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News sentiment scores over time, with growth for any topic. Free key at trendsmcp.ai
About
News sentiment scores over time, with growth for any topic. Free key at trendsmcp.ai
Remote endpoints: streamable-http: https://news-sentiment.api.trendsmcp.ai/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
Endpoint verified · Open access · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"ai-trendsmcp-news-sentiment": {
"url": "https://news-sentiment.api.trendsmcp.ai/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
news-sentiment-mcp
News sentiment trends as an MCP tool. Plug into Claude, Cursor, or any MCP-compatible AI host. Weekly series, growth percentages, and live news feed.
Powered by trendsmcp.ai — one API key, one client, 30+ data sources: Google Search, YouTube, TikTok, Reddit, Amazon, Wikipedia, App Store, Steam, npm, news volume, news sentiment, live trending feeds, and more. No separate credentials per platform.
Get your free API key → trendsmcp.ai — 100 free requests/month, no credit card.
📖 Full API docs → trendsmcp.ai/docs
Updated for 2026. Works with Python 3.8 through 3.13.
Quick install
Same four clients as the site hero. Get a free key first (100 req/mo). Claude and ChatGPT sign you in with OAuth. Cursor and VS Code: click, then put your key from /account if the deeplink used a placeholder.
| Client | After you click |
|---|---|
| Claude | Connector name and URL are prefilled (https://www.trendsmcp.ai/mcp). Confirm, then authorize. |
| Cursor | Approve the MCP install. Replace YOUR_API_KEY if prompted. |
| ChatGPT | Enable Developer mode (Profile → Settings → Security). Name Trends MCP, URL https://www.trendsmcp.ai/mcp, then authorize. |
| VS Code | Sign in on the account page and use the VS Code button so the key is included. |
Then ask: Using TrendsMCP, what's trending on Google right now?
Use as an MCP tool
Add to your mcp.json (Claude Desktop, Cursor, Windsurf, VS Code, or any MCP host):
{
"mcpServers": {
"trends-mcp": {
"url": "https://api.trendsmcp.ai/mcp",
"transport": "http",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}
Get your free key at trendsmcp.ai. Full setup instructions for Claude, Cursor, Windsurf, and VS Code at trendsmcp.ai/docs.
No scraping. No 429 errors. No proxies.
If you have used pytrends or similar scrapers before, you know the problems: random 429 Too Many Requests blocks, broken pipelines at 2am, time.sleep() hacks, proxy rotation costs, and a library that is now archived because Google explicitly flags scrapers at the protocol level.
trendsmcp is the managed alternative. We run the data infrastructure. You call a REST endpoint.
pytrends alternative for News Sentiment data
| Scrapers / pytrends | trendsmcp | |
|---|---|---|
| 429 rate limit errors | constant | never |
| Proxy required | often | never |
| Breaks on platform changes | yes, regularly | no |
| Data sources covered | 1 (Google only) | 30+ |
| Absolute volume estimates | no | yes |
| Cross-platform growth | no | yes |
| Async support | no | yes |
| Actively maintained | no (archived) | yes |
| Free tier | no | yes, 100 req/month |
Install
pip install news-sentiment-mcp
Zero system dependencies. Python 3.8 or later. Uses httpx under the hood.
Quick start
from news_sentiment_mcp import TrendsMcpClient, SOURCE
client = TrendsMcpClient(api_key="YOUR_API_KEY")
# 5-year weekly time series — no sleep(), no proxies, no 429s
series = client.get_trends(source=SOURCE, keyword="bitcoin")
print(series[0])
# TrendsDataPoint(date='2026-03-28', value=72, keyword='bitcoin', source='news sentiment')
# Period-over-period growth
growth = client.get_growth(
source=SOURCE,
keyword="bitcoin",
percent_growth=["3M", "1Y"],
)
print(growth.results[0])
# GrowthResult(period='3M', growth=14.5, direction='increase', ...)
# What's trending right now (across all live platforms)
trending = client.get_top_trends(limit=10)
print(trending.data)
# [[1, 'topic one'], [2, 'topic two'], ...]
Async support
import asyncio
from news_sentiment_mcp import AsyncTrendsMcpClient, SOURCE
async def main():
client = AsyncTrendsMcpClient(api_key="YOUR_API_KEY")
series = await client.get_trends(source=SOURCE, keyword="bitcoin")
print(series[0])
asyncio.run(main())
Query multiple platforms concurrently with one key:
google, youtube, reddit, amazon, tiktok = await asyncio.gather(
client.get_trends(source="google search", keyword="bitcoin"),
client.get_trends(source="youtube", keyword="bitcoin"),
client.get_trends(source="reddit", keyword="bitcoin"),
client.get_trends(source="amazon", keyword="bitcoin"),
client.get_trends(source="tiktok", keyword="bitcoin"),
)
Use cases
- SEO research: track keyword search volume trends across Google Search, Google News, and Google Images before publishing content
- Market research: measure consumer demand signals on Amazon and Google Shopping before entering a product category
- Investment research: monitor Reddit discussion volume, news sentiment, and Wikipedia page view spikes as leading indicators
- Content strategy: find what is growing on YouTube and TikTok before topics peak and competition saturates them
- Competitor tracking: compare brand search volume growth across platforms over custom date ranges
- App analytics: track App Store interest and app download estimates alongside Reddit and news buzz
Works with
- Claude (via MCP — trendsmcp.ai/docs)
- Cursor (via MCP — trendsmcp.ai/docs)
- ChatGPT (via MCP — trendsmcp.ai/docs)
- Windsurf (via MCP — trendsmcp.ai/docs)
- VS Code Copilot (via MCP — trendsmcp.ai/docs)
- LangChain: pass
TrendsMcpClientoutput directly as tool results or context - CrewAI: wrap any method as a
Tooland drop it into your crew - AutoGen: register as a callable tool for any agent
- LlamaIndex: use trend series as structured data nodes for retrieval
- Pandas: each
get_trends()response converts to a DataFrame in one line
Methods
get_trends(source, keyword, data_mode=None)
Returns a historical time series for a keyword. Defaults to 5 years of weekly data. Pass data_mode="daily" for the last 30 days at daily granularity.
get_growth(source, keyword, percent_growth, data_mode=None)
Calculates percentage growth between two points in time. Pass preset strings or CustomGrowthPeriod objects.
Growth presets: 7D 14D 30D 1M 2M 3M 6M 9M 12M 1Y 18M 24M 2Y 36M 3Y 48M 60M 5Y MTD QTD YTD
get_top_trends(type=None, limit=None)
Returns today's live trending items. Omit type to get all feeds at once.
Available live feeds: Google Trends Google News Top News YouTube Trending TikTok Trending Hashtags X (Twitter) Trending Reddit Hot Posts Reddit World News Wikipedia Trending Amazon Best Sellers Top Rated Amazon Best Sellers by Category App Store Top Free App Store Top Paid Google Play Spotify Top Podcasts Top Websites
All 30+ data sources
One API key. One client. Every platform. No separate credentials for each.
| source | What it measures |
|---|---|
"google search" | Google Search volume |
"google images" | Google Images search volume |
"google news" | Google News search volume |
"google shopping" | Google Shopping purchase intent |
"youtube" | YouTube search volume |
"tiktok" | TikTok hashtag volume |
"reddit" | Reddit subreddit subscribers over time |
"amazon" | Amazon product search volume |
"wikipedia" | Wikipedia page views |
"news volume" | News article mention count |
"news sentiment" | News sentiment score (positive/negative) |
"app downloads" | Mobile app download/install estimates (Android) |
"npm" | npm package weekly downloads |
"steam" | Steam concurrent player count |
All values normalized 0–100 so you can compare across platforms directly.
Error handling
from news_sentiment_mcp import TrendsMcpClient, TrendsMcpError, SOURCE
client = TrendsMcpClient(api_key="YOUR_API_KEY")
try:
series = client.get_trends(source=SOURCE, keyword="bitcoin")
except TrendsMcpError as e:
print(e.status) # e.g. 429 if you exceed your plan quota
print(e.code) # e.g. "rate_limited"
print(e.message)
Frequently asked questions
Does this scrape News Sentiment? No. trendsmcp runs managed data infrastructure. Your Python code makes a single authenticated REST call. No scraping, no Selenium, no cookies, no proxies required.
Do I need a News Sentiment developer account, OAuth token, or platform API key? No. One trendsmcp API key gives you access to all 30+ data sources.
Will it break when News Sentiment changes its backend? No. API stability is our responsibility. If something changes upstream, we update the backend. Your code keeps working.
Can I query multiple platforms with the same key?
Yes. One key covers every data source. Switch source to any of the 30+ values listed above.
Is there a free tier? Yes, 100 requests per month, no credit card required. Get your key at trendsmcp.ai.
Can I use this in production data pipelines? Yes. The client is stateless, thread-safe, and supports async for concurrent queries across multiple platforms.
Related packages
- trendsmcp — core package, all 30+ data sources
- youtube-trends-api / youtube-trends-mcp / youtube-trends-agent
- reddit-trends-api / reddit-trends-mcp / reddit-trends-agent
- google-search-trends-api / google-search-trends-mcp / google-search-trends-agent
- amazon-trends-api / amazon-trends-mcp / amazon-trends-agent
- tiktok-trends-api / tiktok-trends-mcp / tiktok-trends-agent
- wikipedia-trends-api / wikipedia-trends-mcp / wikipedia-trends-agent
- npm-trends-api / npm-trends-mcp / npm-trends-agent
- steam-trends-api / steam-trends-mcp / steam-trends-agent
- app-store-trends-api / app-store-trends-mcp / app-store-trends-agent
- news-volume-api / news-volume-mcp / news-volume-agent
- news-sentiment-api / news-sentiment-mcp / news-sentiment-agent
Links
License
MIT
Reviews
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