What are the system requirements for Local Documents?

It requires Windows 10/11, Python 3.13 or higher, and external dependencies like Poppler and Tesseract OCR for full functionality.

What is Local Documents?

Local Documents is a Windows-based server tool that enables efficient listing, content extraction, and OCR for local files, designed for interaction with AI models like Claude Desktop.

What types of documents can Local Documents process?

It supports a wide range of formats including Word (.docx), PDF (regular and scanned), PowerPoint (.pptx), and Excel (.xlsx), converting their content to markdown.

Does Local Documents support Optical Character Recognition (OCR)?

Yes, it features robust OCR support using Tesseract to extract text from scanned PDFs, making their content accessible for search and processing.

How does Local Documents integrate with AI models?

It provides a Model Context Protocol (MCP) server, allowing AI clients like Claude Desktop to discover, load, and process document content efficiently, including automatic token management.

Local Documents

Name: Local Documents
Author: Baronco

byBaronco

•

Productivity & Workflow

Data Science & ML

Content Management

Provides a server for interacting with local documents on Windows, enabling efficient listing, content extraction, and optical character recognition (OCR) on scanned PDFs.

Local Documents

byBaronco

•

Productivity & Workflow

Data Science & ML

Content Management

Provides a server for interacting with local documents on Windows, enabling efficient listing, content extraction, and optical character recognition (OCR) on scanned PDFs.

The Local Documents server acts as a Model Context Protocol (MCP) server designed specifically for Windows users to bridge local document collections with AI models. It allows seamless discovery of files, conversion of various formats (including Word, PowerPoint, Excel, and standard PDFs) into markdown, and features robust OCR support for extracting text from scanned PDFs. By automatically managing content truncation based on token limits, it enables large language models (via MCP clients like Claude Desktop) to effectively 'read' and process vast amounts of local data for analysis, summarization, or other AI-driven tasks.

Key Features

01Document Discovery for specified directories

02Document Processing to convert various formats to markdown

03OCR Support for text extraction from scanned PDFs using Tesseract

04Automatic Token Management and content truncation

05Multi-format Support for Word, PDF, PowerPoint, Excel, and more

060 GitHub stars

Use Cases

01Integrating local document collections with AI models (e.g., Claude Desktop) for contextual understanding.

02Extracting searchable text from scanned documents and images for AI ingestion or archival purposes.

03Enabling AI agents to process, analyze, and summarize content from personal or enterprise local files.