A Model Context Protocol server for managing PDF documents with vector search capabilities
Until 2026-09-26 this record combined several servers published from one repository. The others now have records of their own: pdfkb-mcp. Checks from before that date include them.
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# PDF Knowledgebase MCP Server A powerful Model Context Protocol (MCP) server that transforms your PDF and Markdown document collection into an intelligent, searchable knowledge base. Built for seamless integration with Claude Desktop, VS Code, Continue, and other MCP-enabled AI assistants. ## Description **pdfkb-mcp** processes your documents using advanced PDF parsing, creates semantic embeddings, and provides sophisticated search capabilities through the Model Context Protocol. Whether you're managing research papers, technical documentation, or business reports, pdfkb-mcp makes your document collection instantly searchable and accessible to your AI assistant. ### Motivation I built pdfkb-mcp because I needed a way to efficiently index and search through hundreds of semiconductor datasheets and technical documents. Traditional file search wasn't sufficient—I needed semantic understanding, context preservation, and the ability to ask complex questions about technical specifications across multiple documents. This tool has transformed how I work with technical documentation, and I'm sharing it so others can benefit from intelligent document search in their workflows. ## ✨ Features ### 🤖 **Intelligent Document Processing** - **Multiple PDF Parsers**: PyMuPDF4LLM (fast), Marker (balanced), Docling (tables), MinerU (academic), LLM (complex layouts) - **Markdown Support**: Native processing of .md and .markdown files with metadata extraction - **Smart Chunking**: LangChain, semantic, page-based, and unstructured chunking strategies - **Background Processing**: Non-blocking document processing with intelligent caching ### 🔍 **Advanced Search & AI** - **Hybrid Search**: Combines semantic similarity with keyword matching (BM25) for superior results - **AI Reranking**: Qwen3-Reranker models improve search relevance by 15-30% - **Local & Remote Embeddings**: Privacy-focused local models or high-performance API-based options - **Document Summarization**: Auto-gene…
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