Pornhub MCP Server

Instructions

Implementation Reference

• server.py:332-357 (handler)

  Main handler for compare_platforms tool. Validates input (requires 2-5 platform names), then delegates to search_trends function to fetch and format Google Trends data comparing different adult platforms.
  @mcp.tool() async def compare_platforms( platform_names: list[str], timeframe: str = "today 12-m", region: str = "US" ) -> str: """ Compare Google search trends between different adult platforms. Args: platform_names: List of platform names (max 5) Example: ["pornhub", "onlyfans", "xvideos"] timeframe: Time period (default: past 12 months) region: Region code (default: US) Returns: Comparative analysis showing platform popularity trends. """ if len(platform_names) > 5: return "⚠️ Maximum 5 platforms can be compared at once." if len(platform_names) < 2: return "⚠️ Please provide at least 2 platforms to compare." return await search_trends(platform_names, timeframe, region)
• server.py:244-299 (helper)

  search_trends function called by compare_platforms. Validates keywords (max 5), calls get_trends_data to fetch data, and formats results using format_interest_over_time, format_regional_interest, and format_related_queries helper functions.
  @mcp.tool() async def search_trends( keywords: list[str], timeframe: str = "today 12-m", region: str = "US" ) -> str: """ Search Google Trends for any keywords (performers, platforms, categories, etc.). Args: keywords: List of search terms to analyze (max 5). Examples: ["Lana Rhoades", "Riley Reid"] ["pornhub", "onlyfans"] ["milf", "teen", "amateur"] timeframe: Time period. Options: 'today 12-m' (past year, default) 'today 3-m' (past 3 months) 'today 5-y' (past 5 years) '2020-01-01 2024-12-31' (custom date range) Available back to 2004 region: Geographic region code: 'US' (USA, default) 'GB' (UK) '' (Worldwide) Any ISO country code Returns: Complete Google Trends analysis with interest over time, regional data, and related queries. """ if not pytrends: return "❌ Google Trends API is not available. Please check configuration." if len(keywords) > 5: return "⚠️ Maximum 5 keywords allowed per query. Please reduce your list." if len(keywords) == 0: return "⚠️ Please provide at least one keyword to search." # Fetch data data = get_trends_data(keywords, timeframe, region) # Format and return results result = [ format_interest_over_time(data), "", format_regional_interest(data, top_n=10), format_related_queries(data), "", "📝 Notes:", "- Values are on a 0-100 scale where 100 = peak popularity for the time period", "- Data represents search interest, not absolute search volumes", f"- Data fetched: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" ] return "\n".join(result)
• server.py:42-109 (helper)

  get_trends_data function that performs the core data fetching using Google Trends API (pytrends). Includes caching (1 hour), rate limiting, and retrieves interest over time, regional data, and related queries. Returns structured dictionary with all trend data.
  def get_trends_data(keywords: list[str], timeframe: str = 'today 12-m', geo: str = 'US') -> dict: """ Fetch Google Trends data for given keywords. Args: keywords: List of search terms to compare (max 5) timeframe: Time period (e.g., 'today 12-m', 'today 5-y', '2020-01-01 2024-12-31') geo: Geographic region (e.g., 'US', 'GB', '' for worldwide) Returns: Dictionary with trends data """ if not pytrends: return {"error": "Google Trends API not available"} # Check cache cache_key = f"{','.join(keywords)}_{timeframe}_{geo}" if cache_key in TRENDS_CACHE: cached = TRENDS_CACHE[cache_key] age = (datetime.now() - datetime.fromisoformat(cached['fetched_at'])).seconds if age < 3600: # Cache for 1 hour print(f"Using cached data (age: {age}s)", file=sys.stderr) return cached try: print(f"Fetching Google Trends: {keywords}, {timeframe}, {geo}", file=sys.stderr) # Build payload pytrends.build_payload(keywords, cat=0, timeframe=timeframe, geo=geo, gprop='') # Get interest over time interest_over_time_df = pytrends.interest_over_time() # Get interest by region try: interest_by_region_df = pytrends.interest_by_region(resolution='REGION', inc_low_vol=True, inc_geo_code=False) except Exception as e: print(f"Could not fetch regional data: {e}", file=sys.stderr) interest_by_region_df = pd.DataFrame() # Get related queries try: related_queries = pytrends.related_queries() except Exception as e: print(f"Could not fetch related queries: {e}", file=sys.stderr) related_queries = {} result = { "keywords": keywords, "timeframe": timeframe, "geo": geo, "interest_over_time": interest_over_time_df.to_dict() if not interest_over_time_df.empty else {}, "interest_by_region": interest_by_region_df.to_dict() if not interest_by_region_df.empty else {}, "related_queries": related_queries, "fetched_at": datetime.now().isoformat() } # Cache the result TRENDS_CACHE[cache_key] = result # Rate limiting time.sleep(1) return result except Exception as e: print(f"Error fetching Google Trends data: {e}", file=sys.stderr) return {"error": str(e)}
• server.py:112-161 (helper)

  format_interest_over_time helper function that formats Google Trends interest data into readable text. Calculates statistics (average, peak, low values) for each keyword and determines trend direction (growing, declining, or stable).
  def format_interest_over_time(data: dict) -> str: """Format interest over time data into readable text.""" if "error" in data: return f"❌ Error: {data['error']}" if not data.get("interest_over_time"): return "No data available for the specified time period and keywords." lines = [ f"📊 Google Trends Analysis", f"Keywords: {', '.join(data['keywords'])}", f"Region: {data['geo'] if data['geo'] else 'Worldwide'}", f"Period: {data['timeframe']}", "=" * 60, "" ] # Calculate statistics for each keyword interest_data = data['interest_over_time'] if interest_data: lines.append("📈 Search Interest Statistics (0-100 scale):") lines.append("") for keyword in data['keywords']: if keyword in interest_data: values = [v for v in interest_data[keyword].values() if isinstance(v, (int, float))] if values: avg = sum(values) / len(values) max_val = max(values) min_val = min(values) lines.append(f"'{keyword}':") lines.append(f" Average: {avg:.1f}") lines.append(f" Peak: {max_val}") lines.append(f" Low: {min_val}") # Trend direction if len(values) >= 2: recent_avg = sum(values[-4:]) / min(4, len(values[-4:])) older_avg = sum(values[:4]) / min(4, len(values[:4])) if recent_avg > older_avg * 1.1: lines.append(f" Trend: 📈 Growing ({((recent_avg/older_avg - 1) * 100):.0f}%)") elif recent_avg < older_avg * 0.9: lines.append(f" Trend: 📉 Declining ({((1 - recent_avg/older_avg) * 100):.0f}%)") else: lines.append(f" Trend: ➡️ Stable") lines.append("") return "\n".join(lines)
• server.py:164-195 (helper)

  format_regional_interest helper function that formats regional interest data from Google Trends. Shows top regions for each keyword, sorted by interest value, with configurable top_n parameter (default 10).
  def format_regional_interest(data: dict, top_n: int = 10) -> str: """Format regional interest data into readable text.""" if "error" in data: return f"❌ Error: {data['error']}" if not data.get("interest_by_region"): return "No regional data available." lines = [ f"🌎 Regional Interest", "=" * 60, "" ] region_data = data['interest_by_region'] for keyword in data['keywords']: if keyword in region_data: lines.append(f"Top regions for '{keyword}':") # Sort regions by interest regions = {region: value for region, value in region_data[keyword].items() if isinstance(value, (int, float)) and value > 0} sorted_regions = sorted(regions.items(), key=lambda x: x[1], reverse=True)[:top_n] if sorted_regions: for i, (region, value) in enumerate(sorted_regions, 1): lines.append(f" {i}. {region}: {value}/100") else: lines.append(" No regional data available") lines.append("") return "\n".join(lines)