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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.

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.

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.

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

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.

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。