mcp using PyPDF2 to:
• merge-pdfs
• extract-pages
• search-pdfs
• merge-pdfs-ordered (merge in user spec. order)
• find-related-pdfs (regex extracted text for related PDF files)
A comprehensive tool server for reading, merging, and extracting content from PDF files via local paths or direct URLs. It enables metadata retrieval, regex searching, and page-specific text extraction with built-in caching and workspace-restricted security.
MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.
Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
A modular backend that connects language models to real-world tools like Gmail and Google Docs via MCP server abstractions, enabling agentic workflows with LLM reasoning and secure OAuth integration.
Enables users to convert text into high-quality audio by accessing the OpenAI Text-to-Speech API. It supports customizable model selection and voice options for synthesized speech generation via the MCP protocol.
A comprehensive MCP server for Linux automation operations management, providing 88 tools across 8 categories for file, system, process, network, compression, and package management via SSH connections.
Enables AI-powered extraction and analysis of PDF documents with 40+ specialized tools for text, tables, images, layout analysis, security assessment, and document intelligence. Supports both text-based and scanned PDFs with OCR capabilities.