search-webproductivity

YouTube to Sheets

rickyyy1116

by rickyyy1116

YouTube to Sheets automates video data collection in Google Sheets—perfect for content creators, marketers, and research

Integrates YouTube search with Google Sheets to automate video data collection and analysis for content creators, marketers, and researchers.

github stars

11

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Automatic spreadsheet populationRequires YouTube API key setup

best for

  • / Content creators researching trending topics
  • / Marketing teams analyzing competitor videos
  • / Researchers collecting YouTube data for analysis

capabilities

  • / Search YouTube videos using API
  • / Save video results to Google Sheets automatically
  • / Configure search parameters and result limits
  • / Extract video metadata including title and channel info

what it does

Searches YouTube videos and automatically saves results (title, URL, channel, publish date) to Google Sheets for easy data collection and analysis.

about

YouTube to Sheets is a community-built MCP server published by rickyyy1116 that provides AI assistants with tools and capabilities via the Model Context Protocol. YouTube to Sheets automates video data collection in Google Sheets—perfect for content creators, marketers, and research It is categorized under search web, productivity.

how to install

You can install YouTube to Sheets in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

YouTube to Sheets is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

YouTube to Google Sheets MCP Server

This MCP server provides functionality to search YouTube videos and automatically save the results to Google Sheets. It's designed to work with Claude and other AI assistants that support the Model Context Protocol.

English | 日本語

Features

  • Search YouTube videos using the YouTube Data API v3
  • Save search results to Google Sheets automatically
  • Configurable search parameters (query, max results)
  • Results include video title, URL, channel name, and publish date

Installation

npm install @rikukawa/youtube-sheets-server

Prerequisites

  1. YouTube Data API v3 Setup:

  2. Google Sheets API Setup:

    • In the same project, enable Google Sheets API
    • Create a service account
    • Download the service account key (JSON format)
    • Share your target Google Sheet with the service account email

Configuration

Add the server to your MCP settings file:

{
  "mcpServers": {
    "youtube-sheets": {
      "command": "node",
      "args": ["path/to/youtube-sheets-server/build/index.js"],
      "env": {
        "YOUTUBE_API_KEY": "your-youtube-api-key",
        "SPREADSHEET_ID": "your-spreadsheet-id"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Usage

“Ask the AI assistant to ‘search for YouTube videos with “ChatGPT usage” and retrieve 10 videos’ and try using it in that way.”

Output Format

The tool will save the following information to your Google Sheet:

  • Video Title
  • Video URL
  • Channel Name
  • Publish Date

License

MIT

Author

Riku Kawashima

Repository

GitHub Repository

NPM Package

@rikukawa/youtube-sheets-server

FAQ

What is the YouTube to Sheets MCP server?
YouTube to Sheets is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for YouTube to Sheets?
This profile displays 74 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Web Research & Information Gathering

Fetch and extract information from websites automatically

Example

Research competitor pricing, scrape product reviews, monitor news mentions

Automate 5-10 hours/week of manual web research

Content Monitoring & Alerts

Track website changes, new content, price updates

Example

Monitor competitor blog for new posts, track stock availability, watch for pricing changes

Stay informed without manual checking, never miss important updates

Data Extraction & Aggregation

Extract structured data from multiple websites

Example

Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data

Build datasets 100x faster than manual copying

API-less Integration

Interact with services that don't offer APIs

Example

Check form submissions, validate website functionality, test user flows

Automate interactions with any website, even without API

Implementation Guide

Prerequisites

  • Claude Desktop or Cursor with MCP support
  • Understanding of web scraping ethics and robots.txt
  • Rate limiting awareness to avoid overwhelming target sites
  • Knowledge of legal restrictions on data collection

Time Estimate

20-40 minutes including configuration and testing

Installation Steps

  1. 1.Install web automation MCP server via npm or pip
  2. 2.Configure allowed domains and rate limits in MCP config
  3. 3.Test with simple fetch: 'Get content from example.com'
  4. 4.Progress to extraction: 'Extract all product prices from this page'
  5. 5.Set up monitoring: 'Check this URL daily for changes'
  6. 6.Parse structured data: 'Create CSV from this table'
  7. 7.Respect robots.txt and rate limits always

Troubleshooting

  • 403 Forbidden: Website blocks bots—respect their wishes, use official API instead
  • Rate limit errors: Slow down requests, add delays between fetches
  • Stale data: Target site changed HTML structure—update selectors
  • Timeout errors: Site is slow or blocking—increase timeout, try different user agent
  • JavaScript-rendered content: Use headless browser MCP servers for dynamic sites

Best Practices

✓ Do

  • +Check robots.txt and respect crawl rules
  • +Rate limit requests: 1-2 requests/second maximum
  • +Use official APIs when available instead of scraping
  • +Identify your bot with descriptive user agent
  • +Cache results to minimize repeated requests
  • +Handle errors gracefully with retries and fallbacks
  • +Validate extracted data for accuracy

✗ Don't

  • Don't scrape sites that explicitly forbid it (robots.txt, ToS)
  • Don't overwhelm servers with rapid requests—use rate limiting
  • Don't scrape personal data without consent and legal basis
  • Don't ignore copyright on extracted content
  • Don't assume HTML structure is stable—handle changes
  • Don't use scraped data for commercial purposes without permission

💡 Pro Tips

  • Use CSS selectors or XPath for robust data extraction
  • Set up monitoring alerts for extraction failures (structure changed)
  • Implement exponential backoff for retries on failures
  • Store raw HTML for reprocessing if extraction logic changes
  • Combine with data analysis tools for insights from extracted data
  • Consider using official APIs or RSS feeds as more stable alternatives

Technical Details

Architecture

MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.

Protocols

  • HTTP/HTTPS
  • WebSocket (for real-time sites)
  • Puppeteer/Playwright (for JavaScript sites)

Compatibility

  • Static HTML sites
  • JavaScript-rendered SPAs (with headless browser)
  • REST APIs
  • GraphQL endpoints

When to Use This

✓ Use When

Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.

✗ Avoid When

Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.

Integration

  • Scheduled monitoring with change detection
  • Multi-source data aggregation pipelines
  • Fallback to web scraping when API rate limits hit
  • Headless browser for JavaScript-heavy sites

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.574 reviews
  • Omar Gill· Dec 28, 2024

    YouTube to Sheets reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Sophia Thomas· Dec 24, 2024

    We wired YouTube to Sheets into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Shikha Mishra· Dec 20, 2024

    YouTube to Sheets is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Omar Patel· Dec 12, 2024

    YouTube to Sheets is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Kiara Park· Dec 12, 2024

    We evaluated YouTube to Sheets against two servers with overlapping tools; this profile had the clearer scope statement.

  • Sakura Thompson· Dec 8, 2024

    YouTube to Sheets is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Sakura Patel· Nov 27, 2024

    Useful MCP listing: YouTube to Sheets is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Omar Gupta· Nov 23, 2024

    Strong directory entry: YouTube to Sheets surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Yuki Khanna· Nov 23, 2024

    I recommend YouTube to Sheets for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Hiroshi Dixit· Nov 19, 2024

    We wired YouTube to Sheets into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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