YouTube DLP MCP Server

The YouTube DLP MCP Server bridges the gap between AI assistants and the vast world of YouTube content without requiring local video downloads. By exposing a set of lightweight, asynchronous tools that pull metadata, subtitles, and comments directly from the YouTube API or via yt‑dlp, it enables developers to enrich conversational agents with up‑to‑date video insights while keeping resource usage minimal. This is particularly valuable for use cases where an AI must quickly answer questions about a video’s popularity, extract captions for content analysis, or surface community sentiment through top comments—all without storing the full media file.

At its core, the server offers three primary capabilities:

• Extract Video Info – Returns comprehensive metadata such as title, view count, like ratio, description, upload date, and channel details. This data is essential for building context around a video, allowing an assistant to discuss relevance or trends.
• Extract Subtitles – Retrieves both manually uploaded subtitles and auto‑generated captions in specified languages. The ability to pull multiple language tracks is useful for multilingual content creation, translation workflows, or accessibility features.
• Extract Top Comments – Gathers the most liked comments (up to 20 by default), including creator badges and like counts. This surface-level social proof can help an AI gauge audience reaction or identify key discussion points.

Each tool is implemented as a non‑blocking async operation, ensuring that the MCP server can handle multiple concurrent requests from an AI assistant without bottlenecking. Proxy support (HTTP/HTTPS/SOCKS) further expands its reach, allowing deployment behind corporate firewalls or in regions with restricted YouTube access.

Real‑world scenarios that benefit from this server include:

• Content strategy assistants that analyze trending videos and surface insights for creators.
• Educational chatbots that fetch lecture subtitles or summarize key points from instructional videos.
• Social listening platforms that monitor comment sentiment to inform marketing decisions.
• Accessibility services that retrieve captions for screen readers or subtitle generators.

By adhering to the standard MCP protocol, the YouTube DLP server integrates seamlessly into existing AI workflows. An assistant can simply invoke the relevant tool, pass a YouTube URL, and receive structured JSON responses that are ready for downstream processing or natural‑language generation. The combination of speed, ease of integration, and rich content extraction makes this MCP server a standout choice for developers looking to unlock YouTube’s informational value without the overhead of media handling.