mcp-file-contents-reader 1.0.7

MCP (Model Context Protocol) server for reading and analyzing various file formats including PDF, Excel, Word, and PowerPoint

MCP File Contents Reader

A Model Context Protocol (MCP) server for reading and analyzing various file formats including PDF, Excel, Word, and PowerPoint documents.

Features

• Multi-format Support: Read PDF, Excel (.xlsx, .xls), Word (.docx, .doc), and PowerPoint (.pptx, .ppt) files
• Content Analysis: Extract and analyze file contents with structured information extraction
• Document Search: Search for specific content across multiple documents
• File Upload: Support for temporary file upload and processing
• MCP Integration: Full Model Context Protocol compliance

Installation

Using uvx (Recommended)

uvx mcp-file-contents-reader

Using pip

pip install mcp-file-contents-reader

From Source

git clone https://github.com/yourusername/mcp-file-contents-reader.git
cd mcp-file-contents-reader
pip install -e .

Usage

MCP Configuration

Add the following to your mcp.json configuration file:

{
  "mcpServers": {
    "file-reader": {
      "command": "uvx",
      "args": ["mcp-file-contents-reader"]
    }
  }
}

Or if installed via pip:

{
  "mcpServers": {
    "file-reader": {
      "command": "mcp-file-contents-reader"
    }
  }
}

Available Tools

1. read_file

Read Excel, PDF, PPT, Word files and return content as text.

Parameters:

• file_path (required): Path to the file to read
• sheet_name (optional): Sheet name for Excel files
• page_range (optional): Page range for PDF files (e.g., '1-5' or '1,3,5')

2. search_documents

Search for specific content in Documents directory and analyze files.

Parameters:

• keywords (required): Keywords to search for in file content
• search_path (optional): Directory path to search (default: ~/Documents)
• file_types (optional): File types to search (default: ["pdf", "docx", "xlsx", "pptx", "doc", "xls", "ppt"])

3. analyze_file_content

Analyze specific file content in detail and extract structured information.

Parameters:

• file_path (required): Path to the file to analyze
• extract_patterns (optional): Specific patterns or information types to extract

4. upload_file

Upload and temporarily store Base64 encoded file data.

Parameters:

• file_data (required): Base64 encoded file data
• filename (required): Filename with extension

5. read_uploaded_file

Read uploaded file and return content.

Parameters:

• file_id (required): ID of the uploaded file

6. list_uploaded_files

Return list of uploaded files.

7. delete_uploaded_file

Delete uploaded file.

Parameters:

• file_id (required): ID of the file to delete

8. get_file_info

Return basic information about a file.

Parameters:

• file_path (required): Path to the file to get information about

9. list_supported_formats

Return list of supported file formats.

Supported File Formats

• Excel: .xlsx, .xls
• PDF: .pdf
• PowerPoint: .pptx, .ppt
• Word: .docx, .doc

Example Usage

Search for donation receipts

{
  "tool": "search_documents",
  "arguments": {
    "keywords": ["donation", "receipt", "charity", "fund"],
    "search_path": "/Users/username/Documents",
    "file_types": ["pdf", "docx", "xlsx"]
  }
}

Analyze a specific file

{
  "tool": "analyze_file_content",
  "arguments": {
    "file_path": "/Users/username/Documents/receipt.pdf",
    "extract_patterns": ["donor", "amount", "organization", "date"]
  }
}

Development

Setup Development Environment

git clone https://github.com/yourusername/mcp-file-contents-reader.git
cd mcp-file-contents-reader
pip install -e ".[dev]"

Running Tests

pytest

Code Formatting

black mcp_file_reader/

Type Checking

mypy mcp_file_reader/

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for your changes
5. Run the test suite
6. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

1.0.0

• Initial release
• Support for PDF, Excel, Word, and PowerPoint files
• MCP server implementation
• Document search and analysis capabilities