MCP 服务器

Wikipedia Trends API

An MCP server that exposes Wikipedia page view trends as clean JSON, providing normalized 0-100 trend scores, growth rates over 3M/6M/12M/5Y windows, and live most-viewed article feeds, comparable across 15 sources including Google, YouTube, and TikTok.

README

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

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());

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

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?

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`

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.