Pornhub MCP Server

Instructions

Implementation Reference

• server.py:419-454 (handler)

  Main handler function for the trending_searches tool. Decorated with @mcp.tool(), it takes a base_keyword, timeframe, and region as parameters, fetches Google Trends data, and returns formatted trending and related searches for the keyword.
  @mcp.tool() async def trending_searches( base_keyword: str, timeframe: str = "today 12-m", region: str = "US" ) -> str: """ Find trending and related searches for a keyword. Args: base_keyword: Main keyword to find related searches for Example: "pornhub", "onlyfans", or any performer name timeframe: Time period (default: past 12 months) region: Region code (default: US) Returns: List of related and rising search terms. """ data = get_trends_data([base_keyword], timeframe, region) if "error" in data: return f"❌ Error: {data['error']}" result = [ f"🔥 Trending Searches Related to '{base_keyword}'", f"Region: {region if region else 'Worldwide'}", f"Period: {timeframe}", "=" * 60, "", format_related_queries(data), "", f"Data fetched: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" ] return "\n".join(result)
• server.py:42-101 (helper)

  Helper function get_trends_data that fetches Google Trends data for given keywords. Implements caching mechanism to avoid excessive API calls and returns interest over time, interest by region, and related queries 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
• server.py:198-237 (helper)

  Helper function format_related_queries that formats the related queries data from Google Trends API into a readable string format with top queries and rising queries sections.
  def format_related_queries(data: dict) -> str: """Format related queries data.""" if "error" in data or not data.get("related_queries"): return "" lines = [ "", "🔍 Related & Trending Queries", "=" * 60, "" ] related = data['related_queries'] for keyword in data['keywords']: if keyword in related: lines.append(f"Related to '{keyword}':") # Top related queries if 'top' in related[keyword] and related[keyword]['top'] is not None: top_df = related[keyword]['top'] if not top_df.empty: lines.append(" Top:") for idx, row in top_df.head(5).iterrows(): lines.append(f" • {row['query']} ({row['value']})") # Rising queries if 'rising' in related[keyword] and related[keyword]['rising'] is not None: rising_df = related[keyword]['rising'] if not rising_df.empty: lines.append(" Rising:") for idx, row in rising_df.head(5).iterrows(): growth = row['value'] if growth == 'Breakout': lines.append(f" • {row['query']} (🔥 Breakout)") else: lines.append(f" • {row['query']} (+{growth}%)") lines.append("") return "\n".join(lines)