YouTube MCP Server Integration

What does “YouTube” MCP Server do?

The YouTube MCP Server is a Model Context Protocol (MCP) server implementation that enables AI language models and assistants to interact with YouTube content programmatically through a standardized interface. By connecting the YouTube MCP Server to your AI workflow, you can automate video management, access advanced analytics, retrieve transcripts, and manage channels and playlists directly via API calls. This integration empowers developers and AI agents to perform tasks such as searching for videos, extracting detailed metadata, managing playlists, and analyzing channel statistics, all without leaving their development environment. The server enhances productivity by streamlining access to YouTube’s wealth of data and services, making it a powerful tool for building content-driven applications, automating content moderation, and enabling rich AI-powered media workflows.

List of Prompts

No prompt templates are documented in the repository.

List of Resources

No explicit MCP resources are documented in the repository.

List of Tools

No direct tool definitions found in server.py or similar files. The following features are implied by the README and may be implemented as tools:

• Get video details: Retrieve title, description, duration, etc.
• List channel videos: Fetch a list of videos for a specific channel.
• Get video statistics: Access views, likes, and comments count.
• Search videos: Find videos across YouTube by keyword or filter.
• Retrieve video transcripts: Fetch transcripts, captions, and search within them.
• Get channel details and statistics: Access metadata and analytics for channels.
• List channel playlists and playlist items: Manage and explore playlists.
• Get playlist video transcripts: Retrieve transcripts for all videos in a playlist.

Use Cases of this MCP Server

• Automated Video Analytics: Developers can use the server to fetch view, like, and comment statistics to monitor video performance and gain actionable insights.
• Content Moderation and Management: The server allows tools or agents to list channel videos, retrieve details, and manage playlists, facilitating automation of content curation and moderation.
• Transcript Retrieval and Search: Enables AI agents to extract and analyze video transcripts for accessibility, summarization, or content search purposes.
• Channel and Playlist Exploration: Developers can programmatically list channel playlists, get details, and explore playlist items, empowering content management and recommendation systems.
• Advanced Search and Filtering: AI tools can leverage the server to search YouTube videos and playlists for specific topics, trends, or compliance checks, streamlining research and discovery.

How to set it up

Windsurf

No Windsurf-specific setup instructions are provided in the repository.

Claude

1. Install the package:

   npm install -g zubeid-youtube-mcp-server
   

2. Edit your Claude Desktop configuration file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows).
3. Add the YouTube MCP Server configuration:

   {
     "mcpServers": {
       "zubeid-youtube-mcp-server": {
         "command": "zubeid-youtube-mcp-server",
         "env": {
           "YOUTUBE_API_KEY": "your_youtube_api_key_here"
         }
       }
     }
   }
   

4. Save the configuration and restart Claude Desktop.
5. Verify the server is running and accessible from Claude.

Alternative using NPX:

{
  "mcpServers": {
    "youtube": {
      "command": "npx",
      "args": ["-y", "zubeid-youtube-mcp-server"],
      "env": {
        "YOUTUBE_API_KEY": "your_youtube_api_key_here"
      }
    }
  }
}

Cursor

No Cursor-specific setup instructions are provided in the repository.

Cline

No Cline-specific setup instructions are provided in the repository.

Securing API Keys

It is recommended to store your YouTube API key using environment variables in the configuration. Example:

{
  "mcpServers": {
    "zubeid-youtube-mcp-server": {
      "command": "zubeid-youtube-mcp-server",
      "env": {
        "YOUTUBE_API_KEY": "your_youtube_api_key_here"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "youtube-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “youtube-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the information provided and the two tables, the YouTube MCP Server is well-documented for installation and usage on Claude, with clear instructions for securing API keys and a strong feature set. However, it lacks explicit documentation for prompt templates, resource primitives, and sampling/roots support, limiting its extensibility for advanced MCP workflows.

Our opinion

Overall, this MCP server is a strong candidate for YouTube content and analytics integration, especially for Claude users. Its lack of prompt/resource documentation and missing explicit sampling/roots support are notable downsides, but it remains quite useful for practical video management and analytics workflows.

MCP Score: 7/10

MCP Score