This YouTube research MCP server, developed by an unnamed author, integrates with the YouTube API to perform aggregated video searches. Built with TypeScript and leveraging the Model Context Protocol SDK, it uses sampling to generate search queries from user topics, fetches both short and long videos, and compiles results with metadata. The server implements sequential thinking, logging each step as separate 'thoughts' for operational visibility. By abstracting YouTube search complexities, it enables AI assistants to efficiently gather video content on specified topics. This implementation is particularly useful for content curation, trend analysis, and research tasks, facilitating use cases such as creating themed playlists, analyzing video popularity across topics, or gathering educational resources.
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This MCP server may use standard commands or have its functionality documented in the README. Check the Setup or README tabs for more information.
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