🪄 ImageSorcery MCP

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

Official website: imagesorcery.net

✅ 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:

"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:

😉 Hint:

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

Related MCP server: AI Development Assistant MCP Server

🛠️ Available Tools

😉 Hint:

📚 Available Resources

😉 Hint:

💬 Available Prompts

😉 Hint:

🚀 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

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:

For Docker containers: Add this line to your Dockerfile:

Installation

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):

   # 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:

   pipx install imagesorcery-mcp

2. Run the post-installation script: This step is crucial. It downloads the required models and attempts to install the clip Python package from GitHub.

   imagesorcery-mcp --post-install

Method 2: Manual Virtual Environment (Plan B)

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 .

1. Create and activate a virtual environment:

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

2. Install the package into the activated virtual environment: You can use pip or uv pip.

   pip install imagesorcery-mcp # OR, if you prefer using uv for installation into the venv: # uv pip install imagesorcery-mcp

3. 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.

   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

• Creates a 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 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.

• Using 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

  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:

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

  4. 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 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):

For manual venv installation:

For pipx installation (recommended):

For manual venv installation:

📦 Additional Models

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

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

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

   imagesorcery-mcp

2. Streamable HTTP Mode - For web-based deployments:

   imagesorcery-mcp --transport=streamable-http

   With custom host, port, and path:

   imagesorcery-mcp --transport=streamable-http --host=0.0.0.0 --port=4200 --path=/custom-path

Available transport options:

• --transport: Choose between "stdio" (default), "streamable-http", or "sse"

• --host: Specify host for HTTP-based transports (default: 127.0.0.1)

• --port: Specify port for HTTP-based transports (default: 8000)

• --path: Specify endpoint path for HTTP-based transports (default: /mcp)

🔒 Privacy & Telemetry

We are committed to your privacy. ImageSorcery MCP is designed to run locally, ensuring your images and data stay on your machine.

To help us understand which features are most popular and fix bugs faster, we've included optional, anonymous telemetry.

• It is disabled by default. You must explicitly opt-in to enable it.

• What we collect: Anonymized usage data, including features used (e.g., crop, detect), application version, operating system type (e.g., 'linux', 'win32'), and tool failures.

• What we NEVER collect: We do not collect any personal or sensitive information. This includes image data, file paths, IP addresses, or any other personally identifiable information.

• How to enable/disable: You can control telemetry by setting enabled = true or enabled = false in the [telemetry] section of your config.toml file.

⚙️ Configuring the Server

The server can be configured using a config.toml file in the current directory. The file is created automatically during installation with default values. You can customize the default tool parameters in this file. More in CONFIG.md.

🤝 Contributing

Directory Structure

This repository is organized as follows:

Development Setup

1. Clone the repository:

2. (Recommended) Create and activate a virtual environment:

3. Install the package in editable mode along with development dependencies:

This will install imagesorcery-mcp and all dependencies from [project.dependencies] and [project.optional-dependencies].dev (including build and twine).

Rules

These rules apply to all contributors: humans and AI.

1. Read all the README.md files in the project. Understand the project structure and purpose. Understand the guidelines for contributing. Think through how it relates to your task, and how to make changes accordingly.

2. Read pyproject.toml. Pay attention to sections: [tool.ruff], [tool.ruff.lint], [project.optional-dependencies] and [project]dependencies. Strictly follow code style defined in pyproject.toml. Stick to the stack defined in pyproject.toml dependencies and do not add any new dependencies without a good reason.

3. Write your code in new and existing files. If new dependencies are needed, update pyproject.toml and install them via pip install -e . or pip install -e ".[dev]". Do not install them directly via pip install. Check out existing source codes for examples (e.g. src/imagesorcery_mcp/server.py, src/imagesorcery_mcp/tools/crop.py). Stick to the code style, naming conventions, input and output data formats, code structure, architecture, etc. of the existing code.

4. Update related README.md files with your changes. Stick to the format and structure of the existing README.md files.

5. Write tests for your code. Check out existing tests for examples (e.g. tests/test_server.py, tests/tools/test_crop.py). Stick to the code style, naming conventions, input and output data formats, code structure, architecture, etc. of the existing tests.

6. Run tests and linter to ensure everything works:

In case of failures - fix the code and tests. It is strictly required to have all new code to comply with the linter rules and pass all tests.

Coding hints

• Use type hints where appropriate

• Use pydantic for data validation and serialization

📝 Questions?

If you have any questions, issues, or suggestions regarding this project, feel free to reach out to:

• Project Author: titulus via LinkedIn

• Sunrise Apps CEO: Vlad Karm via LinkedIn

You can also open an issue in the repository for bug reports or feature requests.

📜 License

This project is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License.