Wikipedia Trends Api MCP Server

Wikipedia page-view trends API

Wikipedia article attention via the Trends API. History, growth, and live trending pages as 0-100 scores.

Key: trendsapi.ai/#get-key. HTTP contract and every source: trendsapi-ai/trendsapi.

Authentication

pip install trendsapi-wikipedia
export TRENDSAPI_KEY=your_key

Python 3.9+. Same key as the HTTP API.

from trendsapi_wikipedia import TrendsAPI

client = TrendsAPI()                    # TRENDSAPI_KEY
# client = TrendsAPI(api_key="YOUR_KEY")

Keyword helpers default to source: "wikipedia". Pass source= to hit any other platform with the same client. Official full client (every source, no preset): trendsapi.

Methods

source is lowercase (wikipedia). type is exact (Wikipedia Trending). Mixing them is a 400.

from trendsapi_wikipedia import TrendsAPI

client = TrendsAPI()                    # TRENDSAPI_KEY
# client = TrendsAPI(api_key="YOUR_KEY")

series = client.get_time_series("large language model")
print(series[-1].date, series[-1].value)

growth = client.get_growth("large language model", 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("large language model")

Each point:

Python returns list[TrendsDataPoint]. Use .date and .value, not ["date"]. JS returns the same fields as object properties.

get_growth

g = client.get_growth("large language model", 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"}.

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)

Python: hot.data. JS: hot.data. Optional offset= and category= (Amazon Best Sellers by Category, Top Websites only).

Async

import asyncio
from trendsapi_wikipedia import AsyncTrendsAPI

async def main():
    c = AsyncTrendsAPI()
    return await asyncio.gather(
        c.get_time_series("large language model"),
        c.get_time_series("large language model", source="google search"),
    )

asyncio.run(main())

Each 200 is one billed request.

Pandas

from dataclasses import asdict
import pandas as pd
from trendsapi_wikipedia import TrendsAPI

df = pd.DataFrame(asdict(p) for p in TrendsAPI().get_time_series("large language model"))
df["date"] = pd.to_datetime(df["date"])
print(df.set_index("date")["value"].resample("ME").mean().tail())

Call (curl)

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":"wikipedia","keyword":"large language model"}'

Source notes

• Titles are picky. Java vs Java (programming language) are different series.
• Do not pass source: wikipedia on get_top_trends. Use type: Wikipedia Trending.

Errors

The HTTP body field is a JSON string. SDKs decode it. Raw curl must parse body a second time.

Site: https://trendsapi.ai/trends/wikipedia-trends.

License

MIT. See LICENSE.