Which integrations are available for this server?

Supports converting PDF documents and extracted tables into structured Markdown content to preserve document layout in text-based environments. Enables the extraction of vector graphics, such as schematics, charts, and technical drawings, from PDF files into SVG format.

How do I use MCP PDF?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP PDF summarize the quarterly report PDF for me" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

📄 MCP PDF

A FastMCP server for PDF processing

46 tools for text extraction, OCR, tables, forms, annotations, and more

Python 3.11+ FastMCP License: MIT PyPI

Works great with MCP Office Tools

What It Does

MCP PDF extracts content from PDFs using multiple libraries with automatic fallbacks. If one method fails, it tries another.

Core capabilities:

Text extraction via PyMuPDF, pdfplumber, or pypdf (auto-fallback)
Table extraction via Camelot, pdfplumber, or Tabula (auto-fallback)
OCR for scanned documents via Tesseract
Form handling - extract, fill, and create PDF forms
Document assembly - merge, split, reorder pages
Annotations - sticky notes, highlights, stamps
Vector graphics - extract to SVG for schematics and technical drawings

Quick Start

# Install from PyPI
uvx mcp-pdf

# Or add to Claude Code
claude mcp add pdf-tools uvx mcp-pdf

git clone https://github.com/rsp2k/mcp-pdf
cd mcp-pdf
uv sync

# System dependencies (Ubuntu/Debian)
sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript

# Verify
uv run python examples/verify_installation.py

Tools

Content Extraction

Tool	What it does
`extract_text`	Pull text from PDF pages with automatic chunking for large files
`extract_tables`	Extract tables to JSON, CSV, or Markdown
`extract_images`	Extract embedded images
`extract_links`	Get all hyperlinks with page filtering
`pdf_to_markdown`	Convert PDF to markdown preserving structure
`ocr_pdf`	OCR scanned documents using Tesseract
`extract_vector_graphics`	Export vector graphics to SVG (schematics, charts, drawings)

Document Analysis

Tool	What it does
`extract_metadata`	Get title, author, creation date, page count, etc.
`get_document_structure`	Extract table of contents and bookmarks
`analyze_layout`	Detect columns, headers, footers
`is_scanned_pdf`	Check if PDF needs OCR
`compare_pdfs`	Diff two PDFs by text, structure, or metadata
`analyze_pdf_health`	Check for corruption, optimization opportunities
`analyze_pdf_security`	Report encryption, permissions, signatures

Forms

Tool	What it does
`extract_form_data`	Get form field names and values
`fill_form_pdf`	Fill form fields from JSON
`create_form_pdf`	Create new forms with text fields, checkboxes, dropdowns
`add_form_fields`	Add fields to existing PDFs

Permit Forms (Coordinate-Based)

For scanned PDFs or forms without interactive fields. Draws text at (x, y) coordinates.

Tool	What it does
`fill_permit_form`	Fill any PDF by drawing at coordinates (works with scanned forms)
`get_field_schema`	Get field definitions for validation or UI generation
`validate_permit_form_data`	Check data against field schema before filling
`preview_field_positions`	Generate PDF showing field boundaries (debugging)
`insert_attachment_pages`	Insert image/text pages with "See page X" references

Requires: pip install mcp-pdf[forms] (adds reportlab dependency)

Document Assembly

Tool	What it does
`merge_pdfs`	Combine multiple PDFs with bookmark preservation
`split_pdf_by_pages`	Split by page ranges
`split_pdf_by_bookmarks`	Split at chapter/section boundaries
`reorder_pdf_pages`	Rearrange pages in custom order

Annotations

Tool	What it does
`add_sticky_notes`	Add comment annotations
`add_highlights`	Highlight text regions
`add_stamps`	Add Approved/Draft/Confidential stamps
`extract_all_annotations`	Export annotations to JSON

How Fallbacks Work

The server tries multiple libraries for each operation:

Text extraction:

PyMuPDF (fastest)
pdfplumber (better for complex layouts)
pypdf (most compatible)

Table extraction:

Camelot (best accuracy, requires Ghostscript)
pdfplumber (no dependencies)
Tabula (requires Java)

If a PDF fails with one library, the next is tried automatically.

Token Management

Large PDFs can overflow MCP response limits. The server handles this:

Automatic chunking splits large documents into page groups
Table row limits prevent huge tables from blowing up responses
Summary mode returns structure without full content

# Get first 10 pages
result = await extract_text("huge.pdf", pages="1-10")

# Limit table rows
tables = await extract_tables("data.pdf", max_rows_per_table=50)

# Structure only
tables = await extract_tables("data.pdf", summary_only=True)

URL Processing

PDFs can be fetched directly from HTTPS URLs:

result = await extract_text("https://example.com/report.pdf")

Files are cached locally for subsequent operations.

System Dependencies

Some features require system packages:

Feature	Dependency
OCR	`tesseract-ocr`
Camelot tables	`ghostscript`
Tabula tables	`default-jre-headless`
PDF to images	`poppler-utils`

Ubuntu/Debian:

sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript default-jre-headless

Configuration

Optional environment variables:

Variable	Purpose
`MCP_PDF_ALLOWED_PATHS`	Colon-separated directories for file output
`PDF_TEMP_DIR`	Temp directory for processing (default: `/tmp/mcp-pdf-processing`)
`TESSDATA_PREFIX`	Tesseract language data location

Development

# Run tests
uv run pytest

# With coverage
uv run pytest --cov=mcp_pdf

# Format
uv run black src/ tests/

# Lint
uv run ruff check src/ tests/

License

MIT

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

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