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by sunriseapps · MCP Server · ★ 327
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🪄 ImageSorcery MCP ComputerVision-based 🪄 sorcery of local image recognition and editing tools for AI assistants Official website: imagesorcery.net ✅ With ImageSorcery MCP 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 ✅ D
| Stars | 327 |
| Forks | 50 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.9932077275426/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-19 |
| Created | 2025-05-12 |
| Platforms | mcp, python |
| Est. Tokens | ~110k |
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imagesorcery-mcp is An MCP server providing tools for image processing operations. It is categorized as a MCP Server with 327 GitHub stars.
imagesorcery-mcp is primarily written in Python. It covers topics such as computer-vision, image-editing, image-manipulation.
You can find installation instructions and usage details in the imagesorcery-mcp GitHub repository at github.com/sunriseapps/imagesorcery-mcp. The project has 327 stars and 50 forks, indicating an active community.
imagesorcery-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to imagesorcery-mcp on Agent Skills Hub include MakeMoneyWithAI, Construction-Hazard-Detection, OpenImageDebugger. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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