Wikipedia Trends Api MCP Server

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

Why teams switch

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

Live feeds (get_top_trends, no keyword needed)

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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.