TikTok MCP Server

Instructions

Implementation Reference

• tiktok_mcp/server.py:311-349 (handler)

  The handler implementation for 'tiktok_analyze_trend', which orchestrates video searches and aggregates hashtag/author statistics.

  elif name == "tiktok_analyze_trend":
      keywords = arguments["keywords"]
      count_per = arguments.get("count_per_keyword", 10)
  
      all_videos = []
      hashtag_counts = {}
      author_counts = {}
      total_engagement = {"likes": 0, "comments": 0, "shares": 0}
  
      for kw in keywords:
          videos = await browser.search_videos(kw, count_per)
          for v in videos:
              all_videos.append(v)
              author = v.get("author", "")
              if author:
                  author_counts[author] = author_counts.get(author, 0) + 1
              desc = v.get("description", "")
              for word in desc.split():
                  if word.startswith("#"):
                      tag = word.lower()
                      hashtag_counts[tag] = hashtag_counts.get(tag, 0) + 1
  
          # Respect rate limit between keywords
          await asyncio.sleep(2)
  
      # Sort hashtags and authors by frequency
      top_hashtags = sorted(hashtag_counts.items(), key=lambda x: -x[1])[:20]
      top_authors = sorted(author_counts.items(), key=lambda x: -x[1])[:10]
  
      analysis = {
          "keywords_analyzed": keywords,
          "total_videos_found": len(all_videos),
          "top_hashtags": [{"hashtag": h, "count": c} for h, c in top_hashtags],
          "top_creators": [{"author": a, "appearances": c} for a, c in top_authors],
          "sample_videos": all_videos[:5],
          "analysis_date": datetime.now().isoformat(),
      }
  
      return [TextContent(type="text", text=json.dumps(analysis, indent=2, ensure_ascii=False))]

• tiktok_mcp/server.py:191-210 (schema)

  Tool registration and input schema definition for 'tiktok_analyze_trend'.

  Tool(
      name="tiktok_analyze_trend",
      description="Analyze trends for a niche: search multiple keywords, aggregate stats on popular sounds, hashtags, posting patterns.",
      inputSchema={
          "type": "object",
          "properties": {
              "keywords": {
                  "type": "array",
                  "items": {"type": "string"},
                  "description": "Keywords to research",
              },
              "count_per_keyword": {
                  "type": "integer",
                  "description": "Videos to analyze per keyword (default 10)",
                  "default": 10,
              },
          },
          "required": ["keywords"],
      },
  ),