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

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

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.

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.

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

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.

Enables interactive chat with AI models, including document retrieval and command-based prompts via the Model Control Protocol.

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

Exposes semantic search over a folder of indexed PDF guides as a tool that Claude Code or any MCP client can call. Returns the closest matching passages with relevance scores, source file and page citations, so the client can answer questions about the guides while knowing when nothing relevant was found.

A local RAG MCP server for PDF development experience, enabling document ingestion, semantic search, and Q\&A with source citations using TF-IDF and cosine similarity.

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 (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 coding agents to search and read local Markdown knowledge bases generated by Agent Docs for Markdown, across single or multiple workspaces, including link/backlink discovery and index status checks.

Enables AI assistants to search, read, and navigate a folder of Markdown notes as a knowledge base, with full-text search, backlinks, tags, metadata, and note resources for conversation attachment.

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

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.

Enables local LLMs to match lesson summaries or syllabus topics against PDF textbooks and manuals, returning the exact page ranges relevant to each topic along with brief explanations of why they matter. Runs entirely offline via Ollama embeddings and reasoning plus Tesseract OCR for scanned or image-only pages, so multi-hundred-page documents can be searched privately without API keys or cloud credits.

Enables agents to search local component-manual corpora via hybrid semantic, full-text, and substring retrieval with table recognition and OCR fallback for scanned pages. Provides tools for literal/regex grepping, reading single or multiple pages, navigating bookmarks and outlines, listing categories and indexed files, and checking index and dependency status.