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Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summarize a huge blob cheaply, or query a specific model the user names (Kimi, GPT, Gemini, Claude, DeepSeek, and 300+ more). `model` accepts 'auto' (default: short prompts go cheap, long go mid), 'cheap' | 'mid' | 'best' tiers, or any exact OpenRouter slug like 'moonshotai/kimi-k3'. Typical costs: cheap tier well under 0.1 cents, best tier 1-3 cents per call. Not for the conversation you are already having — it is a one-shot ask to another model.

[Local Maps via Google Maps] Local-business search (data source: Google Maps; use must comply with Google Terms of Service). Search local businesses at a given lat/lng — returns name, address, rating, review count, etc. Use when: user says "Y businesses in city X" / "local retail research" / "offline channel distribution" / "coffee shops/supermarkets/wholesalers in area" / "physical-store coverage density"; offline competitor/channel research; gauging physical-supply density of a category in a region. Don't use: for e-commerce listings (Amazon series); for global trends (use keyword_trends); for Google search results (use ai_search). Returns: data.organicResults[{ place_id, name, about, rating, number_of_reviews, borough, street_addr, city, postal_code, ... }]. Pair with: ↑ query (business keyword) + latitude/longitude/zoom (zoom 1=world, 13=city, 21=single building); ↓ presentation-focused, downstream rarely consumes. Cost: ~1.5 points/call, ~5s. Tips: zoom 13 (city, default) gives you a whole neighborhood; zoom 17+ narrows to one street.

[Design Patent TRO risk control · WIPO global design / IP search] Query the WIPO design database across 12 sources (USPTO US designs, CNID China, HAGUE international registrations, …), with one-click chaining to US design-patent TRO (temporary restraining order) / litigation risk control. Use when: user says "check trademark" / "design patent search" / "any IP risk for new product" / "X company's patent portfolio" / "WIPO search" / "USPTO query" / "what is registration DM/XXX"; pre-launch IP clearance during scouting/GTM SOPs; competitor IP-portfolio research. Don't use: for keyword ranks / product reviews / product detail (this is an IP database, not a commerce database); for US text-trademark search (this DB focuses on design patents — text trademark coverage is limited). Returns: data.data.{ total, hits[{ IRN, HOL[], DETAIL_DATA.structured.{indication_of_products, statement_of_novelty, ...}, IMG[], IMG_DATA[{filename,url}], DC, RD, STATUS, LCS[], DS[], PROD[], SOURCE, DETAIL_URL }] }. With enableLitigation=true each matched patent additionally carries litigationStatus(success/skipped/failed) + caseTotal + cases[{ caseId, docketNumber, caseName, court, status, dateFiled, parties[], patentNumbers[], entries[] }] (backed by US PACER litigation data — one call returns patents + lawsuits). Pair with: ↑ source required; hol=holder name / prod=product name / irn=international registration / lcs=design classification; enableLitigation=true chains US litigation lookup (IP-risk loop, no separate tool needed); ↓ DETAIL_URL lets the user jump to WIPO's official page to verify. Cost: ~2 points/call, ~5s; with enableLitigation=true add +12 points only when a patent is found (free if none). ⚠️ Perf contract: CNID + hol/prod MUST be paired with id/idSearch/rd/status/lcs (otherwise the backend rejects to avoid a 17M-row full scan); JPID has no HOL/PROD; USID has no STATUS; ed (expiration date) is silently ignored on all sources — filter dates via rd instead. With enableLitigation on, each page re-triggers the litigation query and billing.

[AI Search via Google SERP] Scrape publicly-available Google search results (data source: Google; use must comply with Google Terms of Service) with top AI Overview, organic results, and related searches. Two modes: overview (standard SERP) / ai_mode (immersive multi-turn conversational search). Use when: user says "Google for me" / "external demand" / "what do people say about X" / "Reddit/Quora pain points" / "will my content be cited in AI search" / "find user complaints for keyword X"; "consumer voice" step in scouting SOPs; verifying whether a new product concept has off-Amazon demand; **see which Google Shopping ads competitors run / their ad landing pages** (the sponsered block). Don't use: for on-Amazon search (use search_amazon); when only the trend curve matters (use keyword_trends — cheaper and tighter). Returns: data.{ results_num, ai_overview, json.items[ { type:'ai_overview', items:[{content:[...], references:[{title,url,domain}]}] }, { type:'organic', items:[{title,url,text}] }, { type:'related_searches', items:[...] }, { type:'sponsered', items:[{type:'result', url, position:'top'|'bottom', title_of_page, title_above_url}] } ], screenshot, taskId }. ⚠️ The ad block's upstream type is literally spelled 'sponsered' (missing an o — not a typo on our side; match it verbatim, do NOT look for 'sponsored') — it carries Google ad (shopping + text) landing-page url, title (title_of_page), and displayed brand domain (title_above_url). **position** marks whether the ad appears at the top ('top') or bottom ('bottom') of the page — top ads carry higher exposure weight. Pair with: ↑ query inferred from user; in 'ai_mode' pass followups[1..5] for multi-turn; ↓ ai_overview.references[].url for authoritative external sources, organic items for content-competition analysis, sponsered[].url + title_above_url for competitors' paid landing pages and brands, split by position into top/bottom ad slots. Cost: ~2 points/call, ~30s (**slow** — Google AI render time). Tips: prefer overview for single queries (cheaper); use ai_mode only when you need decomposed multi-turn investigation. Followups > 5 visibly slow down responses.

[Keyword Trends via Google Trends] Keyword popularity (data source: Google Trends; use must comply with Google Terms of Service). Time series + per-region heatmap + rising related queries (with 'Breakout' tags). Compare up to 5 keywords on one chart. Use when: user says "how hot is keyword X" / "A vs B popularity" / "any seasonality" / "which states love X" / "find breakout terms" / "new-product direction" / "trend comparison" / "is X past its peak yet". Don't use: for absolute search volume (Trends is 0-100 relative); for products/links (use search_amazon / ai_search); for a single keyword's snapshot (need ≥ 2 for meaningful comparison). Returns: data.json.{ keywordsGeoData[{ keyword, geoMapData[{ geoCode, geoName, value[], formattedValue[], hasData[] }] }], keywordsRankData[{ keyword, rankList[{ rankedKeyword[{ query, value, formattedValue, link, hasData }] }] }], timelineData[{ time, formattedTime, value[], formattedValue[] }], geoMapData[] }, taskId, url. Pair with: ↑ keywords from user or core terms found via search_amazon; ↓ feed Breakout/rising terms back into search_amazon to explore new opportunities, or filter_niches to see if they've crystallized into a niche. Cost: ~1.5 points/call, ~5s. Tips: timeRange = today 12-m (default) | today 3-m | today 5-y | all ; region = ISO country code or 'WORLD'; language affects related-query language.

Find any resource in Clueso by type, optionally filtered by name or exact id. One tool for listing and searching across the workspace. type: • projects | folders | clueprints | workspaces • backgrounds | voices | image_gen_style_packs | element_components • images | videos | music | sfx — media; each result carries a `source` ('org' = your saved-media library, 'stock' = a stock/curated provider). Scope with `source`, pick the library with `provider` (see below). Stock results are a short described shortlist — pick the best fit and use its `src`. Stock video results also carry `safe_src` and a `video_files` tier list with one entry marked `recommended` — use `safe_src` (or the recommended tier) in add_elements; tiers above 1080p can exceed its ~200MB source cap and fail. For a Freesound music/sfx result, `src` is an OPAQUE handle (not a playable URL) — pass it straight to add_audio and the original is fetched + hosted by Clueso server-side; a `preview_url` is included only so you can tell what it sounds like. (image_gen_style_packs = generation style presets for generate_media kind='image' style_id; element_components = saved components (e.g. animations) from THIS WORKSPACE only — there is no community library for components (unlike clueprints); each reports param_keys. Insert one AS-IS with add_elements(component_id=...), or generate a variant from it with base_component_id.) Filters (all optional): • query — for stock media it's the search phrase (real semantic search for provider='clueso'; provider keyword search otherwise). For clueprints a query runs a relevance-ranked search across your workspace + the global community library (search_summary, relevance_reason, tags, is_community, fork_count). For everything else it's a case-insensitive name substring. • provider — which stock library to search (ONE call, no merging). Choose by strength: images → 'pexels' (default; realistic photography) or 'pixabay' (illustrations, vectors, icons, clip-art — set image_type) videos → 'pexels' (default; real-world footage) or 'pixabay' (motion graphics — set video_type='animation') music → 'clueso' (default; our curated, brand-safe library with the best descriptions/search — try this FIRST) or 'freesound' (niche/genre tracks) sfx → 'freesound' (default; vast sound-effect library) or 'clueso' (curated sfx) • image_type — images + provider='pixabay': 'photo' | 'illustration' | 'vector' • video_type — videos + provider='pixabay': 'film' | 'animation' • id — exact id; returns just that one record (any type) • source — media only: 'org' | 'stock' | 'all' (default = org + stock). Under 'all', stock is appended only when a query is given. sfx is stock only. • folder_id — projects + saved media (images/videos/music): restrict to a folder • engine / language — voices only • creator_id / mine_only — clueprints only • orientation — stock images/videos: 'landscape' | 'portrait' | 'square' • color — stock images: a color name/hex, e.g. 'blue' • size — stock videos: 'large' | 'medium' | 'small' • min_duration / max_duration — stock videos + freesound audio: length bounds in seconds • page / limit — paging for large sets (projects, components, clueprints — a clueprint list is sliced to the limit with no marker when more exist, so page through rather than assuming the first page is everything); stock media ignores these (fixed shortlist) Returns { type, count, items: [{ id, name, type, ... }] }. Feed the returned id straight into the consuming tool (set_voice, update_clips background, generate_media style_id, add_audio src, use_clueprint, etc.). Any `duration` on a returned item is in SECONDS — pass it straight to add_audio's source_duration.

查询 npm 包的最新版本信息。输入 npm 包名（如 react、vue、express、@vue/cli），返回最新版本号 latest_version、该版本发布时间 published_at（UTC ISO 8601）、包描述 description、开源协议 license，以及主页/仓库地址等。数据源优先官方 registry.npmjs.org，失败自动兜底淘宝镜像 registry.npmmirror.com（国内更快）。包不存在返回明确的 404 提示。网络 I/O 走线程池不阻塞事件循环。

Search videos inside one channel using YouTube's native relevance search. Results are ranked by relevance, so a video whose title lacks the query word is normal.

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

Real-time X/social signal for a topic: scored (virality + freshness) and CITED (source urls), deduped and ranked. Input: query (topic) OR candidates[] (bring your own posts to score). x402-paid at GET/POST /signal ($0.01; 3 free per wallet via ?wallet=0x…). Example: GET /signal?q=base+memecoin

Returns the AI video, image and audio models this API can generate with, each with the exact parameters it accepts (names, allowed values, limits, defaults where it has them), the media inputs it takes, and its pricing. WHEN: always call this before the first `generate_video`, `generate_image` or `generate_audio` of a session. Model names cannot be guessed, and neither can which resolutions, durations or aspect ratios a given model allows - `generate_*` rejects a combination this endpoint does not publish. The one exception is a speech model's `voice`, whose published `options` are a RECOMMENDED set rather than a closed one: prefer them, but a voice id the user gives you is accepted too. NARROW IT, DO NOT PULL IT. The unfiltered listing is several thousand tokens, most of it pricing tables, and every argument below SELECTS from it without changing a single entry. `query` is free text - send the user's own words (`query: 'vertical product ad'`) and read `ignored_query_terms` on the way back: those are the words that match nothing in the catalog and were dropped, so if it comes back holding most of your sentence, the narrowing you got was smaller than you asked for. `tags` is the closed vocabulary, ANDed - `tags: ['video', 'vertical', 'reference-image']` is the precise version of that same question. `type` is the product, `model` is one id once you know it. THE TAGS, and every one of them is DERIVED from a field on the entry beside it rather than hand-labelled: `video` / `image` / `audio` (the product); `speech` / `music` / `sound-effects` (which audio product); `vertical` / `horizontal` / `square` (computed from the model's own `aspect_ratios`, so a model with several carries several); `image-input` and its detail `start-image` / `end-image` / `reference-image`, or `text-only` when the model takes no picture at all; `native-audio` when the model can generate its own sound. `tag_vocabulary` rides on every answer, so you never have to guess one - an unknown tag is a 400 naming the whole set, not an empty list. THERE IS NO DURATION TAG on purpose: any cut between 'short' and 'long' would be taste frozen into a catalog. Durations are searchable instead (`query: '10'` finds the models offering a 10-second clip) and every entry carries its full `durations` array. IT PAGES ONLY IF YOU ASK. With no `limit` you get every matching model, which is this endpoint's long-standing behaviour and is fine once you have filtered. Send `limit` when you have not: the answer then carries `total_matching`, `has_more` and `next_cursor`, and READ THEM before telling a user something does not exist - a page is not the catalog. Continue with `cursor` set to that `next_cursor` and the SAME `query` / `tags` / `type`; changing a filter invalidates the cursor and is a 400, not a silent restart. PRICING COMES IN TWO SHAPES, and each entry carries exactly one. Video and image models carry `pricing`, an enumerated table: match the row whose resolution, duration, quality and audio equal the settings you intend to send, and that is what the generation costs. Audio models carry `pricing_rates` instead - a rate over an input with no fixed set of values (per character of speech, per minute of music, per second of sound effect) - so there is no row to match and you must NOT multiply the rate out yourself. Price those with `estimate_only: true` on `generate_audio`. OUTPUT: this returns JSON for you to read. When you report back to the user, give them the media URL plus a one-line summary. Do not paste the raw JSON, job ids, or internal field names into the conversation.

Escape hatch: call any path on the YouTube backend. Pass path (e.g. /search), optional method (default GET), query map, and JSON body for POST. Prefer the named youtube_* tools when available; this is for routes not in the manifest.

Browse the catalog of background structures. Optionally filter by a free-text query, tag or mood word. Returns name, one-line summary, mood, use cases and a preview URL for each.

Return origin, host, and path for a URL. Query and fragment are dropped.

Count characters in a search query. Query discarded.

✅ No API key needed — call this now. Listing: nasaiotd: NASA Image of the Day. Price 0.01 USDC/query (max 20 queries/session). Sample questions: What is NASA's latest Image of the Day?; Show recent astronomy photos from NASA this week. FREE preview — no key, no payment. Try one of the sample questions now.

✅ No API key needed — call this now. Free — no wallet needed. Call register on this session to unlock the purchase tools for nasaiotd: NASA Image of the Day (0.01 USDC/query).

Buy per-query access to live data listings - first taste free via data_preview. Listing: nasaiotd: NASA Image of the Day (0.01 USDC/query (max 20 queries/session)). Open a prepaid session, then fund and query.

Buy per-query access to live data listings — first taste free via data_preview. Listing: nasaiotd: NASA Image of the Day (0.01 USDC/query). Returns fund instructions after data_session_open.

Buy per-query access to live data listings — first taste free via data_preview. Listing: nasaiotd: NASA Image of the Day (0.01 USDC/query). Platform-executes funding so you can data_session_query.

Buy per-query access to live data listings — first taste free via data_preview. Requires an agent API key (Authorization: Bearer or X-API-Key). Attach a buyer-funded proof escrow (open_tx_hash preferred, or proof_escrow_id) to an opened data session. Not guest-callable. REST: POST /api/v1/data-sessions/{session_id}/attach-escrow.

START HERE for any clip workflow on a video — `find_clips` is the canonical entry point and includes a full transcription as a free byproduct. **Do not call `transcribe` first**: doing so doubles the upload, doubles the spend, and produces the same transcript. Identify ranked candidate clips in a video — what to cut for highlights, social, or testimonials. Three-call flow: (1) call with `filename` (and optional `query`) to receive {job_id, payment_challenge}; (2) pay via MPP, then call with `job_id` + `payment_credential` to receive {upload_url} (presigned PUT, 1h expiry); (3) PUT the bytes, then complete_upload(job_id), then poll get_job_status(job_id). On completion, get_job_status returns three outputs: role `clip-candidates` (JSON matching /.well-known/weftly-clips-v1.schema.json — includes `source_job_id` and `source_expires_at`), role `transcript` (SRT, free byproduct), role `transcript-words` (JSON matching /.well-known/weftly-transcript-v2.schema.json, free byproduct). Each candidate carries `transcript_text` — the full text of what's in the clip — so callers can preview content before paying for extract_clip. Optional `query` parameter switches to query mode (e.g., "they discuss pricing", "the part about hiring") with the same output shape; the `mode` field in clip-candidates.json indicates which mode produced the result. Flat price: $2.00 video — see /.well-known/mpp.json. **Source-reuse contract:** the source video stays in storage for 72h after find_clips completes. Hand the find_clips `job_id` (also returned as `source_job_id` in the candidates JSON) to `extract_clip` or `extract_vertical_clip` as their `source_job_id` — within those 72h they cut directly from the stored source: no re-upload, no re-transcribe, just $0.50 per cut. Pass the same `source_job_id` to as many extract calls as you need. Use for interviews, podcasts, sales calls, all-hands recordings. Retrying with `job_id` alone returns current state. Failed jobs auto-refund.

Return origin, host, and path for a URL. Query and fragment are dropped.

Count characters in a search query. Query discarded.

Searches the components of every package assigned to the project by use case, behavior, or description, and returns up to 10 ranked matches with a relevance score. Read-only. Use it when you can describe what the UI must do but do not know the component name. The query is free-text describing the need, such as "date range picker" or "dismissible inline warning" - not a component name, not a glob, and not a props expression. Do not use it when the exact name is known (get_component is direct and returns the full spec) or when you want the complete catalog (list_components). Semantic ranking needs AI features on the Team plan or above; on other plans it falls back to name and import-path matching and the score is null. No close match returns an empty result list, not an error.

Break a URL into its components: scheme, host, port, path, decoded query parameters and fragment. Use to inspect or debug a URL instead of parsing it by eye.

Change history {who, change, entity, kind, when}, newest first; filter kind/who/query.

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

Search a PUBLIC, Creative-Commons + public-domain image pool (Openverse) for the query, download the top suitable result, store it in the project's asset bucket, and return the filename ready for add_image_layer. This finds someone else's openly-licensed image — it is NOT for uploading your own image (use upload_image for that). Use it for any "get me a parasol" / "find a beach photo" / "add a coffee cup" request — saves the upload-then-add_image_layer dance. Free; no model spend. Falls back to 404 when no suitable downloadable image is found. Always returns attribution { creator, creator_url, title, license, license_version, source_url } per the CC requirement; the editor/caller should surface this where the licence requires it. (Formerly named fetch_image; that name is still accepted as an alias.)

Discover canonical identity from an ordinary-language query — the entry point when no canonical id is held. Ranked: identity fields and sealed claims decide candidacy, Section text only when no identity field matches; ordered by identity score, then Section score, then how many words matched, then manifest position. Node candidates carry id, status, Hook, and disclosure class; Decision and term candidates carry exactly a read address and Hook. A term's Hook is its full sealed definition. Results are paged. A miss is a successful empty payload.

Retrieves search suggestions from YouTube based on a query. Group: YouTube-Search. Billing per call: 1 Credits.

When recurring events fall, and whether they land on each other. query 'next' gives the first occurrence after the date in 'after' (or on it when inclusive is true). query 'occurrences' gives every occurrence between start and end inclusive, capped at limit and at most 1000; 'truncated' says whether the list was cut short, so never read a truncated list as complete. query 'collisions' gives the days between start and end where two or more of the given events fall together, each naming what shares it — the question a calendar of festivals is really asking, and one to ask here rather than reason out in prose. An event is either a name the calendar defines or an event given inline: {'name':..,'type':'fixed','month':..,'day':..}, {'name':..,'type':'nth_weekday','month':..,'weekday':..,'n':..} or {'name':..,'type':'interval','anchor':{..},'every':N,'unit':'days'|'years'}. Pass one as 'event', or many as 'events' — which 'collisions' requires and which answers the other two queries for every event in a single call. A year in which an event's date does not exist produces no occurrence: a leap-day feast happens in leap years and not otherwise, and is never moved to a nearby day.

Free status lookup for a job accepted by convert_video / probe_media / extract_frames. Requires the job's ownership token - the `t` query parameter of the statusUrl returned at acceptance (store the whole statusUrl; it cannot be regenerated). Status goes [awaiting_upload ->] queued -> finished | error; awaiting_upload jobs report uploadExpiresAt but NEVER the uploadUrl (that credential is handed out exactly once, in the acceptance result); finished jobs carry resultUrl/thumbnailUrl/durationMs (served by this gateway, free to re-download within 24 hours of completion; GET them following redirects); a failed charged job carries retryVoucher. A bad token and an unknown jobId both answer not_found, deliberately.

Search the SoundCreation catalog by free text (brand, model, category, or keywords), by filters, or both. With a query you get relevance-ranked results; with filters only you get a sorted browse ("cheapest in-stock electric guitar under 2000"). All prices include VAT and are locale-correct.

Search real product UI screens for design reference. Use this BEFORE designing any page/component so your output matches how the best-designed products actually solve the problem. Returns structured metadata (description, UX patterns, UI elements, colors, palette) plus an image_url. Section/component/recipe hits also carry `measured` and `retina` booleans — prefer measured:true, retina:true references (pixel-measured, high-res). `query` alone works well — the filters below are optional. A value outside the lists is treated as a HINT (it ranks, it does not exclude), and if the filters together match nothing they relax rather than hand you an empty list. So a near-miss costs you nothing; spelling one exactly is simply more precise. Args: query: free text, e.g. "fintech onboarding", "dark dashboard", "Linear". page_type: one of Billing · Camera / Capture · Changelog · Chat / Assistant · Checkout · Dashboard · Detail · Docs · Editor · Empty State · Feed · Integrations · Landing Page · Log In · Map · Onboarding · Paywall · Player · Pricing · Profile & Account · Search & Results · Settings · Sign Up · Stories. ux_pattern: e.g. "Dark Mode", "Filter & Sorting", "Stats / KPIs", "Data Table", "Command Palette", "Multi-step Form", "Sidebar Navigation", "Bento Grid", "Progressive Onboarding", "Empty State", "Kanban", "Master-Detail", "WYSIWYG". industry: one of AI · Analytics · Communication · Consumer · Creator · Data · Design · Dev Tools · E-commerce · Entertainment · Fintech · Health & Fitness · Productivity · Real Estate · Security · Travel & Local. platform: "Web", "iOS" or "Android" (mobile = official store-listing screens). limit: max results (1-40, default 12). kind: "page" (default, whole screens), "section" (page parts), "recipe" (live-decoded composed patterns: Command Palette, Navbar, Login, Data Table, Hero Effect...) or "component" (measured single components). section_type: narrows by type, e.g. kind="section" + "Pricing / Plans" / "Testimonial / Social Proof" / "Hero", or kind="recipe" + "Login" / "Navbar" / "Data Table" / "Hero Effect".

A URL encoder and decoder for links and query strings, percent-encoded properly. A HelpySelf tool (helpyself.com).

Search indexed sponsorships by brand name, category and creator size. Args: query: Substring of the brand name (case-insensitive). category: Category enum value, e.g. 'vpn_privacy' (see list_categories). min_subscribers: Only creators with at least this many subscribers. limit: Maximum results (1-200).

List available AI models, filtered by category or search query. Categories: - "image" — models that generate and/or edit images - "video" — models that generate video from text and/or images - "speech" — text-to-speech models - "audio" — sound-effect, ambience, and music models Each model shows its capabilities in brackets: [generate], [edit], [image-to-video]. The returned model ID is what you pass as the "model" parameter to generate_image, generate_video, generate_speech, or generate_audio.

Search your media library. Assets are images, videos, audio, and 3D models that were generated, uploaded, or imported — each has a permanent URL, optional name, tags, and description. Filter by type, text query (matches name/description/prompt), tags, name, or source. Results ordered newest-first. Examples: search_assets({}) → recent assets. search_assets({ type: "image", query: "sunset" }) → matching images. search_assets({ tags: ["brand"] }) → tagged assets.

Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles. NOTE: search currently covers the 10 agentic principles only; for the 8 experience-design laws or the 8 spec-quality laws use principles.list(lens='surface') / principles.list(lens='spec') until search spans all three lenses.

Search curated examples by free-text query, ranked by relevance, with optional filters: principle_ids (only examples covering those principles), difficulty (beginner/intermediate/advanced), library (e.g. 'langgraph', 'openai'). Returns each match's slug, title, summary, principle coverage, difficulty, library, and source-code link — slug is the handle examples.get hydrates. Default limit 5, capped server-side. Use this when the user describes a use case, technique, or library and wants matching examples; prefer examples.get when you already have the slug; prefer guides.search when the user wants a full walkthrough; prefer principles.search when the user wants doctrine guidance, not an implementation. Results may include first-party agentic patterns (entry_kind='pattern') carrying an explicit doctrine binding, see examples.get. Filter to one family with pattern_family, which implies patterns only. Patterns take a small relevance preference over generic examples when otherwise equally relevant; that preference never outranks a genuine failing-principle match, and a pattern whose only relation to a failing principle is 'depends' receives no such match at all.

Search application guides by free-text query, matched against section answers and action items. Use this when the user describes an engineering challenge (security review, evaluation harness, observability) and wants matching guides. Prefer guides.get when you already have the guide slug; prefer guides.list when you need the full inventory.

List R3PLICA products with optional structured filters (brand, category, collection, designer, year). Use this for browsing/filtering by attribute (e.g. 'all Cassina sofas', 'chairs from 2020'); use product_search instead when the user gives a free-text query or keyword. Triggers: 'show me <category>', 'list <brand> products', 'browse the catalog', 'mostrami <categoria>', 'fammi vedere i prodotti di <brand>', 'sfoglia il catalogo'. Default limit is 50 products per call — raise via the `limit` argument if needed. Returns a sequence of structured markdown product cards: each has a `###` heading with name and brand, an inline `` markdown image, a 2-column markdown table with the spec rows (Designer, Year, Collection, Dimensions, Materials, Category), a blockquote description, and a catalog deep-link. CRITICAL when replying to the user: emit the cards VERBATIM as returned. Keep every `###` heading, the full markdown image line, the markdown table (`| ... |`), the blockquote, and the `[View on R3PLICA catalog →](url)` link untouched. Do NOT flatten the table into a comma-separated paragraph. Do NOT rewrite specs as prose. Do NOT construct alternate URLs from the asset_name — the CDN prefix is `SM_` (Static Mesh) for all products regardless of brand; pattern-matching brand-derived prefixes produces broken links.

Search R3PLICA products by free-text query. Returns structured markdown product cards (heading + thumbnail image + spec table + description + catalog link). Use this when the user gives a keyword or descriptive phrase (e.g. 'find a curved velvet sofa', 'lounge chairs'); use product_list when filtering purely by structured attributes. Triggers: 'find <x>', 'search for <x>', 'do you have <x>', 'I'm looking for <x>', 'cerca <x>', 'trova <x>', 'avete <x>', 'sto cercando <x>'. Default limit is 50. CRITICAL: preserve the markdown structure verbatim — keep the `` image lines and the tables, do not flatten into prose. NEVER reconstruct image URLs from asset_name yourself — the CDN file prefix is constant (`SM_`), not brand-derived.