MCP Examples & Use Cases

Scenario

Build an automated price tracking system that monitors products across multiple retailers, tracks historical trends, and identifies the best time to buy. Perfect for deal hunters, price comparison apps, or dynamic pricing strategies.

Prompt

What Happens Behind the Scenes

1. AI calls scraping_instruction_enhanced to get optimal scraping parameters
2. AI uses web_scrape with extraction_model: "product" for each retailer's product page
3. AI extracts comprehensive data: price, original price, discount %, stock status, shipping cost, delivery time, and warranty info
4. AI checks for promotional codes, bundle offers, and financing options
5. AI calculates total cost (price + shipping + taxes) for accurate comparison
6. AI compares value propositions: free shipping, faster delivery, return policies
7. AI generates detailed comparison with recommendation based on best overall value

Example Response

Scenario

Conduct comprehensive research by gathering, analyzing, and synthesizing information from multiple authoritative sources. Perfect for market research, academic literature reviews, trend analysis, or competitive intelligence.

Prompt

What Happens Behind the Scenes

1. AI calls scraping_instruction_enhanced for best practices
2. AI constructs search queries for each source (filtering by topic and date range)
3. AI uses web_scrape with extraction_model: "article" for article listing pages
4. AI visits individual articles and extracts: title, authors, publication date, abstract, key findings, methodologies, and citations
5. AI filters articles by publication date (last 90 days) and relevance score
6. AI identifies recurring themes, breakthrough categories, and research trends
7. AI maps researchers to institutions and tracks collaboration networks
8. AI extracts quantitative metrics: qubit counts, error rates, coherence times, gate fidelities
9. AI synthesizes findings into structured report with timeline, thematic analysis, and institutional rankings

Example Response

Scenario

Extract complex, structured data from dynamic websites using AI-powered parsing. Perfect for building datasets, enriching CRM data, or scraping sites with inconsistent layouts that would be difficult to parse with traditional selectors.

Prompt

What Happens Behind the Scenes

1. AI calls scraping_instruction_enhanced to get optimal parameters
2. AI uses web_scrape with render_js: true (Yelp uses dynamic content)
3. AI provides detailed extraction_prompt specifying exact fields and data types
4. Scrapfly's LLM analyzes the page structure and intelligently extracts data across varying HTML layouts
5. AI handles edge cases: missing phone numbers, varied address formats, inconsistent pricing
6. AI may visit individual restaurant pages for additional details (hours, menu, reviews)
7. AI filters results for ratings ≥ 4.0 stars and sorts by relevance
8. AI validates data quality: checks phone format, ensures addresses are complete, normalizes price ranges
9. AI returns structured JSON with consistent schema, ready for database import

Extraction Tool Usage

{
  "tool": "web_scrape",
  "parameters": {
    "url": "https://www.yelp.com/search?find_desc=Italian&find_loc=San+Francisco",
    "pow": "...",
    "render_js": true,
    "extraction_prompt": "Extract detailed information for each Italian restaurant listed. For each restaurant, return a JSON object with these fields: restaurant_name (string), rating (float, out of 5.0), review_count (integer), price_range (string: $ to $$$$), cuisine_tags (array of strings), address (object with street, city, state, zip), phone (string, formatted), business_hours (object with day: hours), popular_dishes (array of strings from reviews), offers_delivery (boolean), offers_takeout (boolean), health_score (string if visible), website_url (string), has_outdoor_seating (boolean), accepts_reservations (boolean). Return as a JSON array of restaurant objects."
  }
}

Example Response (Excerpt)

[
  {
    "restaurant_name": "Flour + Water",
    "rating": 4.5,
    "review_count": 3847,
    "price_range": "$$",
    "cuisine_tags": ["Italian", "Pasta Shop", "Wine Bar"],
    "address": {
      "street": "2401 Harrison St",
      "city": "San Francisco",
      "state": "CA",
      "zip": "94110"
    },
    "phone": "(415) 826-7000",
    "business_hours": {
      "Monday": "Closed",
      "Tuesday-Thursday": "5:30 PM - 10:00 PM",
      "Friday-Saturday": "5:30 PM - 11:00 PM",
      "Sunday": "5:30 PM - 10:00 PM"
    },
    "popular_dishes": [
      "Handmade Pasta",
      "Margherita Pizza",
      "Burrata",
      "Tiramisu"
    ],
    "offers_delivery": true,
    "offers_takeout": true,
    "health_score": "A",
    "website_url": "https://flourandwater.com",
    "has_outdoor_seating": true,
    "accepts_reservations": true
  },
  {
    "restaurant_name": "Delfina",
    "rating": 4.3,
    "review_count": 2912,
    "price_range": "$$",
    "cuisine_tags": ["Italian", "Californian"],
    "address": {
      "street": "3621 18th St",
      "city": "San Francisco",
      "state": "CA",
      "zip": "94110"
    },
    "phone": "(415) 552-4055",
    "business_hours": {
      "Monday-Sunday": "5:00 PM - 10:00 PM"
    },
    "popular_dishes": [
      "Spaghetti Carbonara",
      "Roasted Chicken",
      "Panna Cotta"
    ],
    "offers_delivery": false,
    "offers_takeout": true,
    "health_score": "A",
    "website_url": "https://delfinasf.com",
    "has_outdoor_seating": false,
    "accepts_reservations": true
  }
  // ... 18 more restaurants
]

Scenario

Track competitor features, pricing changes, and marketing strategies across multiple SaaS platforms to inform your product roadmap.

Prompt

What Happens Behind the Scenes

1. AI calls scraping_instruction_enhanced to get optimal scraping parameters
2. AI uses web_scrape with render_js: true to load each competitor's pricing page (dynamic content)
3. AI uses extraction_prompt to extract: pricing tiers, features per tier, discounts, and trial terms
4. AI visits each blog/news page and uses extraction_model: "article" to get recent announcements
5. AI filters articles by publication date (last 30 days)
6. AI analyzes and compares pricing structures, feature availability, and positioning
7. AI generates a competitive analysis report with feature gaps, pricing insights, and strategic recommendations

Example Response

Scenario

Research the real estate market in a specific area by aggregating property listings, analyzing price trends, and comparing neighborhoods.

Prompt

What Happens Behind the Scenes

1. AI calls scraping_instruction_enhanced to get best practices for real estate scraping
2. AI constructs search URLs for each platform with filters (location, price, bedrooms)
3. AI uses web_scrape with extraction_model: "real_estate_property_listing" for listing pages
4. AI visits individual property detail pages using extraction_model: "real_estate_property" to get full details
5. AI extracts key data: price, size, features, rental estimates, HOA fees, property tax
6. AI calculates rental yield: (annual rental income / property price) × 100
7. AI calculates price per square foot for each property
8. AI groups by neighborhood (zip code) and generates comparative analysis with investment recommendations

Example Response

Build and enrich contact lists automatically with data validation and scoring.

1. Search directories - Find companies matching criteria
2. Extract contact info - Get company details, founders, emails
3. Enrich data - Look up founders on LinkedIn
4. Validate - Check company websites for relevance
5. Score leads - Rank by fit and priority
6. Export - Format as CSV for CRM import

Track website changes over time with automated alerts and version archiving.

1. Initial capture - Take screenshot and save HTML
2. Schedule checks - Scrape page periodically
3. Compare - Detect changes in content or layout
4. Alert - Notify when changes detected
5. Archive - Store historical versions

• Financial news aggregation
• Stock data collection from multiple sources
• Economic indicator tracking
• Real estate listing analysis

• Job posting aggregation
• Candidate research (LinkedIn, GitHub, portfolios)
• Salary benchmarking
• Company culture research

• News monitoring and aggregation
• Academic paper tracking
• Social media sentiment analysis
• Event and conference tracking

• Property listing aggregation
• Price trend analysis
• Neighborhood research
• Rental market analysis

• Hotel price comparison
• Flight deal monitoring
• Review aggregation for destinations
• Event and attraction research