"HTTP streaming video service or platform" matching MCP servers:

Extracts transcripts or spoken captions from multi-platform video links (Bilibili, YouTube, Douyin) and returns concise Markdown summaries or full transcripts, saving tokens by keeping full text out of the conversation.

Enables automated video learning workflows by ingesting video URLs, managing remote GPU ASR transcription, pulling transcripts, generating digest summaries, and searching local notes via MCP tools.

Extracts captions, metadata, and descriptions from YouTube videos to enable AI assistants to summarize their content.

A TypeScript-based MCP server that implements a simple note-taking system using low-level server components and streamable HTTP. It enables users to create, store, and summarize text notes through a set of tools, resources, and specialized prompts.

A simple note-taking MCP server that allows storing and summarizing notes with custom URI schemes and provides functionality to add notes and generate summaries with different detail levels.

Enables AI assistants to analyze and summarize YouTube videos by extracting captions, subtitles, and comprehensive metadata including title, description, and duration in multiple languages.

An MCP server for managing notes, with resources, prompts, and a tool to add notes.

A modular production-ready system that provides specialized agents for math, research, weather, and summarization tasks through a unified MCP toolbox with smart supervisor capabilities.

Self-hosted ML inference MCP server providing embeddings and summarization tools using sentence-transformers and Ollama, with low-latency preloaded models and API-key authentication.

Enables AI agents to redact PII from text, summarize redacted content, and manage custom redaction patterns across multiple languages.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Self-hosted digests of your Telegram channels over MCP: AI summaries, the last digest and raw per-channel messages. Works with OpenAI, Anthropic or local Ollama.

Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.

Allows AI agents to fetch, search, and summarize YouTube video transcripts without an API key, including chapter summaries.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.

Enables AI agents and MCP clients to browse Roomtone meeting notes, add YouTube or pasted-text sources, generate summaries, ask questions answered from transcripts, and retrieve raw transcript text. Recording itself stays in the Roomtone app, so the server only reads and adds to existing meetings.

MCP server that fetches YouTube video transcripts and optionally summarizes them. Supports multiple transcript formats (text, JSON, SRT, WebVTT), multi-language retrieval, and flexible YouTube URL parsing.

Turns plain text or Markdown, web pages, and local PDFs into editable Markdown outlines that can be rendered as standalone, offline-capable interactive HTML mind maps. Works with any MCP client with no API key required, since the connected model writes the final outline from a draft and cleaned source text.

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.