PDF Server

A PDF file reading server based on FastMCP.

Supports PDF text extraction, OCR recognition, and image extraction via the MCP protocol, with a built-in web debugger for easy testing.

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🚀 Features

• read_pdf_text
  Extracts normal text from a PDF (page by page).

• read_by_ocr
  Uses OCR to recognize text from scanned or image-based PDFs.

• read_pdf_images
  Extracts all images from a specified PDF page (Base64 encoded output).

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📂 Project Structure

mcp-pdf-server/
├── pdf_server.py         # Main server entry point
└── README.md             # Project documentation

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⚙️ Installation

Recommended Python version: 3.9+

pip install pymupdf mcp

Note: To use OCR features, you may need a MuPDF build with OCR support or external OCR libraries.

🤖 Configuration

{
  "mcpServers": {
    "pdf-reader": {
      "command": "uvx",
      "timeout": 60000,
      "args": [
        "mcp-pdf-reader"
      ]
    }
  }
}

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🔦 Start the Server

Run the following command:

python pdf_server.py

You should see logs like:

INFO:mcp-pdf-server:Starting MCP PDF Server...

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🛠️ API Tool List

Tool	Description	Input Parameters	Returns
read_pdf_text	Extracts normal text from PDF pages	file_path, start_page, end_page	List of page texts
read_by_ocr	Recognizes text via OCR	file_path, start_page, end_page, language, dpi	OCR extracted text
read_pdf_images	Extracts images from a PDF page	file_path, page_number	List of images (Base64 encoded)

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📝 Example Usage

Extract text from pages 1 to 5:

mcp run read_pdf_text --args '{"file_path": "pdf_resources/example.pdf", "start_page": 1, "end_page": 5}'

Perform OCR recognition on page 1:

mcp run read_by_ocr --args '{"file_path": "pdf_resources/example.pdf", "start_page": 1, "end_page": 1, "language": "eng"}'

Extract all images from page 3:

mcp run read_pdf_images --args '{"file_path": "pdf_resources/example.pdf", "page_number": 3}'

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📢 Notes

• Files must be placed inside the pdf_resources/ directory, or an absolute path must be provided.
• OCR functionality requires appropriate OCR support in the environment.
• When processing large files, adjust memory and timeout settings as needed.

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📜 License

This project is licensed under the MIT License.
For commercial use, please credit the original source.

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