About TikTok Trends API
TikTok Trends API provides data on TikTok hashtag and search-interest trends, with Python and JavaScript/TypeScript clients. It supports historical series, growth calculations, and live trending feeds.
Key features
- 01Retrieve historical hashtag trend series
- 02Calculate growth across time windows
- 03Get live trending hashtags and searches
- 04Use synchronous and asynchronous clients
- 05Set pagination and feed-category parameters
Use cases
- 01Track interest changes for a hashtag
- 02Compare keyword growth across periods
- 03Review live TikTok hashtag or search trends
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://tiktok.api.trendsapi.ai/mcpclaude mcp add --transport http ai-trendsapi-tiktok https://tiktok.api.trendsapi.ai/mcp{"mcpServers":{"ai-trendsapi-tiktok":{"type":"http","url":"https://tiktok.api.trendsapi.ai/mcp"}}}Open in Cursor{"servers":{"ai-trendsapi-tiktok":{"type":"http","url":"https://tiktok.api.trendsapi.ai/mcp"}}}Open in VS Code{"mcpServers":{"ai-trendsapi-tiktok":{"type":"http","url":"https://tiktok.api.trendsapi.ai/mcp"}}}{"mcpServers":{"ai-trendsapi-tiktok":{"serverUrl":"https://tiktok.api.trendsapi.ai/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://tiktok.api.trendsapi.ai/mcpAuth requiredTools
Details
From the project READMETikTok hashtag volume API
TikTok hashtag and search interest trends via the TikTok Trends API. History, growth, and live trending hashtags.
Key: trendsapi.ai/#get-key. HTTP contract and every source: trendsapi-ai/trendsapi.
Authentication
pip install trendsapi-tiktok
export TRENDSAPI_KEY=your_key
Python 3.9+. Same key as the HTTP API.
from trendsapi_tiktok import TrendsAPI
client = TrendsAPI() # TRENDSAPI_KEY
# client = TrendsAPI(api_key="YOUR_KEY")
Keyword helpers default to source: "tiktok". Pass source= to hit any other platform with the same client. Official full client (every source, no preset): trendsapi.
Methods
| Method | REST mode | Returns |
|---|---|---|
get_time_series(keyword, source=, data_mode=) | get_time_series | list[TrendsDataPoint] |
get_growth(keyword, percent_growth=, source=, data_mode=) | get_growth | GetGrowthResponse |
get_live(limit=, offset=, category=) | get_top_trends | GetTopTrendsResponse |
get_top_trends(type=, ...) | get_top_trends | GetTopTrendsResponse |
source is lowercase (tiktok). type is exact (TikTok Trending Hashtags). Mixing them is a 400.
from trendsapi_tiktok import TrendsAPI
client = TrendsAPI() # TRENDSAPI_KEY
# client = TrendsAPI(api_key="YOUR_KEY")
series = client.get_time_series("matcha")
print(series[-1].date, series[-1].value)
growth = client.get_growth("matcha", percent_growth=["3M", "12M"])
print(growth.results[0].growth, growth.results[0].direction)
hot = client.get_live(limit=10)
print(hot.data) # [[1, "..."], ...]
get_time_series
points = client.get_time_series("matcha")
Each point:
| Field | Always | Meaning |
|---|---|---|
date | yes | YYYY-MM-DD |
value | yes | 0-100 index for this series |
keyword | yes | Echo |
volume | no | Absolute volume when available |
source or datatype | no | Pipeline label |
Python returns list[TrendsDataPoint]. Use .date and .value, not ["date"].
JS returns the same fields as object properties.
get_growth
g = client.get_growth("matcha", percent_growth=["12M", "3M", "YTD"])
print(g.results[0].growth, g.results[0].direction)
percent_growth default: ["12M"]. Presets: 7D 14D 30D 1M 2M 3M 6M 9M 12M/1Y 18M 24M/2Y 36M/3Y 48M 60M/5Y MTD QTD YTD. Custom: {"name": "Launch", "recent": "2024-06-01", "baseline": "2024-01-01"}.
| Field | Meaning |
|---|---|
search_term | Keyword |
data_source | Source |
results | One object per window (period, growth, direction, dates, values) |
metadata | Counts / success flag |
Several windows still count as one request. Python: growth.results[0].growth. JS: growth.results[0].growth.
get_live
hot = client.get_live(limit=10)
| Field | Meaning |
|---|---|
as_of_ts | Snapshot time |
type | Feed name |
limit, offset, count | Pagination |
data | [rank, label] rows |
Python: hot.data. JS: hot.data. Optional offset= and category= (Amazon Best Sellers by Category, Top Websites only).
Async
import asyncio
from trendsapi_tiktok import AsyncTrendsAPI
async def main():
c = AsyncTrendsAPI()
return await asyncio.gather(
c.get_time_series("matcha"),
c.get_time_series("matcha", source="google search"),
)
asyncio.run(main())
Each 200 is one billed request.
Pandas
from dataclasses import asdict
import pandas as pd
from trendsapi_tiktok import TrendsAPI
df = pd.DataFrame(asdict(p) for p in TrendsAPI().get_time_series("matcha"))
df["date"] = pd.to_datetime(df["date"])
print(df.set_index("date")["value"].resample("ME").mean().tail())
JavaScript / TypeScript
npm install trendsapi-tiktok
Node 18+, Deno, Bun, Workers. Same API key. Field tables above apply.
Methods
| Method | REST mode | Returns |
|---|---|---|
getTimeSeries(keyword, { source, data_mode }) | get_time_series | weekly points |
getGrowth(keyword, { percent_growth, source, data_mode }) | get_growth | growth object |
getLive({ limit, offset, category }) | get_top_trends | live feed |
getTopTrends({ type, ... }) | get_top_trends | live feed |
import { TrendsAPI } from "trendsapi-tiktok";
const client = new TrendsAPI({ apiKey: process.env.TRENDSAPI_KEY! });
const series = await client.getTimeSeries("matcha");
console.log(series.at(-1)?.date, series.at(-1)?.value);
const growth = await client.getGrowth("matcha", {
percent_growth: ["3M", "12M"],
});
console.log(growth.results[0].growth, growth.results[0].direction);
const live = await client.getLive({ limit: 10 });
console.log(live.data); // [[1, "..."], ...]
Call (curl)
| Field | Value |
|---|---|
| Endpoint | POST https://api.trendsapi.ai/api |
| Auth | Authorization: Bearer $TRENDSAPI_KEY |
| History | source: tiktok with get_time_series or get_growth |
| Keyword | Hashtag or topic, e.g. matcha (# optional) |
Live type | TikTok Trending Hashtags, TikTok Trending Searches, TikTok Shop Hot Products |
curl -sS -X POST https://api.trendsapi.ai/api \
-H "Authorization: Bearer $TRENDSAPI_KEY" \
-H "Content-Type: application/json" \
-d '{"mode":"get_time_series","source":"tiktok","keyword":"matcha"}'
Source notes
valueis 0-100 for this hashtag, not view count.tiktok trending hashtags(wrong case) is 400.- Publishing videos still requires TikTok's own APIs. This endpoint is demand only.
Errors
| HTTP | Client |
|---|---|
| 200 | Parsed payload. Python dataclasses / JS typed objects |
| 400 | Raises. Fix source or type spelling |
| 401 | Raises. Check TRENDSAPI_KEY |
| 404 | Raises. No series for that keyword. Do not retry |
| 429 | Raises. Quota |
| 5xx | Client retries, then raises |
The HTTP body field is a JSON string. SDKs decode it. Raw curl must parse body a second time.
Site: https://trendsapi.ai/trends/tiktok-trends.
License
MIT. See LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.