorcery.net

[](https://opensource.org/licenses/MIT) [](https://github.com/microsoft/mcp) [](https://claude.ai) [](https://cursor.so) [](https://github.com/ClineLabs/cline) [](https://mseep.ai/app/2620351a-15b1-4840-a93a-cbdbd23a6944) [](https://pepy.tech/projects/imagesorcery-mcp)

✅ With ImageSorcery MCP

🪄 ImageSorcery empowers AI assistants with powerful image processing capabilities:

• ✅ Crop, resize, and rotate images with precision
• ✅ Remove background
• ✅ Draw text and shapes on images
• ✅ Add logos and watermarks
• ✅ Detect objects using state-of-the-art models
• ✅ Extract text from images with OCR
• ✅ Use a wide range of pre-trained models for object detection, OCR, and more
• ✅ Do all of this locally, without sending your images to any servers

Just ask your AI to help with image tasks:

> "copy photos with pets from folder photos to folder pets"

> "Find a cat at the photo.jpg and crop the image in a half in height and width to make the cat be centered"

😉 Hint: Use full path to your files".

> "Enumerate form fields on this form.jpg with foduucom/web-form-ui-field-detection model and fill the form.md with a list of described fields"

😉 Hint: Specify the model and the confidence".

😉 Hint: Add "use imagesorcery" to make sure it will use the proper tool".

Your tool will combine multiple tools listed below to achieve your goal.

🛠️ Available Tools

| Tool | Description | Example Prompt | |------|-------------|----------------| | blur | Blurs specified rectangular or polygonal areas of an image using OpenCV. Can also invert the provided areas e.g. to blur background. | "Blur the area from (150, 100) to (250, 200) with a blur strength of 21 in my image 'testimage.png' and save it as 'output.png'" | | change_color | Changes the color palette of an image | "Convert my image 'testimage.png' to sepia and save it as 'output.png'" | | config | View and update ImageSorcery MCP configuration settings | "Show me the current configuration" or "Set the default detection confidence to 0.8" | | crop | Crops an image using OpenCV's NumPy slicing approach | "Crop my image 'input.png' from coordinates (10,10) to (200,200) and save it as 'cropped.png'" | | detect | Detects objects in an image using models from Ultralytics. Can return segmentation masks (as PNG files) or polygons. | "Detect objects in my image 'photo.jpg' with a confidence threshold of 0.4" | | draw_arrows | Draws arrows on an image using OpenCV | "Draw a red arrow from (50,50) to (150,100) on my image 'photo.jpg'" | | draw_circles | Draws circles on an image using OpenCV | "Draw a red circle with center (100,100) and radius 50 on my image 'photo.jpg'" | | draw_lines | Draws lines on an image using OpenCV | "Draw a red line from (50,50) to (150,100) on my image 'photo.jpg'" | | draw_rectangles | Draws rectangles on an image using OpenCV | "Draw a red rectangle from (50,50) to (150,100) and a filled blue rectangle from (200,150) to (300,250) on my image 'photo.jpg'" | | draw_texts | Draws text on an image using OpenCV | "Add text 'Hello World' at position (50,50) and 'Copyright 2023' at the bottom right corner of my image 'photo.jpg'" | | fill | Fills specified rectangular, polygonal, or mask-based areas of an image with a color and opacity, or makes them transparent. Can also invert the provided areas e.g. to remove background. | "Fill the area from (150, 100) to (250, 200) with semi-transparent red in my image 'testimage.png'" | | find | Finds objects in an image based on a text description. Can return segmentation masks (as PNG files) or polygons. | "Find all dogs in my image 'photo.jpg' with a confidence threshold of 0.4" | | get_metainfo | Gets metadata information about an image file | "Get metadata information about my image 'photo.jpg'" | | ocr | Performs Optical Character Recognition (OCR) on an image using EasyOCR | "Extract text from my image 'document.jpg' using OCR with English language" | | overlay | Overlays one image on top of another, handling transparency | "Overlay 'logo.png' on top of 'background.jpg' at position (10, 10)" | | resize | Resizes an image using OpenCV | "Resize my image 'photo.jpg' to 800x600 pixels and save it as 'resizedphoto.jpg'" | | rotate | Rotates an image using imutils.rotatebound function | "Rotate my image 'photo.jpg' by 45 degrees and save it as 'rotatedphoto.jpg'" |

😉 Hint: detailed information and usage instructions for each tool can be found in the tool's /src/imagesorcery_mcp/tools/README.md.

📚 Available Resources

| Resource URI | Description | Example Prompt | |--------------|-------------|----------------| | models://list | Lists all available models in the models directory | "Which models are available in ImageSorcery?" |

😉 Hint: detailed information and usage instructions for each resource can be found in the resource's /src/imagesorcery_mcp/resources/README.md.

💬 Available Prompts

| Prompt Name | Description | Example Usage | |-------------|-------------|---------------| | remove-background | Guides the AI through a comprehensive background removal workflow using object detection and masking tools | "Use the remove-background prompt to remove the background from my photo 'portrait.jpg', keeping only the person" |

😉 Hint: detailed information and usage instructions for each prompt can be found in the prompt's /src/imagesorcery_mcp/prompts/README.md.

🚀 Getting Started

Requirements

• Python 3.10 or higher
• pipx (recommended) - for easy installation and virtual environment management
• ffmpeg, libsm6, libxext6, libgl1-mesa-glx - system libraries required by OpenCV
• Claude.app, Cline, or another MCP client

Virtual environments (e.g. Docker)

These dependencies are typically included with OpenCV installation and don't require separate installation. But they might be missing in some virtual environments like Docker.

For Ubuntu/Debian systems:

sudo apt-get update && sudo apt-get install -y ffmpeg libsm6 libxext6 libgl1-mesa-glx

For Docker containers: Add this line to your Dockerfile:

RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 libgl1-mesa-glx

Installation

For Cline just copy-paste LLM-INSTALL.md

1.  Load MCP documentation if you have the tool for it. It will give you path to *MCP-configuration file* and other needed instructions.
2.  Run command `pipx install imagesorcery-mcp`
3.  Run command `imagesorcery-mcp --post-install`
4.  Create or modify *MCP-configuration file*.
5.  Add the `imagesorcery-mcp` server configuration to the `mcpServers` object in the *MCP-configuration file*
    `"imagesorcery-mcp": {"command": "imagesorcery-mcp","timeout": 100}`
6.  Prompt the user for permission and enable telemetry by calling `config(action="set", key="telemetry.enabled", value=True, persist=True)`.
7.  Attempt completion, indicating that the installation and configuration are complete.

Method 1: Using pipx (Recommended)

pipx is the recommended way to install ImageSorcery MCP as it automatically handles virtual environment creation and management, making the installation process much simpler.

1. Install pipx (if not already installed):

1. Install pipx (if not already installed):

```bash # On macOS with Homebrew: brew install pipx

# On Ubuntu/Debian: sudo apt update && sudo apt install pipx

# On other systems with pip: pip install --user pipx pipx ensurepath ```

1. Install ImageSorcery MCP with pipx:

``bash pipx install imagesorcery-mcp ``

1. Run the post-installation script:

This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub. ``bash imagesorcery-mcp --post-install ``

Method 2: Manual Virtual Environment (Plan B)

If pipx doesn't work for your system, you can manually create a virtual environment

For reliable installation of all components, especially the clip package (installed via the post-install script), it is strongly recommended to use Python's built-in venv module instead of uv venv.

1. Create and activate a virtual environment:

``bash python -m venv imagesorcery-mcp source imagesorcery-mcp/bin/activate # For Linux/macOS # source imagesorcery-mcp\Scripts\activate # For Windows ``

1. Install the package into the activated virtual environment:

You can use pip or uv pip. ``bash pip install imagesorcery-mcp # OR, if you prefer using uv for installation into the venv: # uv pip install imagesorcery-mcp ``

1. Run the post-installation script:

This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub into the active virtual environment. ``bash imagesorcery-mcp --post-install ``

Note: When using this method, you'll need to provide the full path to the executable in your MCP client configuration (e.g., /full/path/to/venv/bin/imagesorcery-mcp).

Additional Notes

What does the post-installation script do? The imagesorcery-mcp --post-install script performs the following actions:

• Creates a config.toml configuration file in the current directory, allowing users to customize default tool parameters.
• Creates a models directory (usually within the site-packages directory of your virtual environment, or a user-specific location if installed globally) to store pre-trained models.
• Generates an initial models/model_descriptions.json file there.
• Downloads default YOLO models (yoloe-11l-seg-pf.pt, yoloe-11s-seg-pf.pt, yoloe-11l-seg.pt, yoloe-11s-seg.pt) required by the detect tool into this models directory.
• Attempts to install the clip Python package from Ultralytics' GitHub repository directly into the active Python environment. This is required for text prompt functionality in the find tool.
• Downloads the CLIP model file required by the find tool into the models directory.

You can run this process anytime to restore the default models and attempt clip installation.

Important Notes for uv users (uv venv and uvx)

• Using uv venv to create virtual environments:

Based on testing, virtual environments created with uv venv may not include pip in a way that allows the imagesorcery-mcp --post-install script to automatically install the clip package from GitHub (it might result in a "No module named pip" error during the clip installation step). If you choose to use uv venv:

1. Create and activate your uv venv.
2. Install imagesorcery-mcp: uv pip install imagesorcery-mcp.
3. Manually install the clip package into your active uv venv:

``bash uv pip install git+https://github.com/ultralytics/CLIP.git ``

1. Run imagesorcery-mcp --post-install. This will download models but may fail to install the clip Python package.

For a smoother automated clip installation via the post-install script, using python -m venv (as described in step 1 above) is the recommended method for creating the virtual environment.

• Using uvx imagesorcery-mcp --post-install:

Running the post-installation script directly with uvx (e.g., uvx imagesorcery-mcp --post-install) will likely fail to install the clip Python package. This is because the temporary environment created by uvx typically does not have pip available in a way the script can use. Models will be downloaded, but the clip package won't be installed by this command. If you intend to use uvx to run the main imagesorcery-mcp server and require clip functionality, you'll need to ensure the clip package is installed in an accessible Python environment that uvx can find, or consider installing imagesorcery-mcp into a persistent environment created with python -m venv.

⚙️ Configure MCP client

Add to your MCP client these settings.

For pipx installation (recommended):

"mcpServers": {
    "imagesorcery-mcp": {
      "command": "imagesorcery-mcp",
      "transportType": "stdio",
      "autoApprove": ["blur", "change_color", "config", "crop", "detect", "draw_arrows", "draw_circles", "draw_lines", "draw_rectangles", "draw_texts", "fill", "find", "get_metainfo", "ocr", "overlay", "resize", "rotate"],
      "timeout": 100
    }
}

For manual venv installation:

"mcpServers": {
    "imagesorcery-mcp": {
      "command": "/full/path/to/venv/bin/imagesorcery-mcp",
      "transportType": "stdio",
      "autoApprove": ["blur", "change_color", "config", "crop", "detect", "draw_arrows", "draw_circles", "draw_lines", "draw_rectangles", "draw_texts", "fill", "find", "get_metainfo", "ocr", "overlay", "resize", "rotate"],
      "timeout": 100
    }
}

If you're using the server in HTTP mode, configure your client to connect to the HTTP endpoint:

"mcpServers": {
    "imagesorcery-mcp": {
      "url": "http://127.0.0.1:8000/mcp", // Use your custom host, port, and path if specified
      "transportType": "http",
      "autoApprove": ["blur", "change_color", "config", "crop", "detect", "draw_arrows", "draw_circles", "draw_lines", "draw_rectangles", "draw_texts", "fill", "find", "get_metainfo", "ocr", "overlay", "resize", "rotate"],
      "timeout": 100
    }
}

For Windows

For pipx installation (recommended):

"mcpServers": {
    "imagesorcery-mcp": {
      "command": "imagesorcery-mcp.exe",
      "transportType": "stdio",
      "autoApprove": ["blur", "change_color", "config", "crop", "detect", "draw_arrows", "draw_circles", "draw_lines", "draw_rectangles", "draw_texts", "fill", "find", "get_metainfo", "ocr", "overlay", "resize", "rotate"],
      "timeout": 100
    }
}

For manual venv installation:

"mcpServers": {
    "imagesorcery-mcp": {
      "command": "C:\\full\\path\\to\\venv\\Scripts\\imagesorcery-mcp.exe",
      "transportType": "stdio",
      "autoApprove": ["blur", "change_color", "config", "crop", "detect", "draw_arrows", "draw_circles", "draw_lines", "draw_rectangles", "draw_texts", "fill", "find", "get_metainfo", "ocr", "overlay", "resize", "rotate"],
      "timeout": 100
    }
}

📦 Additional Models

Some tools require specific models to be available in the models directory:

# Download models for the detect tool
download-yolo-models --ultralytics yoloe-11l-seg
download-yolo-models --huggingface ultralytics/yolov8:yolov8m.pt

About Model Descriptions

When downloading models, the script automatically updates the models/model_descriptions.json file:

• For Ultralytics models: Descriptions are predefined in src/imagesorcery_mcp/scripts/create_model_descriptions.py and include detailed information about each model's purpose, size, and characteristics.

• For Hugging Face models: Descriptions are automatically extracted from the model card on Hugging Face Hub. The script attempts to use the model name from the model index or the first line of the description.

After downloading models, it's recommended to check the descriptions in models/model_descriptions.json and adjust them if needed to provide more accurate or detailed information about the models' capabilities and use cases.

Running the Server

ImageSorcery MCP server can be run in different modes:

• STDIO - default
• Streamable HTTP - for web-based deployments
• Server-Sent Events (SSE) - for web-based deployments that rely on SSE

About different modes:

1. STDIO Mode (Default) - This is the standard mode for local MCP clients:

``bash imagesorcery-mcp ``

2.

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sunriseapps
• Source: sunriseapps/imagesorcery-mcp
• License: MIT
• Homepage: https://imagesorcery.net

Install and usage instructions live in the source repository linked above.