The PDF Reader MCP service provides AI agents with a secure and flexible function to extract content from PDF files, including text, metadata, and page count information. It supports local and remote PDF files and is easy to integrate into the MCP environment.

This project builds an HR chatbot based on RAG. The MCP server serves as the functional call center to implement PDF document upload, parsing, retrieval, and natural language question answering functions.

This project builds a RAG-based HR chatbot. Through the MCP server as the function call center, it realizes the functions of PDF document uploading, parsing, retrieval, and natural language Q&A.

A local scientific paper auxiliary reading system based on the MCP protocol, providing PDF parsing, in-depth mathematical formula parsing, code generation, and visualization functions, supporting local LLM enhancement and knowledge management.

A paper retrieval and content parsing tool based on arXiv, supporting intelligent search, PDF acquisition, and content parsing functions, with a special focus on the latest papers in the AI field.

PDF Content Extraction Service

A server based on the Model Context Protocol (MCP) that provides access to clinical guidelines from the National Comprehensive Cancer Network (NCCN). This system ensures the accuracy and reliability of medical guidance by directly reading the PDF content of the guidelines instead of using RAG technology.

Zotero - MCP is a Python server that implements the integration of the Model Context Protocol (MCP) with the Zotero literature management software, enabling AI assistants to access and query users' Zotero literature libraries.

ParseFlow is an AI-driven all-in-one document parsing library that supports PDF, Word, Excel, PPT, and image OCR, provides semantic search and batch processing functions, and includes an MCP Server for AI assistants to use.

The MinerU document parsing MCP server supports the extraction of text, tables, and formulas from formats such as PDFs, DOCs, and images. It provides a high-precision VLM model and a fast pipeline model, and supports batch processing and local file uploads.

NetMind ParsePro is a high - quality, robust, and cost - effective PDF parsing AI service that can convert PDF files to JSON or Markdown format and supports seamless integration with AI agents.

GROBID MCP Server Project

A project for parsing the key content of Media Kit PDF files in the advertising and marketing industry, achieving automated processing through integration with the MCP service.

Lizeur is a PDF content extraction server based on the MCP protocol. It uses Mistral AI's OCR technology to convert PDFs into readable Markdown text, supporting intelligent caching and rapid integration.

A service that assists users in learning by analyzing PDF documents, providing functions such as file conversion, content organization, and question generation.

An intelligent learning assistance system based on PDF document analysis, providing functions such as document conversion, content organization, and question generation to help users learn efficiently.