YouTube Video Summarizer MCP Server

What does “YouTube Video Summarizer” MCP Server do?

The YouTube Video Summarizer MCP (Model Context Protocol) Server is a specialized tool designed to enhance development workflows by enabling AI assistants to fetch and summarize content from YouTube videos. It allows clients, such as Claude, to extract key information including video titles, descriptions, and transcripts directly from YouTube. By bridging external data sources—namely YouTube’s public video metadata and transcripts—with AI agents, this MCP server streamlines tasks such as video summarization and contextual content retrieval, making it easier for developers and users to rapidly access and process video information inside their development environments or AI workflows.

List of Prompts

No explicit prompt templates are listed in the documentation or repository files.

List of Resources

No explicit resources are documented in the repository or README.

List of Tools

No tools are explicitly listed in the README or root-level documentation. The repository structure suggests that summarization and data extraction from YouTube videos are core functionalities, but no formal tool definitions are provided.

Use Cases of this MCP Server

• YouTube Video Summarization: Allows developers and AI agents to retrieve summaries of YouTube videos by extracting titles, descriptions, and transcripts, streamlining content review and understanding.
• Content Research: Enables rapid extraction of video metadata, supporting research tasks and content curation by providing essential video context within development tools.
• Automated Knowledge Extraction: Assists in extracting and summarizing educational or informational videos for knowledge bases or internal documentation.
• AI Chat Integration: Integrates with conversational AI agents (e.g., Claude) to allow answering questions about video content and providing summaries on demand.

How to set it up

Windsurf

1. Ensure prerequisites like Node.js are installed.
2. Locate your Windsurf configuration file.
3. Add the YouTube Video Summarizer MCP Server to the mcpServers object:

   {
     "mcpServers": {
       "youtube-video-summarizer-mcp": {
         "command": "npx",
         "args": ["youtube-video-summarizer-mcp"]
       }
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Verify the MCP server appears in your available servers list.

Claude

1. Ensure Claude supports custom MCP server integration.
2. Access the configuration or plugin management interface.
3. Insert the following JSON snippet:

   {
     "mcpServers": {
       "youtube-video-summarizer-mcp": {
         "command": "npx",
         "args": ["youtube-video-summarizer-mcp"]
       }
     }
   }
   

4. Save and reload Claude.
5. Test by querying a YouTube video summary.

Cursor

1. Install Node.js if not already installed.
2. Open Cursor’s settings or configuration file.
3. Add the MCP server configuration:

   {
     "mcpServers": {
       "youtube-video-summarizer-mcp": {
         "command": "npx",
         "args": ["youtube-video-summarizer-mcp"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Confirm connection to the MCP server.

Cline

1. Prepare your environment with Node.js.
2. Open the relevant Cline configuration file.
3. Add the following JSON configuration:

   {
     "mcpServers": {
       "youtube-video-summarizer-mcp": {
         "command": "npx",
         "args": ["youtube-video-summarizer-mcp"]
       }
     }
   }
   

4. Save your changes and restart Cline.
5. Verify the server integration.

Securing API Keys

If the server requires API keys, use environment variables. Example:

{
  "env": {
    "YOUTUBE_API_KEY": "your-api-key"
  },
  "inputs": {}
}

Reference your secrets in the env section and avoid hardcoding sensitive data.

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-video-summarizer-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-video-summarizer-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server offers a focused and useful capability (YouTube video summarization), but lacks detailed documentation on resources, prompts, and explicit tool definitions. For a public MCP server, more implementation details and examples would improve clarity and usability.

MCP Score

Based on the two tables above, this MCP server receives a score of 4/10—it covers the basics and has a clear use case, but lacks depth and explicit MCP primitives (tools, resources, prompts) that would make it a model example for new MCP server developers.