# Pinterest Search API (https://www.socialcrawl.dev/platforms/pinterest/search)

> Searches Pinterest for pins matching a keyword. Each pin includes its permalink (`post.url`), the original image (`post.content.media_urls`, a single URL string on this endpoint) and a thumbnail, the pinner's username, name and avatar, and the date it was pinned. `post.content.text` is the pin's title, or its description when the title is empty; many pins carry neither, so it is null on those rows. Pinterest's own module cards on a results page ("Explore featured boards", "Shop ...") are not pins and are left out. Pin ids: as of 19/10/2026 Pinterest returns search results with an encrypted pin id, so `post.url` reads `https://www.pinterest.com/pin/e1_...` and `post.id` is the base64 of `Pin:e1_...`. The same pin kept the same id across repeated calls, so it still de-duplicates, but Pinterest does not open an `e1_` link for a signed-out reader and the per-pin lookup cannot read it. The search results themselves carry no engagement, so on a plain call `post.engagement.saves`, `.likes` (reactions), `.comments` and `.shares` are null. `include=engagement` joins each row, in the same call, to the per-pin lookup that carries the save, reaction, comment and share counts; a row whose permalink is an `e1_` link cannot be looked up, stays null and is refunded, and the response says so in `_warnings` (`engagement_unavailable` when no row was filled, `engagement_partial` when some were). Every pin on the five search pages measured on 19/10/2026 had an `e1_` link, so expect `engagement_unavailable` and no extra credits today. Cost: 1 credit for the page plus 1 credit per row filled from a fresh lookup (25 rows max, so 26 credits at most); rows already in cache are filled for free, rows that could not be filled are refunded, and a repeat of the same call within the cache window is 0 credits. Time: a plain call is about 2 seconds; `include=engagement` adds 4 to 10 seconds when the lookups fill (they run in parallel and the call waits for the slowest, never more than 12 seconds), about 3 seconds when every lookup comes back empty, and nothing when the rows are already cached. `limit=N` caps the rows and the credits together. The response carries a `hydration` block itemising rows, lookups, cache hits, credits and milliseconds. `post.engagement.views` is not published for search results and stays null either way. To page, send `pagination.next_cursor` back as `cursor`. `data.next_cursor` holds the same position in Pinterest's own form and is kept for integrations that already read it, but `pagination.next_cursor` is the one to use.

TL;DR: `GET /v1/pinterest/search` costs 1 credit for the page. include=engagement holds 1 credit per row, at most 25 (or `limit`), and keeps only the rows filled from a fresh lookup, so a page is at most 26 credits; rows served from cache are free, unfilled rows are refunded, and a repeat of the same call within the cache window costs 0. Relevance against your query (by default, or with relevance=) is free. Adding relevant_to= holds 4 extra credits and refunds down to 1 credit per started 25 rows that were newly judged: rows already judged for the same topic are free, a page where nothing could be judged refunds the whole extra, and a cached page is free. The default labels (sponsored, intent, niche) are free. label=mention, quality, ai_style, ai_written, injection, or label=intent with offer=, holds 4 extra credits and refunds down to 1 credit per started 25 posts that were newly judged: posts already labelled are free, a page where nothing could be judged refunds the whole extra, and a cached page is free. With `max_pages`, `max_items` or `max_credits`, each page walked is billed at this price (a cached page is free), and `max_credits` caps the total. and returns SocialCrawl's unified JSON schema. Single x-api-key auth, 100 free credits on signup.

## Parameters

| Parameter | Required | Description |
| --- | --- | --- |
| `query` | yes | Keyword or phrase to search Pinterest pins for. |
| `cursor` | no | Cursor to get the next page. Take it from `pagination.next_cursor` on the previous response. |
| `trim` | no | Set to true for a trimmed down version of the response |
| `include` | no | Set to `engagement` (one token only) to fill `post.engagement.saves`, `.likes` (reactions), `.comments` and `.shares` in this one call on every row the per-pin lookup can read. A row whose `post.url` is an encrypted `/pin/e1_...` link (every pin measured on 19/10/2026) cannot be read: it stays null, is refunded, and `_warnings` carries `engagement_unavailable` (no row filled) or `engagement_partial` (some filled). Holds 1 credit per row (at most 25, or `limit`) and keeps only the rows filled from a fresh lookup; rows already in cache and rows that could not be filled are refunded, so the most a call can cost is 1 + rows and the usual bill is lower. Adds 4 to 10 seconds on a fresh page (never more than 12) and nothing when the rows are already cached; read `data.hydration` for the rows, the credits held and kept, and the time. (engagement) |
| `limit` | no | Take the top N rows of the page (1 to 25) after the search has run. With `include=engagement` it also caps the extra credits at N: `limit=5&include=engagement` costs at most 6. It is not a page size: `next_cursor` still advances past the full page, so rows beyond N on this page are not returned by the next page. |
| `relevance` | no | Optional. Without this param every row already carries computed.relevance against your query, free ({ p, sense, depth, spam }: is this row about what your query means, or a different thing that shares its words?), and nothing is dropped or reordered. score asks for it explicitly and waits for every row; filter also drops the rows that are not about your query, and lists their ids in data.relevance.dropped_ids. Your query is the topic; nothing to configure. A row that could not be judged is never dropped and carries relevance: null. Pagination is unchanged, so a filtered page can hold fewer rows. Free with your query as the topic; with relevant_to it adds 1 credit per started 25 newly judged rows (rows already judged for the same topic are free, and so is a cached page). (score \| filter) |
| `relevance_threshold` | no | Optional, only with relevance. The probability (0 to 1) a row must reach to be kept by relevance=filter. Default 0.5. Lower keeps more rows, higher keeps fewer. |
| `relevant_to` | no | Optional, only with relevance. Up to 200 characters describing what you mean, used as the topic instead of the query. Use it when the query is ambiguous, for example query=cleopatra with relevant_to=Cleopatra, the IGT slot game. |
| `judgments` | no | Optional, on (the default) or off. By default every row gains free SocialCrawl judgments (computed.labels, and computed.relevance on search endpoints), reported in data.labels (mode default) and data.relevance (origin default), each with a status (complete, partial or skipped) and pending: the rows still being judged when the page was sent, which carry null now and are filled on your next call or cached read. Default judgments never add credits, never change an existing field, and never drop or reorder a row. off returns the page exactly as before, with none of those keys. label=none does the same. (on \| off) |
| `dry_run` | no | Optional. When 1, return a cost preview for this labelled or relevance-filtered request without fetching the page or judging any row. data.estimate reports rows_expected, rows_cached, label_credits_min, label_credits_max and base_credits. 0 credits charged. (1) |
| `fit` | no | Optional. When goal, keep the rows and fields needed for the goal you pass in goal= (plus any that are uncertain, and the first and last), and replace the rest with a stub. data.held_back lists the held ids and a recall id that re-reads the full page from cache at no extra charge. Without this param the page is unchanged. (goal) |
| `goal` | no | Required by fit=goal. What you are trying to do, in your own words, up to 300 characters. |
| `fit_tokens` | no | Optional, only with fit=goal. Soft cap on how much of the page to keep, in tokens. Uncertain blocks and the first and last block are kept even if they exceed it. |
| `label` | no | Optional CSV of SocialCrawl labels to add to every post. Without this param every page already carries sponsored, intent and niche, free; label= adds the labels you name to them (the defaults keep running). sponsored, intent and niche are free when asked for too; mention, quality, ai_style, intent with offer=, and injection add 1 credit per started 25 newly judged posts. judgments=off (or label=none) turns the default labels off. sponsored: is the post a paid or gifted promotion (p, 0 to 1), did it carry a disclosure marker such as #ad or 광고 (disclosed), is it likely paid with no marker (undisclosed), and which of the accounts it mentions does it promote (brand, or null). These are signals to review, never a finding. mention (needs brand=): is the post about that brand rather than something that shares its name (about_brand, 0 to 1), how it feels about the brand on five levels (sentiment_level 0 to 4 and sentiment_score 0 to 1, null when the post is not about the brand), is it sarcastic, which aspect it talks about (taste_or_quality, price_or_value, availability_or_delivery, health_or_safety, advertising_or_campaign, customer_service, none), and did the author buy or use it (first_hand). intent: what the author is mainly doing (label: asking_for_recommendation, comparing_options, switching_away, complaining, promoting, news_or_discussion, other, or null when unsure, with confidence), whether they read as a potential buyer rather than a seller (buyer, seller), how pressing the need is (urgency, 0 to 3), and, when you pass offer=, whether your offer would plausibly help them (fits_offer). niche: which of the 33 niches of the published taxonomy sc-niche-v1 the caption belongs to, or personal_no_niche, or other (label, confidence, taxonomy), with label null when the caption is too thin to tell or the pick is unsure. It reads the caption only, not the video. quality: how much checkable detail the caption carries (fact_density 0 to 3), whether it mainly asks for likes, replies, shares, follows or tags (engagement_bait, 0 to 1), whether it is written to provoke anger as a way to get engagement (rage_bait, 0 to 1; about the writing, never the side taken), whether it only repeats someone else's news or view (secondhand, 0 to 1), and what the post is mainly doing (post_aim: inform, opinion, sell, entertain, provoke, other). exclude=engagement_bait drops posts whose engagement_bait is 0.8 or higher. ai_style: writing patterns common in AI-generated text, judged on the text alone. It does not say who wrote the post, and a person who writes in this style is flagged too. Six patterns are judged, each a probability (0 to 1): binary_contrast ("This is not X. It's Y."), faux_insight ("Here's what nobody tells you" and throat-clearing openers), colon_reveal ("The best part: it learns."), profound_kicker (ends on an aphorism or mic-drop line), puffery ("marks a pivotal moment", "highlighting the importance of") and rhetorical_setup ("What if I told you", a question the post answers itself, "Not a X. Not a Y. A Z."). Four are counted in the text: slop_words (words and phrases such as delve, leverage, robust, game changer, at the end of the day), em_dashes, emoji_bullets (lines that start with an emoji or a symbol bullet) and one_line_paragraphs (paragraphs that are one short line). pattern_count is how many patterns are present (a judged pattern at 0.8 or higher, slop_words 1 or more, em_dashes 2 or more, emoji_bullets 2 or more, one_line_paragraphs 5 or more) and markers lists their names, comma-separated. A post with less than about eight words of text is only counted, with every judged pattern 0. The judged patterns read the first 400 and the last 150 characters of a long post, and the counts read the whole post. exclude=ai_style drops posts with 2 or more patterns. ai_written: how likely the post was written by an AI model, from SocialCrawl's own text classifier, reading the opening of the post (about 1,000 characters): p (0 to 1, calibrated), level (likely (p 0.95 or higher), possible (p 0.71 or higher) or unlikely) and model (the model name; a new name means a new scale). A post under 8 words is too short to read: p null, level too_short. It is a probability, not proof: a person can be flagged and an AI-written post can pass, and the shorter the post the less certain it is. exclude=ai_written drops likely posts. injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. A post that could not be judged carries labels: null. Posts already labelled for anyone are free, and so is a cached page. data.labels reports what was judged and billed. |
| `exclude` | no | Optional CSV (engagement_bait, ai_style, ai_written). engagement_bait, only with label=quality, drops posts whose engagement_bait probability is 0.8 or higher. ai_style, only with label=ai_style, drops posts that show 2 or more of its writing patterns (pattern_count 2 or higher). ai_written, only with label=ai_written, drops posts whose level is likely (p 0.95 or higher), or whose p reaches your ai_written_threshold; a post too short to read is never dropped. Dropped posts are listed in data.labels.dropped_ids. A post that could not be judged is never dropped. |
| `ai_written_threshold` | no | Optional, only with label=ai_written and exclude=ai_written. The p (0 to 1) at which exclude=ai_written drops a post. Default 0.95 (level likely). 0.71 also drops possible posts: more AI posts go, and more posts by people with them. |
| `brand` | no | Required by label=mention, ignored otherwise. The brand or product the posts are judged against, up to 300 characters, for example brand=Buldak. Without it label=mention is skipped with the warning label_mention_needs_brand and is not billed. |
| `brand_description` | no | Optional, with label=mention. One plain sentence saying what the brand is, for example brand_description=Samyang's spicy instant noodle brand. Use it when the name is also an ordinary word or another company's name. |
| `offer` | no | Optional, with label=intent. One or two plain sentences saying what you sell, up to 300 characters, for example offer=A web design agency that builds websites for small businesses. Adds fits_offer to every post; without it fits_offer is null. |
| `label_evidence` | no | Optional, only with label=. When 1, every labelled row also carries computed.labels_evidence.<preset> = { quote, sentence_index }: the sentence in the row that most clearly shows the label, copied verbatim. Absent or null when no single sentence shows it. (1) |
| `since` | no | Only rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off each page. This list is not in date order, so the walk does not stop at the date: keep following `next_cursor`, and each page costs what a page costs even when every row on it is filtered out. A row with no date is kept. `published_after`, `start_date`, `from_date`, `publish_time` (`yesterday`, `this-week`, `this-month`, `last-3-months`, `last-6-months`) and `max_age_days` are accepted and applied as `since`. |
| `until` | no | Only rows published on or before this date: YYYY-MM-DD (that whole UTC day is included) or an ISO 8601 timestamp. Newer rows are left off each page and the walk does not stop, so older pages follow. A row with no date is kept. `published_before`, `end_date` and `to_date` are accepted and applied as `until`. |
| `max_pages` | no | 1 to 50 (default 1, or 50 when `max_items` or `max_credits` is sent). Walk up to this many pages in one call and return the rows from all of them. Each page walked is billed exactly as one call to this endpoint (a cached page is free) and counts against your rate limit; its charge and any refund are recorded under the page's own request id, listed in `data.walk.request_ids`. The walk stops early at the last page, or when the request's time budget runs low; `data.walk.stopped` says why (`end`, `max_pages`, `max_items`, `max_credits`, `time_budget`, `page_error`) and `pagination.next_cursor` continues from where it stopped. |
| `max_items` | no | 1 to 5000. Get more in one call: keep walking pages until the response holds at least this many rows, then stop with `data.walk.stopped: "max_items"`. The last page is returned whole (it was billed), so `data.items` can hold up to one page more than this, and `pagination.next_cursor` resumes right after it without skipping a row. Walks up to 50 pages unless `max_pages` says fewer; each page is billed as one call (a cached page is free). Add `max_credits` to cap the spend. A long walk can stop on the time budget first (`"time_budget"`): send the returned cursor with the same params to continue. |
| `max_credits` | no | 1 to 100000. The most this call may spend, across every page it walks. A page starts only while the credits already spent plus that page's price (or, when higher, the most any page of this walk has cost) still fit, so the call never goes past it; the walk then stops with `data.walk.stopped: "max_credits"` and the cursor to resume. Sent alone, it walks as far as the budget goes (up to 50 pages). A value below the price of one page is a free 400 whose `error.details.page_credits` names that price. Credits a row join (`include=`) adds are known only once a page has run, so page 1 is checked against its own price. |

## Code example

```bash
curl -G "https://www.socialcrawl.dev/v1/pinterest/search" \
  --data-urlencode "query=home decor ideas" \
  -H "x-api-key: sc_YOUR_API_KEY"
```

## FAQ

### How do I search Pinterest pins by keyword with an API?

Send a GET request to /v1/pinterest/search with a query parameter. SocialCrawl returns matching pins with their text, full-size image URLs, author info, and the pin date, ranked by Pinterest relevance. Add include=engagement and every row also carries its save, reaction, comment, and share counts.

### What data does each Pinterest search result include?

Each result returns pin id, permalink, text (the pin's title, or its description when the title is empty), image URLs at multiple resolutions, author info, and the pin date. The counts arrive with include=engagement, at 1 extra credit per row filled. All normalised into the unified SocialCrawl post schema.

### Can I paginate through Pinterest search results?

Yes. Pass the cursor parameter returned in the previous response to fetch the next page. Combined with trim=true, this makes it easy to stream thousands of pins for trend analysis without blowing up bandwidth.

### How is this different from the official Pinterest API?

The official Pinterest API requires OAuth, a business account, and app review, and does not expose keyword pin search at the depth SocialCrawl does. Our search endpoint works with any x-api-key, no Pinterest login.

### How much does the Pinterest Search API cost?

Each search costs 1 credit on the standard tier. New accounts get 100 free credits with no credit card required, enough to run 100 Pinterest keyword searches before you pay anything.

See the full Pinterest API: https://www.socialcrawl.dev/platforms/pinterest

## Pricing

- Standard endpoints: 1 credit per call
- Advanced endpoints: 5 credits per call
- Premium endpoints: 10 credits per call
- 100 free credits on signup, no credit card required. Cached responses cost 0 credits. Credit packs never expire.
- Full pricing: https://www.socialcrawl.dev/pricing

## Explore with AI

Questions this API answers, phrased for an AI assistant:

- How to search Pinterest pins by keyword with an API
- What does the SocialCrawl Pinterest Search endpoint return?