YouTube Transcript Generator Using Model Context Protocol in Just 5 Lines of Code

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Ever wanted to get the transcript of a YouTube video without subscribing to expensive services or wrestling with complicated APIs? In this blog post, I’ll show you how to build a YouTube transcript generator using the Model Context Protocol (MCP) in just 5 lines of code, based on the excellent mcp-server-youtube-transcript project.

What is Model Context Protocol?

Before diving in, let’s briefly cover what Model Context Protocol (MCP) is. MCP is an open standard that allows AI assistants like Claude to use external tools. It provides a structured way for AI models to communicate with specialized services, expanding their capabilities beyond their training data.

Think of MCP as a universal adapter that connects AI assistants to a world of tools and services—like our YouTube transcript extractor!

The 5-Line Solution

Here’s all you need to connect to the YouTube transcript service with MCP:

import { MCPClient } from '@modelcontextprotocol/sdk/client';
const client = new MCPClient();
await client.connect(new WebSocketClientTransport('wss://your-mcp-server.com'));
const result = await client.callTool('get_transcript', { url: 'https://www.youtube.com/watch?v=KRw4vVX9aHU', lang: 'en' });
console.log(result.content[0].text); // Output the transcript text

That’s it! Five lines to get the complete transcript of any YouTube video.

How It Works

Let’s break down what’s happening:

Import the MCP client library: This gives us the tools to connect to MCP servers.
Create an MCP client: Initialize a new client instance.
Connect to the server: Establish a connection to the MCP server that hosts the YouTube transcript tool.
Call the transcript tool: Request a transcript for a specific video URL in your preferred language.
Process the result: Extract and use the transcript text from the response.

The magic happens behind the scenes in the MCP server, which:

Extracts the YouTube video ID from various URL formats
Fetches the captions using the YouTube captions scraper
Formats the transcript into readable text
Returns the result with metadata about the video

Using with Claude or Other AI Assistants

The real power comes when you integrate this with AI assistants like Claude. Once you’ve set up the MCP server, you can simply ask:

Can you summarize this YouTube video? https://youtu.be/ODaHJzOyVCQ

Claude will automatically:

Detect the YouTube URL
Use the MCP server to fetch the transcript
Read and analyze the content
Provide you with a summary based on the actual video content

Setting Up Your MCP Server

You have several options to set up the YouTube transcript MCP server:

Option 1: Using Smithery (Easiest)

npx -y @smithery/cli install @kimtaeyoon83/mcp-server-youtube-transcript --client claude

Option 2: Using mcp-get

npx @michaellatman/mcp-get@latest install @kimtaeyoon83/mcp-server-youtube-transcript

Option 3: Manual Setup for Claude Desktop

Add this to your Claude Desktop configuration:

{
  "mcpServers": {
    "youtube-transcript": {
      "command": "npx",
      "args": ["-y", "@kimtaeyoon83/mcp-server-youtube-transcript"]
    }
  }
}

Beyond Simple Transcripts

While getting the raw transcript is useful, you can do much more:

Video summarization: Generate concise summaries of long videos
Content analysis: Extract key points, topics, and themes
Language translation: Get transcripts in different languages
Educational study guides: Create structured notes from lecture videos
Accessibility: Make video content accessible to those who prefer reading

Technical Deep Dive

For those interested in how the MCP server works under the hood, it’s elegantly simple. The core functionality is implemented in a YouTubeTranscriptExtractor class that:

Extracts video IDs from various URL formats (full URLs, shortened URLs, direct IDs)
Makes requests to YouTube’s caption API
Formats the response into clean, readable text

The MCP layer then exposes this functionality through a standardized interface that AI assistants and other applications can easily consume.

Why Use MCP Instead of Direct API Calls?

You might wonder why use MCP instead of making direct API calls. The benefits include:

Plug-and-play with AI assistants: No coding required for end users
Error handling: Robust error messages for various failure scenarios
Standardized interface: Works consistently across different AI systems
Separation of concerns: Server handles the complex API interactions
Maintainability: Updates to the YouTube API are handled at the server level

Conclusion

The Model Context Protocol represents a significant advancement in how we interact with AI assistants. By extending AI capabilities through standardized tool interfaces, we can solve real-world problems with minimal code.

This 5-line YouTube transcript generator showcases how powerful this approach can be. Whether you’re a developer looking to build applications on top of AI assistants or an end user who wants to extract information from videos, MCP provides an elegant solution.

The next time you need to reference content from a YouTube video, remember that you’re just 5 lines of code away from having the entire transcript at your fingertips!