Fashion trend data for AI assistants

Replaced my manual Google Trends scraper in an afternoon. The data is clean and the latency is surprisingly low for a free tier.

We use it for keyword trend reports. The free monthly quota keeps us batching queries for weekly digests. Upgrading is there when we need more headroom.

Hooked it into my MCP server in like 20 minutes. The JSON response is well-structured and the docs are solid. Exactly what I needed.

We pipe weekly series into BigQuery for a few brand cohorts. Compared to maintaining our old Selenium job, this is boring in the best way. Uptime has been solid.

Great for slide-ready trend screenshots when leadership asks why we are prioritizing a feature. I wish the dashboard had saved views, but the API side is great.

Running it from Cursor with the MCP config took one try. I am not a trends person, but my side project now emails me when a niche keyword spikes hard week over week.

Using the growth endpoints to sanity-check retail names before I write up notes. Occasionally the normalization differs from what I see in the raw Google UI, but it is consistent run to run.

Pulling multi-source ranked lists into a notebook is straightforward. Error payloads are actually readable when I fat-finger a parameter, which matters more than people admit.

Does what it says. I knocked a star because onboarding assumed I already knew MCP wiring; a copy-paste block for Claude Desktop would have saved me 15 minutes.

We track TikTok hashtag momentum against paid spend in a Looker sheet. Not glamorous work, but it is the first tool my team did not argue about during rollout.

Retries are predictable and I have not seen weird HTML in responses (looking at you, scrapers). Would pay for a team key rotation flow, but for now we rotate manually.

Quick checks on retail buzz before we dig into filings. Not a silver bullet, but it is faster than opening twelve browser tabs and reconciling by hand.

Helpful for spotting whether a topic is a one-day meme or sticking around. I still cross-check with Search Console, but this gets me 80% of the signal in one call.

I demo this in workshops when people ask how to ground LLM answers in something fresher than training data. The MCP angle lands well with engineers who hate glue code.

Solid for client reporting. Billing is clear enough that finance stopped asking me what line item this is. Minor nit: peak hours can feel a touch slower, still acceptable.

I wired this behind a small CLI for contributors who want trend context in issues. Keeping the surface area tiny matters for OSS, and the schema has not churned on me yet.

Daily pulls for a 30-day window go straight into our internal scoreboard. Stakeholders finally stopped debating whose screenshot of Trends was newer.

We are pre-revenue, so free tier discipline matters. I hit the cap once during a brainstorm where everyone wanted to try random keywords. Learned to batch smarter.

Security review passed without drama: HTTPS, scoped keys, no bizarre third-party redirects in the chain we could find. That is rarer than vendors think.

I do not need this daily, but when App Store rank shifts look weird, having Reddit and news context in one place saves me from context switching across six apps.

I use it to see if a story is genuinely blowing up or just loud on one platform. It is not a replacement for reporting, but it keeps my ledes honest.

We moved off a brittle Playwright script that broke every time Google shuffled markup. Same data shape every week now, which is all I wanted from life.

Seasonal demand spikes line up with what we see in Amazon search interest here. Merch team stopped sending me screenshots from random tools that never matched.

Solid for client decks. I docked one star only because I still export to Sheets manually; a direct connector would be nice someday.

Steam concurrents plus Reddit chatter in one workflow beats our old spreadsheet ritual before milestone reviews.

Quick pulse on whether a feature name is confusing people in search before we ship copy. Cheap sanity check compared to a full survey.

Monitored from Grafana via a thin wrapper. p95 stayed under our SLO budget last month. One noisy day during a holiday but nothing alarming.

Narrative fights in meetings got shorter once we could point at the same trend line everyone agreed on. Sounds silly until you have lived through it.

Using normalized series as a weak prior in a forecasting experiment. Citation-friendly timestamps in the payload made reproducing runs less painful.

Approved for our pilot group after a quick vendor review. Would love SAML, not a blocker for our size.

YouTube search interest plus TikTok hashtags in one place helps me explain why a sponsor should care about a vertical without hand-waving.

Cron job hits the API before standup; Slack gets a compact summary. Took an afternoon to wire, has been stable for two quarters.

Useful for public-interest topics where search interest is a rough proxy for attention. I still triangulate with primary sources; this is one signal among several.

Runs in a VPC egress-only subnet with allowlisted domains. Fewer exceptions to explain to auditors than our last vendor.

Spotting when a topic is about to flood Discord saves my team from reactive moderation fires. Not perfect, but directionally right often enough.

For lean teams the ROI story writes itself. I would not build an in-house scraper for this anymore unless compliance forced it.

Examples in the docs match what the MCP actually returns. You would be surprised how rare that is in this category.

Pager stayed quiet. When something upstream flaked once, the error string told me which parameter to fix without opening logs first.

Students use it for coursework demos. Budget is tight so free tier matters; we coach them to cache aggressively.

Helps prep talking points when retail interest in our name swings after earnings. Not material disclosure, just context for Q&A prep.

Response sizes stay small enough for mobile hotspots. I hate APIs that dump megabytes for a sparkline.