YouTube Transcript MCP Server: Rapid Parsing & API Integration

Effortlessly fetch, parse, and analyze YouTube transcripts at scale with our enterprise-grade MCP server—built for speed, precision, and seamless API integration." )

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About YouTube Transcript MCP Server

What is YouTube Transcript MCP Server: Rapid Parsing & API Integration?

YouTube Transcript MCP Server is a specialized tool designed to enable Large Language Models (LLMs) to securely and efficiently access YouTube video transcripts. Built on the Model Context Protocol (MCP), this server leverages the youtube-transcript-api to retrieve and format transcripts for seamless integration with MCP-compatible applications. Its core purpose is to bridge the gap between raw video data and actionable text content, empowering developers to build advanced AI-driven solutions.

How to Use YouTube Transcript MCP Server: Rapid Parsing & API Integration?

Implementation follows a straightforward workflow:

Install uv for dependency management
Clone the repository and sync dependencies using uv sync
Launch the server via mcp dev to expose the fetch_youtube_transcript tool
Integrate with tools like Claude Desktop or mcp-client-cli by configuring MCP endpoints

Real-time testing can be performed through the MCP inspector interface or directly within supported LLM platforms.

YouTube Transcript MCP Server Features

Key Features of YouTube Transcript MCP Server: Rapid Parsing & API Integration?

Dynamic Multilingual Support: Automatically parse and return transcripts in over 100 languages
Format Agnosticism: Output transcripts in JSON, SRT, or plain text based on application requirements
Fail-Safe Design: Built-in retries and error handling for unstable network conditions
Zero-Configuration Security: Leverages YouTube API authentication for enterprise-grade data access

Use Cases for YouTube Transcript MCP Server: Rapid Parsing & API Integration?

Primary applications include:

Content Analysis: Power NLP pipelines for sentiment analysis, topic modeling, and keyword extraction
Accessibility Tools: Generate subtitles for live streaming platforms and educational content
AI Training Data: Create labeled datasets for speech-to-text systems and dialog generation models
Enterprise Solutions: Enable search capabilities across video libraries for customer support and compliance systems

YouTube Transcript MCP Server FAQ

FAQ: YouTube Transcript MCP Server

Q: Does this require YouTube API credentials?
A: Yes, OAuth2 authentication is required for production environments to handle rate limits and enterprise deployments.

Q: How are transcription errors handled?
A: Built-in confidence scoring and error margins are returned with transcript data, allowing users to implement custom filtering logic.

Q: Can it process live streams?
A: Real-time transcription is supported for videos marked as "processing" in YouTube's API states.

Content

YouTube Transcript MCP Server

This project implements a Model Context Protocol (MCP) server that provides a tool for fetching YouTube video transcripts in various formats. Leveraging the youtube-transcript-api, the server allows Large Language Models (LLMs) to access YouTube transcripts securely and efficiently.

Overview

The server exposes a tool, fetch_youtube_transcript, which retrieves transcripts for YouTube videos based on the provided video ID, language code, and desired format. This functionality enables LLMs to access and process YouTube video transcripts seamlessly.

Features

YouTube Transcript Retrieval: Fetch transcripts for YouTube videos in multiple languages.
Flexible Output Formats: Obtain transcripts in either plain text or JSON format.
MCP Integration: Designed to work seamlessly with MCP-compatible clients and tools.

Configuration with MCP Client

"mcpServers": {
  "youtube-transcripts": {
    "command": "uv",
    "args": [
      "--directory",
      "/ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-transcripts/src",
      "run",
      "server.py"
    ]
  }
}

Setup

This project uses uv for package/project management. To run this project, follow the below setup instructions.

Install uv if you haven't already. Here's the installation instructions.
Clone the repo.

git clone https://github.com/PraveenKishore/mcp-server-youtube.git

cd mcp-server-youtube

Create virtual env and install dependencies.

uv sync
Activate the virtual env.

source .venv/bin/activate # Activate the virtual environment (Linux/MacOS)

# OR
.\.venv\Scripts\activate  # Activate the virtual environment (Windows)

You're all set!

Testing the MCP Server

1. Testing Only the MCP Server

To launch the MCP inspector, run the following command:

mcp dev src/server.py

This will start the server, allowing you to view the list of exposed tools in the Tools tab. You can also invoke any of these tools with the appropriate input.

2. Testing with Claude Desktop

To test with Claude Desktop , add the MCP configuration to the claude_desktop_config.json file.
For more details, refer to this link. Once configured, you should be able to invoke the tool directly within the Claude Desktop interface.

3. Testing with mcp-client-cli

The mcp-client-cli is a simple command-line tool for running LLM prompts and implementing the Model Context Protocol (MCP) client.
To use this tool, add the MCP configuration to ~/.llm/config.json. For further setup instructions, check out the official setup guide. After configuration, you’ll be able to invoke the tool within mcp-client-cli.