trendspyg is a capable open-source library for Google Trends RSS and CSV pulls. Teams that outgrow single-platform scrapers often need keyword history, growth math, and live feeds across search, social, and commerce in one contract their AI stack can call reliably.
Developers searching for a trendspyg alternative usually already fixed broken pytrends jobs. trendspyg (see flack0x/trendspyg on GitHub and trendspyg on PyPI) answers a narrow job well: pull the public Google Trends RSS feed in about two tenths of a second, optionally escalate to CSV mode for hundreds of rows, and export dicts or DataFrames for notebooks. That pattern shows up everywhere in 2026 automation tutorials, including n8n workflow templates that treat the RSS URL as the discovery layer.
The friction appears on the next step. Content strategists, ecommerce analysts, and SEO agents rarely stop at "what is trending on Google today." They ask whether a product keyword is climbing on Amazon, whether TikTok hashtags led Google by a month, and whether three-month growth on YouTube still justifies a shoot. trendspyg does not ship those answers. Trends MCP does, through the same MCP endpoint and REST body shape documented at https://trendsmcp.ai/docs.
trendspyg earns its install base on speed and cost for Google-only discovery.
| Capability | trendspyg RSS | trendspyg CSV | Trends MCP |
|---|---|---|---|
| Typical latency | About 0.2s | About 10s, may need Chrome | Network API call |
| Trending list size | Roughly 5 to 25 terms | Hundreds with filters | Live feeds plus keyword history |
| Linked news articles in feed | Yes on RSS | No on CSV path | Use Google News sources separately |
| Five-year keyword series | No | Partial via scraping | Yes via get_trends |
| Amazon, TikTok, YouTube on same keyword | No | No | Yes via get_growth |
| Agent-ready JSON without custom code | Manual export | Manual export | MCP tools and REST |
For a solo analyst who only needs today's US trending list inside a Python script, trendspyg remains rational. The README's country and category matrix is enormous, and the five-minute cache keeps repeat cron jobs polite to Google.
RSS snapshots are discovery, not monitoring. Each row is a moment in time. Building a chart that asks "how did heat pump interest move since 2022?" requires another pipeline, often pytrends-modern or a Selenium-backed CSV pull. Those paths reintroduce the maintenance tax trendspyg was meant to reduce.
Hosted automations compound the risk. n8n content-factory guides on sites like Easy Workflows chain RSS ingestion to OpenAI nodes. When Google tweaks feed markup or CSV access, the workflow owner debugs parsers, not copy. Apify actors such as Google Trends Tracker outsource that fragility for a per-run fee, yet still center on Google.
AI assistants raise the bar further. A marketing lead in Cursor wants a single prompt: "Using TrendsMCP, compare 12M Google Search, Amazon, and TikTok growth for standing desk." trendspyg cannot answer that without additional scrapers, normalisation logic, and glue code the model will not maintain.
An SEO specialist comparing content angles needs stable week-over-week series, not a refreshed RSS list. get_trends on google search returns normalized 0 to 100 scores plus volume fields where the pipeline has them. Pair google news when the question is which publishers already cover a spike. For a broader playbook, see https://trendsmcp.ai/seo-keyword-research.
Amazon demand often leads Google informational queries on transactional categories. Trends MCP exposes amazon as a first-class source, so a product researcher can call get_growth with source='google search, amazon, youtube' and read directional growth in one response. That mirrors how sellers reason about launch timing without opening three browser tabs. The ecommerce research page at https://trendsmcp.ai/ecommerce-product-research goes deeper on purchase-intent signals.
The managed surface is intentionally boring: POST https://api.trendsmcp.ai/api with Authorization: Bearer YOUR_API_KEY. Keyword operations need source and keyword. Live discovery uses mode: "top_trends" and a feed type such as Google Trends. Agents connect through https://api.trendsmcp.ai/mcp with the same token, which means Claude, Cursor, VS Code, and ChatGPT can call tools without a local Python environment.
Example REST body for cross-platform growth:
{
"source": "google search, amazon, youtube",
"keyword": "portable sauna",
"percent_growth": ["3M", "12M", "YTD"]
}
Example MCP-style prompt after connecting the server:
Using TrendsMCP, show 3M and 12M growth for portable sauna on Google Search, Amazon, and YouTube, then list today's Google Trends leaders.
Teams already running RSS in n8n can keep trendspyg for experiments while promoting production paths to HTTP Request nodes against Trends MCP. That swap removes Chrome workers from the fleet and lets downstream LLM nodes consume JSON growth metrics instead of raw XML. Step-by-step wiring lives at https://trendsmcp.ai/n8n-trends-mcp.
Readers comparing scrapers should still read https://trendsmcp.ai/pytrends-alternative for archived pytrends migrations and https://trendsmcp.ai/google-trends-api for Google's own alpha API waitlist. trendspyg sits between those options: more reliable than pytrends for RSS, still Google-bound, and still operator-maintained.
Trends MCP is the path when the organization standardizes on one trend contract for humans and agents alike. Free tier includes 100 requests per month at https://trendsmcp.ai/account. Python pipelines can install trendsmcp on PyPI while MCP hosts use the HTTP connector from https://trendsmcp.ai/mcp-server-for-cursor or the matching guide for their client.
download_google_trends_rss. Keep them if the output is a morning briefing email with headlines.get_trends and get_growth.source string from the docs table.Tools for this workflow
get_trendsPull up to five years of weekly Google Search interest for a keyword, with optional daily mode for the last thirty days, instead of stitching RSS snapshots by hand.
get_trends(keyword='heat pump', source='google search', data_mode='weekly')
get_growthReturn 3M, 12M, or YTD growth on Google plus Amazon and YouTube in one agent turn, which trendspyg cannot express without separate scrapers per platform.
get_growth(keyword='heat pump', source='google search, amazon, youtube', percent_growth=['3M', '12M', 'YTD'])
get_top_trendsReplace a daily RSS poll with ranked live leaders on Google Trends, TikTok Trending Hashtags, Reddit Hot Posts, and other feeds using the same JSON shape.
get_top_trends(type='Google Trends', limit=25)
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