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Image Reader MCP

Reads local image facts, optional Tesseract OCR, citeable crops, and pixel differences through MCP.

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Install Image Reader MCP

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

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "image-reader-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@sylphx/anymd"
      ]
    }
  }
}

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

Automated check passed— started and listed 4 tools correctly (1mo ago).

Overview

Image Reader MCP MCP server reads local images and returns measurable geometry, format, metadata, hashes, and optional OCR evidence. It uses local Tesseract only when OCR is requested, rather than generating transcriptions with a vision model. Separate tools support probing files, extracting citeable regions, and comparing same-sized images for changed pixels. Reach for it when an agent needs auditable image facts, screenshot text, region references, or visual change detection.

Use cases

  • Inspect screenshot dimensions and metadata before processing
  • Extract OCR lines with bounding boxes from local images
  • Create hashed, citeable crops for evidence
  • Compare same-sized screenshots for changed pixels

Key features

  • Local image geometry and metadata
  • Optional Tesseract OCR
  • Source and region hashes
  • Citeable region cropping
  • Same-dimension pixel comparison
  • Trust warnings and OCR availability gaps

Tools4

What Image Reader MCP lets your agent do. Expand a tool for its parameters.

Verified live~3.5k tokens of context

Security & health

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

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11/25 checks healthy over the last 91d

Supply-chain signal

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Scanned 5d ago via OSV.dev · @sylphx/anymd (npm)

Adoption & maintenance

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

GitHub stars2Stargazers on the source repository.
npm downloads20kPackage downloads in the last 30 days.
Last commit1mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed4Callable tools this server registers over MCP.
Directory activity11 viewsConfig copies, upvotes, and views on AllMCPs.

How Image Reader MCP works

What Image Reader MCP MCP server does

Image Reader MCP MCP server exposes local image analysis through the Model Context Protocol. Its main read operation reports image geometry, format, metadata, and trust-related warnings. OCR is opt-in, so ordinary inspection does not invoke a text-recognition process. When requested, OCR results contain recognized text, bounding boxes, and confidence values.

The package is published as @sylphx/iris and starts an MCP server over standard input and output.

How it works

Call read_image with a local path to inspect one image. A region can limit that read to part of the image. The default or fast profile returns image facts without OCR; setting include_ocr to true or using the quality profile enables local Tesseract. An explicit include_ocr: false takes precedence over the quality profile.

image_probe provides a narrower inspection containing format, dimensions, pixel count, and a source hash. It does not crop or recognize text. crop_region identifies one region with its pixel bounds and hash, and can optionally return PNG bytes for that region. compare_images calculates changed pixels and a changed bounding box when both input images have identical dimensions; it does not resize them.

Setup and configuration

Run the server with:

Terminal
npx -y @sylphx/iris

This requires no API key. OCR additionally requires the tesseract executable to be available on PATH. Claude Code can register it with claude mcp add iris -- npx -y @sylphx/iris. Claude Desktop, Cursor, VS Code, and Codex use an MCP configuration with npx as the command and -y, @sylphx/iris as its arguments.

The default input file limit is 32 MiB. A cropped region may contain up to 67,108,864 pixels. GPS metadata is always redacted, with no configuration option to restore it.

Tools and capabilities

The Image Reader MCP MCP server provides four focused tools:

  • read_image: Inspect one local image, optionally within a region, with OCR only when enabled.
  • image_probe: Return basic image properties and a source hash without OCR.
  • crop_region: Produce citeable region bounds and a hash, with optional PNG data.
  • compare_images: Find changed pixels between two same-sized images.

OCR defaults to English, and each OCR line includes text, bbox, and confidence. These outputs support evidence-oriented workflows where an agent should be able to identify the image or region behind a statement.

Limitations and notes

The Image Reader MCP MCP server does not use a generative vision model. If Tesseract is unavailable, a requested OCR operation remains otherwise successful but reports an OCR_UNAVAILABLE gap rather than inventing text. Oversized or undecodable files fail instead of being returned as valid reads.

Comparison only works for images with equal dimensions. Cropping does not itself perform OCR, and probing never crops or reads text. GPS fields remain redacted in all cases. The documented license is MIT.

Read the full README View source on GitHub

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Frequently Asked Questions about Image Reader MCP

Image Reader MCP is an MCP server that connects AI agents to local image files through four tools: read_image, image_probe, crop_region, and compare_images. It returns measurable image facts, optional local Tesseract OCR, hashed regions, and changed-pixel results without using a generative vision model.

Install it by running `npx -y @sylphx/iris`. The command starts a stdio MCP server and does not require an API key. OCR additionally requires the `tesseract` binary on the system `PATH`.

Yes, Image Reader MCP works with Cursor through its MCP configuration. Set the command to `npx` and pass `-y` and `@sylphx/iris` as arguments.

Enable OCR by setting `include_ocr` to true or selecting the `quality` profile in `read_image`. Local Tesseract must be installed and available on `PATH`; otherwise the response reports an `OCR_UNAVAILABLE` gap.

Yes, `compare_images` reports changed pixels and a changed bounding box for two images with equal dimensions. It does not resize either image and does not perform OCR.

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