"Tools for converting Word and PDF documents to Markdown format" matching MCP servers:

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.

An MCP server that enables users to download webpages as markdown files using r.jina.ai service, with features for configurable download directories and automatic date-stamped filenames.

Converts any URL to clean, LLM-ready Markdown by removing ads, navigation, and other clutter, using Mozilla Readability and Turndown.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

This MCP server enables AI agents to view PDFs as accessible HTML with bounding-box citations, and provides tools for layout-aware parsing, schema extraction, cross-document Q&A, and PDF rendering.

A Model Context Protocol server that enables AI agents to read, search, and extract content from PDF files.

A Model Context Protocol (MCP) server that provides powerful RAG (Retrieval-Augmented Generation) capabilities for PDF documents. This server uses ChromaDB for vector storage, sentence-transformers for embeddings, and semantic chunking for intelligent text segmentation.

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.

Enables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.

Provides semantic search over markdown documentation using RAG, allowing natural language queries and integration with MCP clients.

Enables managing and searching markdown notes with semantic search, question answering, and note generation, and provides an MCP server for GitHub Copilot integration.

Enables agents to extract clean structured JSON tables from any PDF with per-cell citations to the source page.

A Model Context Protocol server that provides RAG capabilities for markdown documents using Qdrant for vector storage and Ollama for embeddings, enabling semantic search and document ingestion directly from Cursor IDE.

Crawl entire websites, sitemaps, and documentation portals into clean, LLM-ready Markdown without ads, cookie popups, or noise for RAG pipelines and AI agents.

Enables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.

Rust-powered PDF toolkit over MCP: create, read, and analyze PDFs; extract text and entities for RAG; convert to Markdown; split/merge/rotate/reorder pages; manage form fields and annotations; encrypt documents. Runs locally via uvx oxidize-mcp.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables a persistent local library for mathematical PDFs with cached LaTeX extraction, full-text search in English and Greek, and page/image retrieval over MCP.

Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.