About Wikipedia Trends API
Wikipedia Trends API provides a Python client and API for tracking attention to Wikipedia topics. It returns historical series, growth figures, and live trending pages for developers analyzing topic interest.
Key features
- 01Retrieve Wikipedia attention history
- 02Calculate growth across time windows
- 03Fetch live trending pages
- 04Use synchronous and async clients
Use cases
- 01Track changes in Wikipedia page attention
- 02Compare growth across time periods
- 03Review currently trending Wikipedia pages
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://wikipedia.api.trendsapi.ai/mcpclaude mcp add --transport http ai-trendsapi-wikipedia https://wikipedia.api.trendsapi.ai/mcp{"mcpServers":{"ai-trendsapi-wikipedia":{"type":"http","url":"https://wikipedia.api.trendsapi.ai/mcp"}}}Open in Cursor{"servers":{"ai-trendsapi-wikipedia":{"type":"http","url":"https://wikipedia.api.trendsapi.ai/mcp"}}}Open in VS Code{"mcpServers":{"ai-trendsapi-wikipedia":{"type":"http","url":"https://wikipedia.api.trendsapi.ai/mcp"}}}{"mcpServers":{"ai-trendsapi-wikipedia":{"serverUrl":"https://wikipedia.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://wikipedia.api.trendsapi.ai/mcpAuth requiredTools
Details
From the project READMEWikipedia 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
| 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 (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:
| 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("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"}.
| 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_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)
| Field | Value |
|---|---|
| Endpoint | POST https://api.trendsapi.ai/api |
| Auth | Authorization: Bearer $TRENDSAPI_KEY |
| History | source: wikipedia with get_time_series or get_growth |
| Keyword | Article title or topic, e.g. large language model |
Live type | Wikipedia Trending |
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.
JavavsJava (programming language)are different series. - Do not pass
source: wikipediaonget_top_trends. Usetype: Wikipedia Trending.
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/wikipedia-trends.
License
MIT. See LICENSE.
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