liteparse-mcp

Fast, local PDF parsing as an MCP server — text extraction, bounding boxes, OCR, and visual citations. No cloud. No API key. Powered by LiteParse.

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Tools

Tool	Description
parse_pdf	Extract text + bounding boxes (x, y, width, height in PDF points) from a PDF
batch_parse_pdfs	Parse every PDF in a folder; write JSON + screenshots per file
screenshot_pdf	Render pages as base64 PNG images
cited_screenshot	Render a page with highlight boxes drawn over every text item
search_pdf	Find a phrase and return all matching positions with coordinates

Bounding-box coordinates are in PDF points (1 pt = 1/72 in), origin top-left. To convert to pixels: px = pt × (dpi / 72).

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Install

pip install liteparse-mcp

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Usage

Claude Desktop

Add to ~/AppData/Roaming/Claude/claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "liteparse": {
      "command": "liteparse-mcp"
    }
  }
}

Or with the explicit Python path (if liteparse-mcp is not on PATH):

{
  "mcpServers": {
    "liteparse": {
      "command": "python",
      "args": ["-m", "liteparse_mcp"]
    }
  }
}

Restart Claude Desktop — the five tools appear automatically.

Claude Code

claude mcp add liteparse -- python -m liteparse_mcp

HTTP / SSE (for remote agents or testing)

liteparse-mcp --http
# Server listens on http://127.0.0.1:8765

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Example agent prompts

• "Parse report.pdf and show me where 'efficacy' appears with bounding boxes"
• "Get a cited screenshot of page 3 of study.pdf"
• "Batch parse every PDF in my Downloads folder and save the output"
• "Search safety_data.pdf for 'adverse event' and list the page numbers"

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Outputs (batch mode)

For each PDF, batch_parse_pdfs writes:

<output_folder>/
  <stem>/
    pages.json          # structured JSON: page text + TextItem bounding boxes
    summary.txt         # plain text of the whole document
    page_1.png          # raw page screenshot
    page_1_cited.png    # screenshot with bounding-box highlights
    ...
  batch_report.json     # overall success / error summary

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Requirements

• Python ≥ 3.10
• liteparse ≥ 2.0.0 (Rust-based; wheels available for Windows, macOS, Linux)
• fastmcp ≥ 2.0.0

No Tesseract installation required for text-based PDFs. For scanned PDFs with ocr_enabled=true, Tesseract is used automatically if available on PATH.

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License

MIT