Wikipedia Trends API - page view trends as JSON

Wikipedia trend data as clean JSON: page view time series for any topic, growth rates and the live most-viewed articles feed from one REST endpoint. A clean proxy for public attention.

One endpoint. One API key. One normalized 0-100 trend score you can compare against 14 other platforms.

Docs: https://trendsapi.ai/#quickstart · llms.txt: https://trendsapi.ai/llms.txt · Free API key (100 req/mo): https://trendsapi.ai/#get-key

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What a call looks like

curl -X POST https://api.trendsapi.ai/api \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"mode": "get_growth", "source": "wikipedia", "keyword": "artificial intelligence", "percent_growth": ["3M", "12M"]}'

{
  "keyword": "artificial intelligence",
  "source": "wikipedia",
  "growth": { "3M": 41.8, "12M": 212.4 },
  "timestamp": "2026-08-03T12:00:00Z"
}

Quickstart (60 seconds)

1. Get a free API key at https://trendsapi.ai/#get-key - 100 requests/month, no credit card.

2. Make your first call:

curl -X POST https://api.trendsapi.ai/api \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"mode": "get_time_series", "source": "wikipedia", "keyword": "artificial intelligence"}'

Python:

import requests

res = requests.post(
    "https://api.trendsapi.ai/api",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={"mode": "get_growth", "source": "wikipedia", "keyword": "artificial intelligence",
          "percent_growth": ["3M", "12M"]},
)
print(res.json())

Node.js:

const res = await fetch("https://api.trendsapi.ai/api", {
  method: "POST",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ mode: "get_growth", source: "wikipedia", keyword: "artificial intelligence",
                          percent_growth: ["3M", "12M"] }),
});
console.log(await res.json());

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The three modes

Mode	What it returns	Needs a keyword?
get_time_series	Historical page views as a normalized 0-100 series	yes
get_growth	Growth % over 3M / 6M / 12M / 5Y windows	yes
get_top_trends	Live trending feeds (21 of them)	no

Why teams switch

	Wikimedia Pageviews API	Trends API
Normalization	raw counts, DIY	0-100 score, done
Growth rates	compute yourself	3M/6M/12M/5Y built in
Trending feed	separate endpoint	included, one call
Cross-source compare	no	same scale as 14 other sources
Free tier	free (rate limited)	100 requests/month

Use cases

• Investment research: rising page views on a company or technology as an attention signal
• PR measurement: did the press coverage actually move public attention?
• Research: track when a topic enters public consciousness
• Editorial: find what the world is looking up right now

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Use it from your AI assistant (MCP)

The same API key powers the Trends API MCP server, so Claude, Cursor, VS Code, ChatGPT and any MCP-compatible client can query this data in natural language.

+ Add to Cursor (one click)

Cursor / Windsurf / Cline (~/.cursor/mcp.json or equivalent):

{
  "mcpServers": {
    "trendsapi": {
      "url": "https://api.trendsapi.ai/mcp",
      "transport": "http",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

VS Code / GitHub Copilot (.vscode/mcp.json):

{
  "servers": {
    "trendsapi": {
      "type": "http",
      "url": "https://api.trendsapi.ai/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "trendsapi": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://api.trendsapi.ai/mcp", "--header", "Authorization:${AUTH_HEADER}"],
      "env": { "AUTH_HEADER": "Bearer YOUR_API_KEY" }
    }
  }
}

Claude.ai (browser): Settings -> Connectors -> Add custom connector -> https://api.trendsapi.ai/mcp

Then ask things like:

How did "creatine gummies" grow on TikTok vs Google over the last 12 months?
What is trending on YouTube right now?

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Every source on the same key

Source	source value	What it measures
Google Search	google search	Search volume
Google Images	google images	Image search volume
Google News	google news	News search volume
Google Shopping	google shopping	Shopping search volume
YouTube	youtube	Search volume
TikTok	tiktok	Hashtag volume
Reddit	reddit	Subreddit subscribers
Amazon	amazon	Product search volume
Wikipedia	wikipedia	Page views
News volume	news volume	Article mention volume
News sentiment	news sentiment	Positive / negative score
App downloads	app downloads	Android downloads (AppBrain)
App rankings	app rankings	Android chart position
npm	npm	Weekly package downloads
Steam	steam	Concurrent players (monthly)

Live feeds (get_top_trends, no keyword needed)

Feed	type value
Google Trends	Google Trends
Google News Top News	Google News Top News
TikTok Trending Hashtags	TikTok Trending Hashtags
TikTok Trending Searches	TikTok Trending Searches
TikTok Shop Hot Products	TikTok Shop Hot Products
YouTube Trending	YouTube Trending
X (Twitter) Trending	X (Twitter) Trending
Reddit Hot Posts	Reddit Hot Posts
Reddit World News	Reddit World News
Wikipedia Trending	Wikipedia Trending
Amazon Best Sellers Top Rated	Amazon Best Sellers Top Rated
Amazon Best Sellers by Category	Amazon Best Sellers by Category
App Store Top Free	App Store Top Free
App Store Top Paid	App Store Top Paid
Google Play	Google Play
Top Websites	Top Websites
Spotify Top Podcasts	Spotify Top Podcasts
Steam Most Played	Steam Most Played
GitHub Trending Repos	GitHub Trending Repos
IMDb MOVIEmeter	IMDb MOVIEmeter
Open Library Trending Books	Open Library Trending Books

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FAQ

What Wikipedia data does Trends API provide?

Page view volume for any article or topic as a normalized time series, growth percentages over 3M/6M/12M/5Y windows, and the live Wikipedia Trending feed of most-viewed articles today.

Why use this instead of the Wikimedia Pageviews API?

The Wikimedia API returns raw counts you must normalize and window yourself. Trends API returns a rescaled 0-100 series with growth already computed, in the same shape as 14 other sources - so cross-platform attention comparisons take one line of code.

Is Wikipedia attention a good proxy for real-world interest?

It is one of the cleaner ones: page views are driven by active curiosity rather than algorithmic feeds, so spikes usually reflect genuine public attention events.

How fresh is the trending feed?

Updated through the day. Every response includes its own timestamp.

Can I compare a topic's Wikipedia attention with Google search interest?

Yes - query both sources with the same keyword and compare normalized scores directly.

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Links

• Docs & quickstart: https://trendsapi.ai/#quickstart
• llms.txt (machine-readable API reference): https://trendsapi.ai/llms.txt
• Pricing (free tier: 100 requests/month): https://trendsapi.ai/#pricing
• Get an API key: https://trendsapi.ai/#get-key

License

MIT - see LICENSE. Data is served by Trends API; usage of the API itself is subject to the plan limits on your key.