YT-DLP MCP Server

Overview

The yt‑dlp‑mcp server bridges the gap between large language models and real‑world multimedia content by exposing a suite of tools that let an AI assistant interact with video platforms such as YouTube, Facebook, and TikTok. It solves a common pain point for developers: retrieving rich media data—metadata, subtitles, audio, and video itself—without having to write custom scrapers or deal with the complexities of each platform’s API. By wrapping yt‑dlp behind the Model Context Protocol, developers can embed media retrieval directly into conversational flows, enabling assistants to answer questions about video content, provide downloadable resources, or analyze subtitles for sentiment and topic modeling.

At its core, the server offers a collection of high‑level operations. search_videos lets an assistant surface relevant clips based on user queries, while get_video_metadata and get_video_metadata_summary return structured or human‑readable descriptions of any video URL. Subtitles are handled through list_subtitle_languages, download_video_subtitles, and download_transcript, giving LLMs access to clean, timestamp‑free text that can be fed into downstream NLP tasks. For users who need the actual media, download_video and download_audio provide resolution‑controlled downloads directly to a local Downloads folder, supporting optional trimming via start and end times. The server also respects privacy by performing direct downloads without external tracking.

Developers find these capabilities invaluable when building AI‑powered media assistants. For instance, a learning platform can let students search for tutorial videos, retrieve transcripts for note‑taking, and download audio clips for offline study. Content creators can automate the collection of competitor videos’ metadata or subtitles to inform strategy. A customer support bot could fetch and summarize product demo videos on demand, improving response times.

Integration is straightforward: any MCP‑compatible LLM (such as Dive or Claude) can invoke the server’s tools by name, passing JSON‑formatted arguments. The server handles all communication with yt‑dlp and returns results in a consistent structure, freeing developers from handling platform quirks or parsing raw HTML. The privacy‑focused design further appeals to applications that must avoid third‑party tracking.

Unique advantages of yt‑dlp‑mcp include its comprehensive subtitle support—auto‑generated captions and multiple language options—and the ability to trim videos on download, saving bandwidth. The server’s modular toolset means developers can cherry‑pick only the operations they need, keeping models lightweight while still offering rich media functionality. Overall, this MCP server empowers AI assistants to seamlessly fetch, analyze, and deliver video content in real‑time, unlocking a wide range of interactive media applications.