Supadata

@supadata search YouTube for 'machine learning tutorials' uploaded in the last month and show me the top 5 results with their view counts

Found 5 recent machine learning tutorials: 1. "Neural Networks Explained" by TechEdu (450K views, 3 weeks ago) 2. "PyTorch Crash Course 2024" by CodeMaster (280K views, 2 weeks ago) 3. "ML Fundamentals in 20 Minutes" by DataScience101 (195K views, 4 weeks ago) 4. "Building Your First Model" by AIAcademy (120K views, 1 week ago) 5. "Deep Learning Basics" by LearnAI (88K views, 3 weeks ago) Each result includes channel ID, video ID, publish date, and engagement metrics. This is an illustrative example based on typical search results.

This example demonstrates Supadata's YouTube search capability with filtering. The MCP requires a valid API key with YouTube Data API access. Search results are subject to YouTube's rate limits (typically 10,000 quota units per day). Results reflect YouTube's algorithm at query time and may vary.

@supadata scrape the pricing page at example-saas.com/pricing and extract all plan names, prices, and feature lists into a structured format

Extracted pricing data from example-saas.com/pricing: **Starter Plan** - $29/month - 5 user seats - 10GB storage - Email support **Professional Plan** - $79/month - 20 user seats - 100GB storage - Priority support - API access **Enterprise Plan** - Custom pricing - Unlimited seats - 1TB storage - Dedicated support - SSO integration This is an illustrative extraction showing the structured data Supadata would return from the target page.

This showcases Supadata's web scraping tool for competitive analysis or data collection. The scraper respects robots.txt and may be blocked by sites with aggressive anti-bot measures. Users should verify they have permission to scrape target sites and be aware that page structure changes will affect extraction accuracy.

@supadata get the full video list from the YouTube channel UC1234567890 and analyze which topics get the most engagement based on titles and view counts

Retrieved 47 videos from channel UC1234567890. Analyzing engagement patterns: **High-performing topics:** - Tutorial content (avg 125K views): 12 videos with 'how-to' or 'tutorial' in title - Quick tips (avg 98K views): 8 videos under 10 minutes - Case studies (avg 87K views): 6 deep-dive videos **Lower engagement:** - News updates (avg 22K views): 15 videos - Q&A sessions (avg 18K views): 6 videos Top video: "Complete Beginner's Guide" with 340K views. This analysis combines Supadata's channel data retrieval with AI synthesis of patterns.

This example pairs Supadata's channel video retrieval with AI reasoning to surface insights. Requires a valid channel ID (not channel name). The MCP fetches metadata only—no transcript or comment data. Pagination handles large channels, but quota consumption scales with video count. Analysis quality depends on having sufficient video history.

When Supadata wins for tracking competitor YouTube strategy

A 3-person marketing team at a B2B SaaS company needs to monitor five competitor YouTube channels weekly—tracking upload frequency, video titles, and engagement patterns to inform their own content calendar. Supadata is the right call here because it bundles channel metadata, video lists, and search into one MCP instead of forcing the team to juggle YouTube Data API quotas or scrape manually. The channel videos tool supports pagination, so you can pull the last 50 uploads without hitting rate limits. The trade-off: if you need comment sentiment or view-count deltas over time, Supadata only gives you point-in-time metadata—you'll need to log results yourself or pair it with a spreadsheet MCP. If your workflow is 'pull competitor uploads once a week and summarize trends in Slack,' this MCP closes the loop in one prompt.

When Supadata's web scrape tool fits niche research tasks

A solo founder building a vertical SaaS product wants to scrape 20 industry blogs monthly to extract feature announcements and pricing changes from competitors who don't publish APIs. Supadata's web scrape and URL map tools handle this if the target sites are static HTML or light JavaScript—you specify the URL, get parsed content back, and feed it into a summarization prompt. This works for small-batch research (under 100 pages a month) where you're not fighting bot detection or needing headless browser rendering. The boundary: if the sites use heavy client-side rendering or require login, Supadata's scraper will return empty or partial content. If your targets are mostly WordPress blogs or marketing sites with public pricing pages, this MCP saves you from spinning up Puppeteer. Pair it with a spreadsheet MCP to log results across weeks.

When Supadata speeds up YouTube playlist audits for training teams

A 6-person customer success team maintains three YouTube playlists (product demos, feature walkthroughs, troubleshooting) used in onboarding new customers. Every quarter, they need to audit playlist contents—checking for outdated videos, broken links, or videos that no longer match the current product version. Supadata's playlist and playlist videos tools let you pull all metadata in one call, then prompt an AI to flag videos older than 12 months or with titles referencing deprecated features. This beats manually clicking through 40 videos in the YouTube UI. The catch: Supadata only fetches metadata (title, upload date, duration)—it doesn't transcribe or analyze video content, so you can't auto-detect 'this demo shows the old dashboard UI.' If your audit is metadata-driven (dates, titles, descriptions), this MCP cuts the task from 90 minutes to 10.