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Nutrient Dws MCP Server
The Nutrient DWS MCP Server is a model context protocol server integrated with the Nutrient Document Web Service processor API, providing powerful PDF processing functions for AI assistants, including digital signatures, document generation, editing, OCR, watermarking, redaction, and other operations.
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P
Pdfco MCP
The PDF.co MCP Server is a tool that provides PDF processing functions through the Model Context Protocol, supporting various operations such as PDF conversion, editing, searching, security protection, and file management.
Python
4.9K
2 points
M
Memvid MCP Server
The Memvid MCP Server is an AI service interface based on video memory technology, supporting encoding content such as text and PDFs into video formats and providing semantic search and conversation functions.
Python
6.5K
2.5 points
M
MCP Pdf Processor
A PDF processing server based on the MCP protocol, supporting text and LaTeX mathematical formula extraction from PDFs, and capable of integrating with Claude for document analysis.
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PDF Forms
A PDF form processing toolkit based on MCP and PyMuPDF
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5.9K
2.5 points
Pdf
The PDF MCP Service is a Python-based PDF processing service that provides a quick start and configuration guide.
Python
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2 points
MCP Ev Assistant Server
A powerful electric vehicle service management server that provides comprehensive services such as charging station location, trip planning, and resource management.
Python
8.2K
2 points
MCP Server Pdfme
MCP Server PDF Processing Service
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2.5 points
MCP Docs Reader
A lightweight MCP server project for loading local PDF files, extracting and chunking content, building semantic search indexes, and sending relevant paragraphs to Claude Desktop for document-based Q&A.
Python
7.7K
2 points
Watsonx Rag MCP Server
This project builds a Retrieval Augmented Generation (RAG) server based on IBM Watsonx.ai, uses ChromaDB for vector indexing, and exposes interfaces through the Model Context Protocol (MCP). The system can process PDF documents and answer questions based on the document content, realizing an intelligent question answering function that combines large language models with specific domain knowledge.
Python
8.9K
2.5 points
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