macos-vision-mcp

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 and returning structured paragraphs, lines, and bounding boxes so the model can reconstruct the document into Markdown, HTML, DOCX, or any other format. Files never leave your Mac: no cloud API, no API keys, no network requests.

What you get

• OCR for images and PDFs (JPG, PNG, HEIC, TIFF, multi-page PDF) via Apple Vision Framework.
• ~97% token reduction: a 44-page PDF costs ~2,400 tokens instead of ~73,500.
• Reading-order paragraphs + raw text blocks with bounding boxes — rich structure for the model to reconstruct the document into any output format (Markdown, HTML, DOCX, JSON), not a lossy plain-text dump.
• Face detection, barcode/QR reading, and image classification — all on-device.
• Full document pipeline: OCR + faces + barcodes + rectangles in a single tool call.
• Works with Claude Code, Claude Desktop, and Cursor — any MCP-compatible client.
• No files uploaded to any server — processing stays entirely on your Mac.
• 100% offline after npm install — powered by Apple Vision Framework, same engine as Live Text in Photos.app.

❌ Without / ✅ With

❌ Without macos-vision-mcp:

• Sending a 44-page PDF costs ~73,500 tokens
• Every image, invoice, or contract goes through a cloud API
• Sensitive documents leave your machine on every request

✅ With macos-vision-mcp:

• Local Apple Vision pre-extracts text before Claude ever sees it
• ~2,400 tokens for the same 44-page PDF — 97% fewer
• Files never leave your Mac

Privacy layer

macos-vision-mcp acts as a local pre-processing layer between your documents and the cloud. Useful for:

• Legal documents, contracts, NDAs
• Financial reports, invoices, internal spreadsheets
• Medical records or any GDPR-sensitive content
• Any situation where you want to extract structured data locally before deciding what (if anything) to send upstream

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.

Quick Start

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-vision compiles against your local Node.js at install time. If you switch Node versions, run npm rebuild inside the package directory.

Available Tools

| Tool | What it does | Example prompt | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | | ocr_image | Extract text from an image or PDF (JPG, PNG, HEIC, TIFF, PDF). Returns plain text, or per-page paragraphs + text blocks with lineId / paragraphId and 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 | Returns structured JSON with reading-order paragraphs, raw text blocks (bbox / confidence), faces, barcodes, and rectangles — ready for the model to reconstruct into Markdown, HTML, or anything else. | "Reconstruct ~/Desktop/scan.pdf as clean Markdown" |

Usage

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 + reconstruction:

Use analyze_document on ~/Desktop/report.pdf and reconstruct it as clean Markdown

The tool returns structured JSON; the model picks the output format you ask for (Markdown, HTML, DOCX outline, etc.) without any extra dependencies — no Ollama, no cloud LLM, no extra tooling.

Output schema (analyze_document)

{
  "source": { "path": "...", "pageCount": 1, "isPdf": false },
  "pages": [
    {
      "page": 0,
      // primary surface for reconstruction — reading-order paragraphs joined with "\n"
      "paragraphs": [
        { "paragraphId": 0, "lineIds": [0], "text": "ACME COFFEE" },
        { "paragraphId": 1, "lineIds": [1, 2], "text": "12 Main St\nPortland, OR" },
      ],
      // spatial fallback — raw blocks with page-local 0–1 bbox, confidence, line/paragraph membership
      "textBlocks": [
        {
          "text": "ACME COFFEE",
          "lineId": 0,
          "paragraphId": 0,
          "confidence": 0.99,
          "bbox": { "x": 0.21, "y": 0.04, "width": 0.58, "height": 0.06 },
        },
      ],
      "faces": [],
      "barcodes": [],
      "rectangles": [],
    },
  ],
  "summary": {
    "totalTextBlocks": 8,
    "totalParagraphs": 2,
    "totalFaces": 0,
    "totalBarcodes": 0,
    "totalRectangles": 0,
  },
}

Use paragraphs[].text for the 95% case (rebuild Markdown/HTML/plain text directly). Reach for textBlocks[] when you need spatial context — multi-column layouts, tables, forms, IDs.

Notes:

• ocr_image in blocks mode returns the same per-page shape minus the detection sections: { pages: [{ page, paragraphs, textBlocks }] }.
• PDFs are processed page by page. All coordinates are page-local (0–1), and paragraphId / lineId reset on every page.
• Face, barcode, and rectangle detection on PDFs is best-effort — the underlying binary analyzes the file as a whole rather than per page, so any detections returned are attached to page 0 only.
• Paragraph grouping uses spatial heuristics. For multi-column layouts (magazine spreads, wiki pages with side panels) the heuristic can collapse the whole page into a single paragraph. When that happens, fall back to textBlocks[] and reconstruct from the bounding boxes.

Configuration

Claude Code

claude mcp add macos-vision-mcp -- macos-vision-mcp

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "macos-vision-mcp": {
      "command": "macos-vision-mcp"
    }
  }
}

Cursor

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"].

Contributing

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

License

MIT — Adrian Wolczuk