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Imagesorcery MCP

by sunriseapps

Local image recognition and editing through MCP, including OCR, object detection, drawing, transformations, metadata, and background removal.

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Install Imagesorcery MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "sunriseapps-imagesorcery-mcp": {
      "command": "uvx",
      "args": [
        "imagesorcery-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx imagesorcery-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 25d ago.

Overview

sunriseapps/imagesorcery-mcp MCP server gives AI assistants local tools for image recognition and editing. It uses OpenCV, EasyOCR, and Ultralytics models to transform images, extract text, inspect metadata, detect objects, and find objects from text descriptions. Processing runs locally, so image files do not need to be sent to a remote service. Reach for it when an MCP client needs file-based image operations, OCR, or computer-vision workflows.

Use cases

  • Crop photos around detected objects
  • Extract text from scanned images with OCR
  • Remove image backgrounds with detection and masking
  • Add annotations, logos, or watermarks
  • Detect objects and return masks or polygons

Key features

  • Local OpenCV image transformations
  • Ultralytics object detection
  • Text-based object finding
  • EasyOCR text extraction
  • Image metadata inspection
  • Configurable detection settings

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

19/20 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10d ago via OSV.dev · imagesorcery-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars334Stargazers on the source repository.
Last commit5mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity6 viewsConfig copies, upvotes, and views on AllMCPs.

How Imagesorcery MCP works

What sunriseapps/imagesorcery-mcp MCP server does

sunriseapps/imagesorcery-mcp MCP server connects an MCP-compatible AI assistant to local image-processing operations. It can crop, resize, rotate, blur, recolor, overlay, and fill image regions. Drawing tools add text, arrows, circles, lines, and rectangles, which is useful for annotations, redactions, diagrams, and watermarks.

Recognition features include object detection with Ultralytics models, text-based object finding, and OCR through EasyOCR. Detection can produce polygons or segmentation masks as PNG files. The server can also return image metadata and expose the models available in its models directory. A supplied remove-background prompt describes a workflow that combines detection and masking to isolate a subject.

All processing is described as local. That makes the server suitable for workflows where source images should remain on the machine running the MCP process.

How it works

The assistant calls individual MCP tools or combines several tools to complete a larger task. For example, a workflow can find an object in a photo, crop around it, and save the result. Detection accepts a confidence threshold, while OCR accepts language information. Operations use local file paths; the project specifically recommends using full paths in prompts.

The server also provides the models://list resource, which reports the models in the local models directory. The config tool can show or update ImageSorcery MCP settings, including the default detection confidence mentioned in the examples. The remove-background prompt provides structured guidance for a multi-step background-removal task.

Setup and configuration

sunriseapps/imagesorcery-mcp MCP server requires Python 3.10 or newer and an MCP client such as Claude.app, Cursor, or Cline. The documented installation path uses pipx:

bash
pipx install imagesorcery-mcp
imagesorcery-mcp --post-install

The post-install command downloads required models and attempts to install the clip Python package from GitHub. OpenCV may also require ffmpeg, libsm6, libxext6, and libgl1-mesa-glx; the README provides Ubuntu/Debian and Docker installation commands for those libraries.

For an MCP configuration, the documented stdio entry uses the imagesorcery-mcp command and a timeout of 100. Configuration can be inspected or changed through the config tool. The README’s Cline instructions also show enabling telemetry through that tool only after requesting user permission.

Tools and capabilities

The available tools cover:

  • Image changes: crop, resize, rotate, blur, change_color, fill, and overlay.
  • Annotations: draw_texts, draw_arrows, draw_circles, draw_lines, and draw_rectangles.
  • Recognition: detect, find, and ocr.
  • Inspection and settings: get_metainfo and config.

detect uses Ultralytics models, while find accepts a text description such as an object category. Both can return polygons or segmentation masks. ocr uses EasyOCR to extract text from images. The tools generally accept an input image and output path, with operation-specific parameters such as coordinates, colors, opacity, rotation angle, language, or confidence.

Limitations and notes

Results depend on the selected recognition model and confidence setting. Required system libraries can be missing in minimal environments such as Docker containers. Detailed parameter references are maintained in the repository’s tool, resource, and prompt README files.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Imagesorcery MCP

sunriseapps/imagesorcery-mcp is an MCP server that connects AI assistants to local image files and computer-vision tools. Its main tools crop, resize, rotate, detect, find, OCR, draw, overlay, fill, blur, inspect metadata, and manage configuration. It also provides a model-list resource and a background-removal workflow prompt.

Install it with pipx, then run its post-install command: `pipx install imagesorcery-mcp` followed by `imagesorcery-mcp --post-install`. The post-install step downloads required models. You may also need OpenCV system libraries, including ffmpeg, libsm6, libxext6, and libgl1-mesa-glx.

Yes, Cursor is listed as a supported client. Configure the MCP server using the `imagesorcery-mcp` command in the client’s MCP configuration, then use local image paths when requesting operations.

Yes, it includes a `remove-background` prompt that guides detection and masking steps to isolate a subject. The underlying tools can detect objects and fill or make selected regions transparent.

Yes, the `ocr` tool extracts text with EasyOCR. Prompts can specify an image and an OCR language, such as English.

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