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Local OCR & image analysis via Apple Vision — no cloud, no API keys, ~97% fewer tokens on PDFs.
Local OCR & image analysis via Apple Vision — no cloud, no API keys, ~97% fewer tokens on PDFs.
Valid MCP server (2 strong, 3 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-woladi-macos-vision-mcp": {
"args": [
"-y",
"macos-vision-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Local OCR & image analysis for any MCP client — private, offline, no API keys.
Pre-extracts text and image data locally before your AI ever sees it — cutting token usage by ~97% on real documents. Files never leave your Mac: no cloud API, no API keys, no network requests.
npm install — powered by Apple Vision Framework, same engine as Live Text in Photos.app.❌ Without macos-vision-mcp:
✅ With macos-vision-mcp:
macos-vision-mcp acts as a local pre-processing layer between your documents and the cloud. Useful for:
Instead of sending the raw document to your AI, you extract the text and structure locally first. The model then works only with the extracted text — never the original file.
Step 1 — Install the package:
npm install -g macos-vision-mcp
Step 2 — Add to your MCP client (example for Claude Code):
claude mcp add macos-vision-mcp -- macos-vision-mcp
Restart your client. The tools appear automatically.
Note: The native module
macos-visioncompiles against your local Node.js at install time. If you switch Node versions, runnpm rebuildinside the package directory.
| Tool | What it does | Example prompt |
|---|---|---|
ocr_image | Extract text from an image or PDF (JPG, PNG, HEIC, TIFF, PDF). Returns plain text or structured blocks with bounding boxes. | "Read the text from ~/Desktop/screenshot.png" |
detect_faces | Detect human faces and return their count and positions. | "How many people are in this photo?" |
detect_barcodes | Read QR codes, EAN, UPC, Code128, PDF417, Aztec, and other 1D/2D codes. | "What does the QR code in /tmp/qr.jpg say?" |
classify_image | Classify image content into 1000+ categories with confidence scores. | "What is in this image?" |
analyze_document | Full pipeline: OCR + faces + barcodes + rectangles in one call. | "Extract everything from this scanned invoice" |
Use the tool name explicitly in your prompt to guarantee local processing:
Extract text from an image or PDF:
Use ocr_image to extract text from ~/Desktop/invoice.pdf
Detect faces in a photo:
Use detect_faces on ~/Photos/team.jpg and tell me how many people are in it
Classify image content:
Use classify_image on ~/Downloads/unknown.jpg
Full document analysis (OCR + faces + barcodes in one call):
Use analyze_document on ~/Desktop/scan.pdf and extract everything you can find
claude mcp add macos-vision-mcp -- macos-vision-mcp
Edit ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"macos-vision-mcp": {
"command": "macos-vision-mcp"
}
}
}
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"macos-vision-mcp": {
"command": "macos-vision-mcp"
}
}
}
If you installed with npx rather than globally, replace "command": "macos-vision-mcp" with "command": "npx", "args": ["macos-vision-mcp"].
Contributions are welcome. Please follow Conventional Commits for commit messages — this project uses release-it with @release-it/conventional-changelog to automate releases.
git clone <repo>
cd macos-vision-mcp
npm install
npm run dev # watch mode
MIT — Adrian Wolczuk
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