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youtube_research_mcp

MCP.Pizza Chef: danhilse

The YouTube Research MCP Server is a specialized MCP server designed to facilitate advanced video search and aggregation on YouTube. It leverages MCP's sequential thinking to log each reasoning step as distinct thoughts, enhancing transparency and traceability of operations. The server uses MCP's sampling mechanism to generate diverse search queries based on user topics, then integrates with the YouTube API to fetch both short and long videos. Results are aggregated and returned with detailed metadata, making it ideal for research, content discovery, and data analysis workflows that require structured, multi-step video retrieval and contextual understanding.

Use This MCP server To

Aggregate YouTube videos by topic with metadata Generate diverse search queries for video research Log sequential reasoning steps during video search Fetch both short and long YouTube videos Support content discovery with structured video data

README

My YouTube MCP Server

This MCP server integrates:

  • Sequential Thinking: Logs each step as separate "thoughts" to provide visibility into the server's operations.
  • YouTube Aggregated Search:
    • Sampling: Uses MCP's sampling mechanism to generate search queries based on user topics.
    • YouTube API Integration: Fetches short and long videos for each search query.
    • Result Aggregation: Compiles and returns video IDs with metadata.

Setup Instructions

  1. Clone the Repository

    git clone https://github.com/yourusername/my-youtube-server.git
cd my-youtube-server

youtube_research_mcp FAQ

How do I set up the YouTube Research MCP Server?
Clone the repository from GitHub, install dependencies, and configure your YouTube API key as per the setup instructions.
Does the server support fetching both short and long videos?
Yes, it integrates with the YouTube API to retrieve videos of varying lengths based on search queries.
How does sequential thinking improve the server's operation?
Sequential thinking logs each step as separate thoughts, providing transparency and traceability of the search and aggregation process.
Can I customize the search queries generated by the server?
Yes, the server uses MCP's sampling mechanism to generate queries, which can be tailored to specific user topics or research needs.
What metadata is returned with the video results?
The server aggregates video IDs along with metadata such as titles, descriptions, durations, and other relevant YouTube video details.
Is the server compatible with multiple LLM providers?
While the server focuses on YouTube integration, it is designed to work within MCP ecosystems that support models like OpenAI, Claude, and Gemini.
How does the server handle API rate limits?
The server implementation should include mechanisms to respect YouTube API rate limits, such as query sampling and request pacing, to ensure stable operation.
Can this MCP server be integrated into larger AI workflows?
Yes, it is designed as a modular MCP server that can be plugged into broader AI systems for enhanced video research and content discovery.