YouTube to LinkedIn MCP Server

Overview

The YouTube to LinkedIn MCP Server streamlines the transformation of video content into polished, shareable LinkedIn posts. By automating transcript extraction, summarization, and post generation, the server removes manual editing overhead for content creators, marketers, and knowledge workers who want to repurpose YouTube videos into professional social media updates. The result is a ready‑to‑publish draft that can be tweaked or approved before posting, saving time and ensuring consistency across a brand’s LinkedIn presence.

At its core, the server exposes four main MCP endpoints. First, it pulls a video’s transcript—either via the YouTube Data API or by parsing the on‑screen text—to produce a structured text payload. Second, it feeds that transcript into an OpenAI GPT model to generate concise summaries, allowing users to set tone, audience, and length constraints. Third, the summary is transformed into a LinkedIn‑style post that includes customizable voice (first‑person or third‑person), hashtags, and optional calls to action. Finally, an output formatting endpoint can wrap the content in markdown or plain text for direct copy‑paste into LinkedIn’s editor. Each step is designed to be stateless, making the server easy to scale and integrate into larger AI workflows.

Developers benefit from a clean FastAPI architecture that exposes these capabilities as lightweight HTTP services. The modular design means you can plug the server into an existing chatbot or workflow automation tool without touching its internals. By exposing a standardized MCP interface, the server can be called from any AI assistant that understands the protocol—whether it’s Claude, GPT‑4o, or a custom in‑house model. This enables seamless content creation pipelines: an AI assistant can request a transcript, summarize it, generate a post draft, and even push the final text back to the user for review.

Real‑world scenarios include content marketing teams that curate weekly video series, personal brands who want to share insights from their talks, or corporate knowledge managers who need to disseminate training videos on LinkedIn. The server’s ability to honor tone, audience, and length parameters ensures that each post aligns with brand guidelines or personal style. Moreover, the optional inclusion of a YouTube Data API key allows deeper metadata extraction (e.g., channel name, upload date), which can be used to enrich the post with contextual information.

Unique advantages of this MCP server lie in its end‑to‑end automation and AI‑driven customization. By combining transcript extraction, GPT summarization, and post generation into a single protocol‑compliant service, it eliminates the need for multiple third‑party tools or manual copy‑pasting. The server’s containerized deployment on platforms like Smithery further simplifies scaling, while the clear separation of concerns in its API design makes it straightforward to extend—for example, adding support for other social networks or integrating advanced sentiment analysis. Overall, the YouTube to LinkedIn MCP Server offers a powerful, developer‑friendly bridge between video content and professional social media engagement.