How do I use PDF Redaction MCP Server?

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., "@PDF Redaction MCP Server Redact all credit card numbers in statement.pdf" 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.

PDF Redaction MCP Server

by marc-hanheide

Overview Schema Related Servers Score Discussions

Python

Hybrid

PDF Redaction MCP Server

A Model Context Protocol (MCP) server that provides comprehensive PDF redaction capabilities using FastMCP and pymupdf.

Features

This MCP server enables LLMs to:

Session-based in-memory operations - load PDFs once and perform multiple operations without repeated file I/O
Load and save PDFs - explicit control over when documents are read from and written to disk
Extract text from PDFs in multiple formats (plain text, JSON, or structured blocks)
Search for text patterns using exact match or regex with location information
Redact text by search - automatically find and redact all occurrences of specified strings
Redact by coordinates - precisely redact specific areas of a PDF
Redact images - remove images from PDFs with customisable overlays
Verify redactions - confirm that sensitive information has been properly removed
Get PDF information - retrieve metadata and structure information

Related MCP server: PDF Reader MCP Server

Installation

Prerequisites

Python 3.10 or higher
uv (recommended) or pip

Using uv (Recommended)

# Clone or download the project
cd pdf-redaction-mcp

# Install dependencies
uv sync

# Run the server
uv run pdf-redaction-mcp

Using pip

pip install -e .
pdf-redaction-mcp

Usage

Running the Server

The server supports multiple transport modes and configurations via command-line flags:

# Show all available options
uv run pdf-redaction-mcp --help

# STDIO mode (default) - for desktop clients
uv run pdf-redaction-mcp

# SSE mode - for mobile apps and remote clients
uv run pdf-redaction-mcp --transport sse --port 8000

# HTTP mode - for web-based clients  
uv run pdf-redaction-mcp --transport http --host 0.0.0.0 --port 8080

# With custom PDF directory (relative paths resolved against this)
uv run pdf-redaction-mcp --pdf-dir /path/to/pdfs

# Combined options
uv run pdf-redaction-mcp --transport sse --port 8000 --pdf-dir ~/Documents/pdfs

Command-Line Options

--transport {stdio,http,sse}: Transport mode (default: stdio)
--host HOST: Host to bind to for HTTP/SSE mode (default: 127.0.0.1)
--port PORT: Port to listen on for HTTP/SSE mode (default: 8000)
--pdf-dir PDF_DIR: Base directory for PDF files. Relative paths in tools will be resolved against this directory.

Available Tools

All tools work with in-memory PDF documents using a session-based workflow:

Load a PDF into memory with load_pdf
Operate on it with any of the tools below
Save changes to disk with save_pdf

This approach avoids repeated file I/O and allows multiple operations on the same document efficiently.

1. `load_pdf`

Load a PDF file into memory for session-based operations.

Parameters:

pdf_path (str): Path to the PDF file to load
document_id (str, optional): Identifier for this document (defaults to filename)

Returns: JSON with document_id and basic info

Example:

load_pdf(
    pdf_path="/path/to/document.pdf",
    document_id="my_doc"
)
# Returns: {"document_id": "my_doc", "pages": 10, "status": "loaded"}

2. `save_pdf`

Save an in-memory PDF document to disk.

Parameters:

document_id (str): Identifier of the loaded document
output_path (str): Path where the PDF will be saved

Returns: JSON with save confirmation

Example:

save_pdf(
    document_id="my_doc",
    output_path="/path/to/output.pdf"
)

3. `close_pdf`

Close and remove an in-memory PDF document to free memory.

Parameters:

document_id (str): Identifier of the loaded document

Returns: JSON with close confirmation

Example:

close_pdf(document_id="my_doc")

4. `list_loaded_pdfs`

List all currently loaded PDF documents in memory.

Returns: JSON with information about all loaded documents

Example:

list_loaded_pdfs()
# Returns: {"total_documents": 2, "documents": [{...}, {...}]}

5. `extract_text_from_pdf`

Extract text from a loaded PDF document.

Parameters:

document_id (str): Identifier of the loaded document
page_number (int, optional): Specific page to extract (0-indexed)
format (str): Output format - "text", "json", or "blocks"

Example:

# Load document first
load_pdf(pdf_path="/path/to/document.pdf", document_id="doc1")

# Extract all text
extract_text_from_pdf(
    document_id="doc1",
    format="text"
)

# Extract specific page as JSON
extract_text_from_pdf(
    document_id="doc1",
    page_number=0,
    format="json"
)

6. `search_text_in_pdf`

Search for text patterns and get their locations in a loaded PDF document.

Parameters:

document_id (str): Identifier of the loaded document
search_string (str): Text or regex pattern to search for
case_sensitive (bool): Whether search should be case sensitive
use_regex (bool): Whether to treat search_string as regex
page_number (int, optional): Specific page to search

Example:

# Load document first
load_pdf(pdf_path="/path/to/document.pdf", document_id="doc1")

# Search for email addresses using regex
search_text_in_pdf(
    document_id="doc1",
    search_string=r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
    use_regex=True
)

7. `redact_text_by_search`

Automatically find and redact all occurrences of specified strings in a loaded PDF document.

Parameters:

document_id (str): Identifier of the loaded document
search_strings (List[str]): List of strings to redact
fill_color (Tuple[float, float, float]): RGB colour (0-1 range)
overlay_text (str): Optional text over redacted area
text_color (Tuple[float, float, float]): RGB colour for overlay text

Example:

# Load document
load_pdf(pdf_path="/path/to/input.pdf", document_id="doc1")

# Redact sensitive information (modifies in-memory document)
redact_text_by_search(
    document_id="doc1",
    search_strings=["CONFIDENTIAL", "john.doe@example.com", "123-45-6789"],
    fill_color=(0, 0, 0),  # Black
    overlay_text="[REDACTED]"
)

# Save the redacted document
save_pdf(document_id="doc1", output_path="/path/to/redacted.pdf")

8. `redact_by_coordinates`

Redact specific areas by their exact coordinates in a loaded PDF document.

Parameters:

document_id (str): Identifier of the loaded document
redactions (List[Dict]): List of redaction areas with page, bbox, and optional text
fill_color (Tuple[float, float, float]): RGB colour
overlay_text (str): Default overlay text

Example:

# Load document
load_pdf(pdf_path="/path/to/input.pdf", document_id="doc1")

# Redact specific areas (modifies in-memory document)
redact_by_coordinates(
    document_id="doc1",
    redactions=[
        {"page": 0, "bbox": [100, 100, 300, 150], "text": "REDACTED"},
        {"page": 1, "bbox": [50, 200, 250, 250]}
    ],
    fill_color=(0, 0, 0)
)

# Save the redacted document
save_pdf(document_id="doc1", output_path="/path/to/redacted.pdf")

9. `redact_images_in_pdf`

Remove all images from specified pages of a loaded PDF document.

Parameters:

document_id (str): Identifier of the loaded document
page_numbers (List[int], optional): Pages to process (all if None)
fill_color (Tuple[float, float, float]): RGB colour
overlay_text (str): Text over redacted images

Example:

# Load document
load_pdf(pdf_path="/path/to/input.pdf", document_id="doc1")

# Redact all images on first two pages (modifies in-memory document)
redact_images_in_pdf(
    document_id="doc1",
    page_numbers=[0, 1],
    overlay_text="[IMAGE REMOVED]"
)

# Save the redacted document
save_pdf(document_id="doc1", output_path="/path/to/no_images.pdf")

10. `verify_redactions`

Verify that redactions were applied correctly by comparing two loaded PDF documents.

Parameters:

original_document_id (str): Identifier of the original document
redacted_document_id (str): Identifier of the redacted document
search_strings (List[str], optional): Strings that should be gone

Example:

# Load both documents
load_pdf(pdf_path="/path/to/original.pdf", document_id="original")
load_pdf(pdf_path="/path/to/redacted.pdf", document_id="redacted")

# Verify sensitive data was removed
verify_redactions(
    original_document_id="original",
    redacted_document_id="redacted",
    search_strings=["CONFIDENTIAL", "secret@example.com"]
)

11. `get_pdf_info`

Get metadata and structure information about a loaded PDF document.

Parameters:

document_id (str): Identifier of the loaded document

Example:

# Load document first
load_pdf(pdf_path="/path/to/document.pdf", document_id="doc1")

# Get PDF information
get_pdf_info(document_id="doc1")

Configuration

This section covers how to configure the PDF Redaction MCP Server with various MCP clients.

Quick Links:

Claude Desktop - Most common desktop setup
Cursor IDE - For developers using Cursor
Cline (VSCode) - VSCode MCP extension
Other MCP Clients - Generic STDIO configuration

Claude Desktop

Add to your claude_desktop_config.json:

Basic Configuration (STDIO mode):

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/pdf-redaction-mcp",
        "run",
        "pdf-redaction-mcp"
      ]
    }
  }
}

With Custom PDF Directory:

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/pdf-redaction-mcp",
        "run",
        "pdf-redaction-mcp",
        "--pdf-dir",
        "/Users/yourname/Documents/PDFs"
      ]
    }
  }
}

This allows you to use relative paths like "document.pdf" instead of full paths.

Cursor IDE

Add to your .cursor/mcp.json:

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/pdf-redaction-mcp",
        "run",
        "pdf-redaction-mcp"
      ]
    }
  }
}

Cline (VSCode Extension)

Add to your Cline MCP settings:

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/pdf-redaction-mcp",
        "run",
        "pdf-redaction-mcp",
        "--pdf-dir",
        "${workspaceFolder}/pdfs"
      ]
    }
  }
}

Other MCP Clients

For any MCP client supporting STDIO transport, use:

Command: uv

Args:

--directory /path/to/pdf-redaction-mcp
run
pdf-redaction-mcp
[optional flags like --pdf-dir]

Environment Variables (Optional)

For production deployments, you can use environment variables:

# Set PDF directory via environment
export PDF_DIR=/var/pdfs

# Then reference in your startup script
uv run pdf-redaction-mcp --pdf-dir "$PDF_DIR"

Real-World Configuration Examples

Example 1: Personal Use with Claude Desktop

Store all PDFs in your Documents folder:

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/yourname/workspace/pdf-redaction-mcp",
        "run",
        "pdf-redaction-mcp",
        "--pdf-dir",
        "/Users/yourname/Documents"
      ]
    }
  }
}

Now you can say: "Redact emails from report.pdf" instead of using full paths.

Example 2: Team Deployment with Shared PDFs

Deploy remotely with network-mounted PDF storage:

# On your server
uv run pdf-redaction-mcp \
  --transport sse \
  --host 0.0.0.0 \
  --port 8000 \
  --pdf-dir /mnt/shared-pdfs

Team members configure their clients to use the remote server.

Example 3: Development Setup

Use project-relative paths during development:

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory",
        "${workspaceFolder}/pdf-redaction-mcp",
        "run",
        "pdf-redaction-mcp",
        "--pdf-dir",
        "${workspaceFolder}/test-pdfs"
      ]
    }
  }
}

Workflow Examples

Example 1: Redact Personal Information

Session-based workflow (new approach):

User: "Please redact all email addresses and phone numbers from report.pdf"

1. LLM loads the document:
   load_pdf(pdf_path="report.pdf", document_id="report")

2. LLM searches for patterns:
   search_text_in_pdf(
     document_id="report",
     search_string=r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
     use_regex=True
   )

3. LLM redacts in-memory:
   redact_text_by_search(
     document_id="report",
     search_strings=["john@example.com", "555-123-4567", ...]
   )

4. LLM saves the result:
   save_pdf(document_id="report", output_path="report_redacted.pdf")

5. LLM reports: "Successfully redacted 5 email addresses and 3 phone numbers"

Benefits of session-based approach:

PDF loaded once, multiple operations performed
No repeated file I/O
Can verify, modify, and re-verify without reloading

Example 2: Redact Specific Section

1. User: "Redact the financial table on page 3 of the report"

2. LLM loads document:
   load_pdf(pdf_path="report.pdf", document_id="report")

3. LLM extracts page structure:
   extract_text_from_pdf(document_id="report", page_number=2, format="blocks")

4. LLM identifies table coordinates from block structure

5. LLM redacts in-memory:
   redact_by_coordinates(
     document_id="report",
     redactions=[{"page": 2, "bbox": [100, 200, 500, 400]}]
   )

6. LLM verifies by extracting text again:
   extract_text_from_pdf(document_id="report", page_number=2)

7. LLM saves:
   save_pdf(document_id="report", output_path="report_redacted.pdf")

Example 3: Remove All Images

1. User: "Remove all images from the document but keep the text"

2. LLM loads document:
   load_pdf(pdf_path="document.pdf", document_id="doc")

3. LLM checks for images:
   get_pdf_info(document_id="doc")

4. LLM redacts images:
   redact_images_in_pdf(document_id="doc")

5. LLM verifies and saves:
   get_pdf_info(document_id="doc")  # Verify images are gone
   save_pdf(document_id="doc", output_path="document_no_images.pdf")
   
6. LLM cleans up:
   close_pdf(document_id="doc")  # Free memory

Example 4: Multi-Step Verification Workflow

1. User: "Redact all SSNs, then verify they're gone, then redact names too"

2. LLM loads document:
   load_pdf(pdf_path="sensitive.pdf", document_id="sensitive")

3. LLM redacts SSNs:
   redact_text_by_search(
     document_id="sensitive",
     search_strings=[r"\d{3}-\d{2}-\d{4}"],
     use_regex=True
   )

4. LLM creates checkpoint by saving:
   save_pdf(document_id="sensitive", output_path="sensitive_step1.pdf")

5. LLM loads original for comparison:
   load_pdf(pdf_path="sensitive.pdf", document_id="original")

6. LLM verifies:
   verify_redactions(
     original_document_id="original",
     redacted_document_id="sensitive",
     search_strings=["123-45-6789", "987-65-4321"]
   )

7. LLM continues with name redaction:
   redact_text_by_search(
     document_id="sensitive",
     search_strings=["John Doe", "Jane Smith"]
   )

8. LLM saves final version:
   save_pdf(document_id="sensitive", output_path="sensitive_final.pdf")

9. LLM cleans up:
   close_pdf(document_id="original")
   close_pdf(document_id="sensitive")

LLM verifies using get_pdf_info that images are gone




---

## Troubleshooting

### Claude Desktop Connection Issues

**Problem:** MCP server not connecting in Claude Desktop

**Solutions:**
1. Verify the path in `claude_desktop_config.json` is correct:
   ```bash
   # Check if the directory exists
   ls -la /path/to/pdf-redaction-mcp

Test the server manually:

cd /path/to/pdf-redaction-mcp
uv run pdf-redaction-mcp --help

Check Claude Desktop logs:
- macOS: ~/Library/Logs/Claude/
- Windows: %APPDATA%\Claude\logs\
- Linux: ~/.config/Claude/logs/

PDF Path Issues

Problem: "File not found" errors when using relative paths

Solution: Configure --pdf-dir flag in your MCP client config:

{
  "mcpServers": {
    "pdf-redaction": {
      "command": "uv",
      "args": [
        "--directory", "/path/to/pdf-redaction-mcp",
        "run", "pdf-redaction-mcp",
        "--pdf-dir", "/your/pdf/directory"
      ]
    }
  }
}

Port Already in Use (HTTP/SSE mode)

Problem: Address already in use error when starting server

Solution:

Use a different port:

uv run pdf-redaction-mcp --transport sse --port 8001

Or find and kill the process using the port:

# macOS/Linux
lsof -ti:8000 | xargs kill -9

# Windows
netstat -ano | findstr :8000
taskkill /PID <PID> /F

UV Not Found

Problem: uv: command not found

Solution: Install UV package manager:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or use pip
pip install uv

Development

Running Tests

uv run pytest

Project Structure

pdf-redaction-mcp/
├── src/
│   └── pdf_redaction_mcp/
│       ├── __init__.py
│       └── server.py          # Main MCP server implementation
├── tests/
│   └── test_server.py         # Unit tests
├── pyproject.toml             # Project dependencies
└── README.md                  # This file

Technical Details

Redaction Implementation

The server uses pymupdf's redaction annotations, which:

Add redaction annotations to mark areas for removal
Apply redactions to permanently remove content
Cannot be undone once saved - content is truly deleted from PDF structure

Colour Format

Colours are specified as RGB tuples with values from 0 to 1:

Black: (0, 0, 0)
White: (1, 1, 1)
Red: (1, 0, 0)
Green: (0, 1, 0)
Blue: (0, 0, 1)

Coordinate System

PDF coordinates use bottom-left origin:

x0, y0: Bottom-left corner of rectangle
x1, y1: Top-right corner of rectangle

Bounding boxes: [x0, y0, x1, y1]

Security Considerations

Permanent Removal: Redactions permanently remove content from PDF structure
Verify Redactions: Always use verify_redactions to confirm sensitive data is gone
Backup Original: Keep original files backed up before redacting
File Paths: Ensure proper file path validation in production
Access Control: Implement appropriate access controls for sensitive documents

Limitations

Only works with PDF files (use pymupdf's supported formats)
Encrypted PDFs may require password authentication
Very large PDFs may require significant memory
Redactions are permanent once saved

Contributing

Contributions are welcome! Please ensure:

Code follows existing style
Tests pass (uv run pytest)
Documentation is updated
Commit messages are clear

Licence

MIT Licence - see LICENCE file for details

Acknowledgements

FastMCP - MCP framework
pymupdf - PDF manipulation library
Model Context Protocol - MCP specification

Support

For issues, questions, or contributions:

Open an issue on GitHub
Check the FastMCP documentation
Check the pymupdf documentation

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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PDF Redaction MCP Server

Features

Installation

Prerequisites

Using uv (Recommended)

Using pip

Usage

Running the Server

Command-Line Options

Available Tools

1. load_pdf

2. save_pdf

3. close_pdf

4. list_loaded_pdfs

5. extract_text_from_pdf

6. search_text_in_pdf

7. redact_text_by_search

8. redact_by_coordinates

9. redact_images_in_pdf

10. verify_redactions

11. get_pdf_info

Configuration

Claude Desktop

Cursor IDE

Cline (VSCode Extension)

Other MCP Clients

Environment Variables (Optional)

Real-World Configuration Examples

Workflow Examples

Example 1: Redact Personal Information

Example 2: Redact Specific Section

Example 3: Remove All Images

Example 4: Multi-Step Verification Workflow

PDF Path Issues

Port Already in Use (HTTP/SSE mode)

UV Not Found

Development

Running Tests

Project Structure

Technical Details

Redaction Implementation

Colour Format

Coordinate System

Security Considerations

Limitations

Contributing

Licence

Acknowledgements

Support

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

1. `load_pdf`

2. `save_pdf`

3. `close_pdf`

4. `list_loaded_pdfs`

5. `extract_text_from_pdf`

6. `search_text_in_pdf`

7. `redact_text_by_search`

8. `redact_by_coordinates`

9. `redact_images_in_pdf`

10. `verify_redactions`

11. `get_pdf_info`