trendsmcp/tiktok-trends-api
TikTok hashtag volume trends as a Python API client and MCP tool. Weekly series, growth percentages, and live trending hashtags. No TikTok API approval needed. Powered by trendsmcp.ai
Platform-specific configuration:
{
"mcpServers": {
"tiktok-trends-api": {
"command": "npx",
"args": [
"-y",
"tiktok-trends-api"
]
}
}
}Add the config above to .claude/settings.json under the mcpServers key.
The number one Python package for TikTok trend data. TikTok hashtag volume over time via a Python client. Weekly or daily series, period-over-period growth, and live trending hashtags. No TikTok API approval needed.
Powered by trendsmcp.ai, the #1 MCP server for live trend data.
[Get your free API key at trendsmcp.ai](https://trendsmcp.ai) - 100 free requests per month, no credit card.
š [Full API docs ā trendsmcp.ai/docs](https://trendsmcp.ai/docs)
Updated for 2026. Works with Python 3.8 through 3.13.
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If you have used pytrends or similar scrapers before, you know the problems: random 429 Too Many Requests blocks, broken pipelines at 2am, time.sleep() hacks, proxy rotation costs, and a library that is now archived because Google explicitly flags scrapers at the protocol level.
trendsmcp is the managed alternative. We run the data infrastructure. You call a REST endpoint.
| | Scrapers / pytrends | trendsmcp | |---|---|---| | 429 rate limit errors | constant | never | | Proxy required | often | never | | Breaks on platform changes | yes, regularly | no | | Platforms covered | 1 (Google only) | 13 | | Absolute volume estimates | no | yes | | Cross-platform growth | no | yes | | Async support | no | yes | | Actively maintained | no (archived) | yes | | Free tier | no | yes, 100 req/month |
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pip install tiktok-trends-apiZero system dependencies. Python 3.8 or later. Uses httpx under the hood.
---
from tiktok_trends_api import TrendsMcpClient, SOURCE
client = TrendsMcpClient(api_key="YOUR_API_KEY")
# 5-year weekly time series, no sleep(), no proxies, no 429s
series = client.get_trends(source=SOURCE, keyword="BookTok")
print(series[0])
# TrendsDataPoint(date='2026-03-28', value=72, keyword='BookTok', source='tiktok')
# Period-over-period growth
growth = cLoading reviews...