"Methods to Extract Data from Files" matching MCP servers:

MCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

An MCP server that provides a tool to extract text content from local PDF files, supporting both standard PDF reading and OCR capabilities with optional page selection.

Enables AI language models to interact with YouTube content through a standardized interface, providing tools for retrieving video information, transcripts, channel analytics, and trend analysis.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

Enables LLMs to interact with Fathom.video API for managing meeting recordings, retrieving transcripts and AI-generated summaries, searching meeting content, and accessing analytics and team data.

Integrates Vale prose linting into AI coding assistants, enabling users to check text files for style and grammar issues using Vale's powerful linting engine. Provides automated style feedback with smart configuration discovery and rich formatted results.

Enables natural language interaction with local .docx files, allowing users to find, read, search, and summarize Word documents using friendly names and location hints.

MCP server for HumanPen that lets AI agents work on real documents (.docx, .pptx, .pdf) — humanizing content to lower AI-detection scores, converting citations, condensing, and translating while preserving formatting, tables, images, and citations.

Enables transcription, summarization, and action item extraction from audio files on your Mac using MacWhisper and Claude Desktop, all locally without any cloud APIs.

An MCP server that converts PDF files to Markdown format using AI sampling capabilities, supporting both local files and URLs with incremental conversion features.

Enables agents to parse resumes and job postings into structured OTP/OJP skeletons, validate them against JSON Schema, and extract agent-friendly summaries, all without LLM calls.

MCP server that compresses markdown files into caveman-style prose, cutting token usage by 65% while preserving code and structure.

Enables LLM agents to search arXiv, download papers, parse PDFs into structured sections, and extract key findings using client-side LLMs. Features persistent caching and layout-aware PDF extraction.

An MCP server for intelligent YouTube video analysis that provides token-optimized summaries, sentiment analysis, and entity extraction from transcripts. It enables AI assistants to perform video reporting, channel monitoring, and comprehensive YouTube searches through structured data tools.

Enables reading and extracting content from PDF documents including text (as Markdown), images, tables, and metadata from both local files and URLs, with OCR support for scanned documents.

Transforms chat conversations with AI into structured markdown summaries and automatically saves them to organized files in your notes directory. Supports different summary styles, handles large conversations through chunking, and provides tools to manage your saved summaries.

Uses yt-dlp to download subtitles from YouTube and connects it to claude.ai via Model Context Protocol.

Provides fast, multilingual spell-checking for text and files, featuring syntax-aware parsing that intelligently checks only comments and strings in code. It supports over 15 languages and enables directory-wide scanning with custom dictionary management.

Search and retrieve news articles and trending keywords from Google News and Google Trends. Summarize articles and extract keywords using optional NLP and LLM Sampling to get concise insights.