"Methods to Extract Text Content from Videos" matching MCP servers:

A Model Context Protocol server that converts Markdown documents to plain text with flexible options and dual protocol support.

Enables AI agents to transcribe Instagram Reels into text by providing a tool that accepts a Reel URL and returns the speech transcript along with metadata such as author and duration.

Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.

Converts natural language queries into valid GraphQL queries and executes them against GraphQL APIs. Includes schema introspection, query validation, execution with authentication, and query history tracking.

Extract content from URLs, documents, videos, and audio files using intelligent auto-engine selection. Supports web pages, PDFs, Word docs, YouTube transcripts, and more with structured JSON responses.

A secure database query service built on FastMCP framework that allows users to query MySQL databases using natural language, with comprehensive permission management and security controls.

MCP server for Averra Extract — lets AI agents like Claude, Cursor, and ChatGPT convert any webpage into clean, LLM-ready Markdown.

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

Lets AI assistants manage a self-hosted content hub: create, edit, publish, and update articles across nine platforms, refresh account article lists, and sync changes through natural language.

Enables AI agents to analyze a piece of content across 11 dimensions and receive ranked, scored recommendations among six cross-media output formats (PPT, infographic, comic, short video script, newsletter, reusable skill), then generate dispatch prompts for the chosen format. It also exposes the format catalog and profile schema as read-only tools, supporting batch routing of an entire content library.

Enables users to expose arbitrary HTTP APIs as configurable MCP tools by defining tool specifications in a JSON configuration file.

Enables clients to interact with a bundled documentation corpus through MCP tools and resources over HTTP or stdio, and provides a trajectory evaluation harness for grading agent performance.

Enables reading and verifying C2PA Content Credentials from images and videos through both embedded manifests and invisible watermarks. Supports direct filesystem access, URL checking, and provides structured provenance data including creator identity, AI generation info, and content authenticity.

A line-oriented text file editor. Optimized for LLM tools with efficient partial file access to minimize token usage.

Multi-platform content distribution — draft posts, repurpose content, generate carousels, schedule, analyze performance, create threads. 7 MCP tools.

An MCP server that uses AI to generate structured Asana project plans from diverse sources like text, PDFs, and meeting transcripts. It enables users to preview, generate, and directly sync detailed task lists, subtasks, and dependencies into Asana.

MCP server that fetches raw HTML content from a given URL to provide web context to LLMs.

A specialized Model Context Protocol server that enhances AI-assisted medical learning by connecting Claude Desktop to PubMed, NCBI Bookshelf, and user documents for searching, retrieving, and analyzing medical education content.

Provides SEO audits, content analysis, and domain overviews through an MCP server, including on-page, technical, and authority metrics with built-in browser rendering and anti-bot handling.

MCP server exposing content contract validators (SEO.md, PRODUCTHUNT.md, LINKEDIN.md) as tools so AI agents can verify generated content against concrete rules without maintaining linters. It provides linting and JSON-LD export tools with config and content passed as parameters, not file paths.