YouTube Video Summarizer MCP
The YouTube Video Summarizer MCP acts like a smart bridge between video content and AI assistants, making it easy to understand any YouTube video without having to watch it. By simply providing a video link, the tool automatically gathers the title, description, and every word spoken in the video through ā¦
About this Protocol
How to Use
1. Installation
To install the YouTube Video Summarizer MCP server globally, run the following command:
npm install -g youtube-video-summarizer-mcp
2. Configuration
Add the following configuration to your MCP client settings file (e.g., claude_desktop_config.json):
{
"mcpServers": {
"youtube-video-summarizer": {
"command": "youtube-video-summarizer",
"args": []
}
}
}
3. Available Tools
Once integrated, the following tools become available to the AI assistant:
- get-video-info-for-summary-from-url: Extract video information and captions from a YouTube URL.
- get-video-captions: Get captions/subtitles for a specific video.
- get-video-metadata: Retrieve comprehensive video metadata (title, description, duration).
4. Example Prompts
You can use natural language to interact with the server through your MCP client:
- "Can you summarize this YouTube video: https://youtube.com/watch?v=VIDEO_ID"
- "What are the main points from this video's captions?"
- "Extract the key information from this YouTube link"
Use Cases
Use Case 1: Accelerated Research and Competitive Analysis
Problem: Researchers and market analysts often need to digest information from dozens of long-form videos (webinars, product reviews, or conference talks). Manually watching hours of footage to find specific insights is extremely time-consuming.
Solution: This MCP allows the AI to act as a research assistant that "watches" the videos for you. It can pull the full transcripts and metadata across multiple URLs to identify key trends, compare features, or summarize the consensus among experts without the user ever hitting the play button.
Example: A user provides three links to different tech reviews and asks: "Based on these three videos, what are the recurring complaints about the new laptop's battery life and cooling system?"
Use Case 2: Automated Content Repurposing
Problem: Content creators often want to turn their YouTube videos into blog posts, newsletters, or social media threads, but transcribing the video and organizing the thoughts manually is a tedious secondary task.
Solution: By using the get-video-captions and get-video-metadata tools, an AI can instantly access the raw spoken content and the creator's original description. It can then restructure that data into high-quality written formats while maintaining the original tone and key points.
Example: A creator gives a link to their 20-minute cooking tutorial and tells the AI: "Extract the exact ingredients list from this video and write a 500-word SEO-friendly blog post explaining the cooking process step-by-step."
Use Case 3: Step-by-Step Technical Documentation
Problem: Developers and engineers often follow video tutorials for complex software setups or coding tasks. It is difficult to copy-paste code from a video, and constantly pausing/rewinding to find a specific command is frustrating.
Solution: The MCP can fetch the transcript of a technical tutorial. The AI can then parse this transcript to find specific shell commands, code snippets, or configuration steps, presenting them in a clean, searchable Markdown document.
Example: "Iām following this AWS deployment tutorial: [URL]. Can you extract every CLI command mentioned in the video and list them in the order they should be executed?"
Use Case 4: Language Translation and Global Intelligence
Problem: Valuable information is often locked in videos recorded in languages the user doesn't speak fluently. While YouTube has auto-translate, it's difficult to ask deep questions about the content in a different language.
Solution: Since the MCP supports language-specific caption extraction, an AI can fetch the transcript in the original or auto-generated language and then translate/summarize the content in the user's native tongue, allowing for cross-lingual knowledge sharing.
Example: A user provides a link to a Japanese tech keynote and asks: "Extract the captions from this video and provide a summary in English focusing on the new AI features they announced."
Use Case 5: Quick "Pulse Check" for Long Live Streams
Problem: After a 3-hour live stream (like a town hall, gaming stream, or political debate), users want to know if specific topics were mentioned without scrubbing through a massive timeline.
Solution: The AI can use the get-video-info-for-summary-from-url tool to ingest the entire transcript and metadata. The user can then query the transcript for specific keywords or sentiment changes throughout the long duration.
Example: "In this 4-hour city council meeting video, at what point did they start discussing the new bike lane proposal, and what was the final decision made?"