PDF Reader

Fetch with Images

Enhances online data retrieval by combining web scraping and image manipulation functions for efficient web content extraction and enhancement.

Fetch

Converts online information into different types of files.

PDF Manipulation

Utilizing Python libraries, this protocol seamlessly incorporates functions for handling PDF documents, allowing for tasks such as merging, extracting, and retrieving content based on document context.

PDF Extraction

Efficiently analyze and index document content by utilizing Python libraries for text extraction and OCR on PDF files. Optimize your document processing with advanced tools for seamless document analysis.

Docling

Unlock powerful document processing features by connecting seamlessly with the Docling library. From converting files to markdown and extracting tables to handling images with OCR functionality, this model context protocol streamlines the analysis of diverse document types, organized or random.

Mistral OCR

Utilizing Mistral AI's OCR API, this Model Context Protocol (MCP) efficiently analyzes images and PDFs to retrieve text from visual content. It accommodates various file sources, including local files and URLs, and is complemented by Docker containerization for simplified deployment.

Read Website Fast

Efficiently convert web content to Markdown using Mozilla Readability, featuring advanced article detection, disk-based caching, robots.txt adherence, and concurrent crawling for rapid content handling.

Box

Unlock the potential of seamless integration with Box cloud storage, empowering swift access, exploration, and manipulation of PDFs and Word files. Ideal for streamlining tasks such as automated document scrutiny and information extraction.

RapidOCR

Effortlessly capture text from images with the innovative RapidOCR library, allowing seamless integration for automated document workflows. Utilize base64-encoded data or file paths to streamline your document processing tasks with efficiency and precision.

PDF Reader

Efficiently retrieve textual content, metadata details, and page specifics from PDF documents in a project folder by leveraging pdfjs-dist for processing local files and online links.

PDF Reader

Unlock the capability to access and retrieve information from both secured and unsecured PDF documents with the Model Context Protocol (MCP). This protocol empowers users to analyze documents, index content, and extract data seamlessly.

PDF Forms

Discover a comprehensive solution for locating PDF documents, extracting data from form fields, and presenting form field details within PDF files utilizing PyMuPDF functionalities.

Image Reader

Harnessing the power of TypeScript and Sharp, this Model Context Protocol (MCP) offers cutting-edge image processing features. Users can effortlessly manage, analyze, adjust sizes, and transform images while benefiting from versatile tools for metadata extraction, thumbnail creation, and format conversion.

PDF Search

Enhance Zed's search capabilities on PDF files with a powerful combination of Qdrant vector database and OpenAI embeddings for advanced semantic indexing.

Excel Reader

Easily connects with Excel spreadsheets to extract and analyze data, streamlining tasks for automated reporting and in-depth insights.