Pinterest trends data for AI assistants via MCP
Track what people are discovering and saving on Pinterest. Visual search intent, rising categories, and trend momentum for fashion, home, food, and lifestyle - queryable from your AI assistant.
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100 free requests per month. No credit card, no setup fee.
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.
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How will you connect?
What Pinterest trends reveal
Pinterest operates differently from every other social platform. Users come to discover and plan - for home renovations they will start in six months, outfits they will buy next season, recipes they will try at the weekend. The save action is an explicit expression of purchase intent, not passive consumption.
This forward-looking characteristic makes Pinterest trend data valuable for several categories where traditional search volume data arrives too late:
Fashion and apparel. Pinterest trend spikes for specific garment styles, colors, and silhouettes typically precede Google Shopping search volume by four to eight weeks. Retailers and brands that track Pinterest early have a meaningful lead time advantage for inventory and campaign decisions.
Home decor and interior design. Major interior design trends, from maximalist layering to biophilic design elements, consistently appear on Pinterest months before they show up in product search demand. Pinterest is where consumers discover the language for what they want before they know what to search for.
Food and recipes. Seasonal food trends, ingredient categories, and dietary movements show up in Pinterest saves weeks before they drive Amazon demand or Google recipe searches. The TikTok-Pinterest pipeline is especially active for food trends, with viral food content generating Pinterest save spikes that precede broad retail demand.
Events and occasions. Wedding, seasonal celebration, and holiday planning on Pinterest drives meaningful demand signals well in advance of the relevant dates. Florists, event supply retailers, and gift companies have used Pinterest trend data to anticipate demand for years.
DIY and crafts. Pinterest is the primary discovery channel for DIY project categories. Search and save volume for specific craft types, project scales, and material categories is a reliable leading indicator of craft supply demand.
How to query Pinterest trends from your AI
Trends MCP makes Pinterest data accessible through four primary tools available in any MCP-compatible AI assistant.
Get historical trend data
get_trends(keyword='quiet luxury', source='pinterest', data_mode='weekly')
This returns weekly Pinterest search and save interest for the keyword "quiet luxury" over the full historical period available. The output shows whether interest is accelerating, stable, or declining, and how current momentum compares to prior peaks.
Measure growth rates
get_growth(keyword='japandi style', source='pinterest', percent_growth=['3M', '6M', '1Y'])
This returns three-month, six-month, and one-year growth rates for "japandi style" on Pinterest - the most direct measure of whether a trend is early, mid-stage, or past peak.
Compare across platforms
get_growth(keyword='linen sets', source='all', percent_growth=['3M'])
Running with source='all' shows three-month growth for "linen sets" across Pinterest, TikTok, Instagram, Google Search, Google Shopping, Amazon, and all other covered sources simultaneously. This cross-platform view confirms whether a Pinterest trend is translating into broad consumer intent or remaining within the Pinterest ecosystem.
Discover fast-growing Pinterest categories
get_ranked_trends(source='pinterest', limit=20)
This surfaces the twenty fastest-growing keywords on Pinterest right now, without requiring a starting keyword. It is the most efficient way to identify emerging categories before they become widely discussed.
Use cases
Content strategy and editorial planning. Media brands, bloggers, and content creators use Pinterest trend data to plan content that aligns with what audiences are actively discovering. A home decor publication that tracks rising Pinterest saves for "curved furniture" in January can plan a feature for March and capture organic traffic when search demand peaks.
E-commerce product sourcing. Retailers and brands sourcing new SKUs use Pinterest demand signals to identify categories worth investing in. A rising trend in a specific color palette or material type on Pinterest three months before peak season gives procurement teams actionable lead time.
Campaign timing. Advertising teams use Pinterest trend momentum to time campaign launches. Running campaigns when Pinterest interest is rising but before it peaks maximizes impression value and minimizes competition for attention.
Influencer and brand collaboration. Brands tracking which aesthetic categories are gaining Pinterest saves can identify content creators whose output aligns with rising trends before those creators become expensive to work with.
Competitive intelligence. Tracking Pinterest interest for competitor brand terms and product categories shows where competing brands are generating organic discovery, which is a useful proxy for marketing investment and product development direction.
Pinterest vs. other visual platforms
Pinterest, Instagram, and TikTok all generate visual content engagement, but their signals mean different things.
Pinterest signals planning and purchase intent. A save is a commitment to return to something. Trend spikes are genuine demand signals.
Instagram signals current cultural relevance. High engagement on Instagram indicates a trend is already popular. It is a validation signal rather than an early-warning signal.
TikTok signals emerging viral interest. TikTok trends are fast and unpredictable, often peaking and declining within weeks. They are excellent for spotting breakout moments but less reliable for sustained demand forecasting.
The strongest research workflow uses all three: TikTok to catch viral emergence, Pinterest to confirm planning intent, and Instagram to validate mainstream adoption.
Trends MCP covers all three alongside Google Search, Google Shopping, Amazon, Reddit, YouTube, Wikipedia, and more, so cross-platform comparison happens in a single session without managing multiple tools.
Getting started
Access to Pinterest trend data through Trends MCP requires a Trends MCP API key. Get a free key at trendsmcp.ai - 100 free requests per day, no credit card required.
Once connected, Pinterest data is available through the same tools used for all other sources. No separate Pinterest API key, no scraping, no rate limit management. One MCP connection covers all 15+ platforms.
Connect
Add to your AI in 30 seconds
An API key is required to connect. Get your free key above, then copy the pre-filled config for your client.
Cursor
Cursor Settings → Tools & MCP → Add a Custom MCP Server
"trends-mcp": { "url": "https://api.trendsmcp.ai/mcp", "transport": "http", "headers": { "Authorization": "Bearer YOUR_API_KEY" } }
+ Add to Cursor
Or paste into Mac / Linux — ~/.cursor/mcp.json
Windows — %USERPROFILE%\.cursor\mcp.json
↑ Get your free key above first — the config won't work without it.
Claude Desktop
User → Settings → Developer → Edit Config — add inside mcpServers
"trends-mcp": { "command": "npx", "args": [ "-y", "mcp-remote", "https://api.trendsmcp.ai/mcp", "--header", "Authorization:${AUTH_HEADER}" ], "env": { "AUTH_HEADER": "Bearer YOUR_API_KEY" } }
Mac — ~/Library/Application Support/Claude/claude_desktop_config.json
Windows — %APPDATA%\Claude\claude_desktop_config.json
Fully quit and restart Claude Desktop after saving.
Claude Code (CLI)
claude mcp add --transport http trends-mcp https://api.trendsmcp.ai/mcp \ --header "Authorization: Bearer YOUR_API_KEY"
Windsurf
Settings → Advanced Settings → Cascade → Add custom server +
"trends-mcp": { "url": "https://api.trendsmcp.ai/mcp", "transport": "http", "headers": { "Authorization": "Bearer YOUR_API_KEY" } }
Mac / Linux — ~/.codeium/windsurf/mcp_config.json
Windows — %USERPROFILE%\.codeium\windsurf\mcp_config.json
Or: Command Palette → Windsurf: Configure MCP Servers
VS Code
Extensions sidebar → search @mcp trends-mcp → Install — or paste manually into .vscode/mcp.json inside servers
"trends-mcp": { "type": "http", "url": "https://api.trendsmcp.ai/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } }
Paste into .vscode/mcp.json, or:
Command Palette (⇧⌘P / Ctrl+Shift+P) → MCP: Add Server
Data Sources
What you can query
All data is normalized to a 0-100 scale for consistent cross-platform comparison.
Tools
What your AI can call
Four tools, organized by how you start. With a keyword, track history and growth. Without one, use discovery to see ranked movers or what is live right now.
You already have a keyword.
Chart how it moves over time and compare growth across sources.
No keyword required.
Ranked lists on one source with a growth sort you choose, or a live snapshot of what is trending across platforms.
Outputs
What you get back
FAQ