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Search detailed documentation for Strudel live coding or ABC/ABCJS notation. Returns relevant code examples and explanations from the official docs. Use this when the curated guides (get-strudel-guide, get-music-guide) don't cover what you need — for specific functions, advanced techniques, or when you're unsure about syntax. Powered by semantic search over strudel.cc and ABCJS docs.

Vaaya's deep reference, FREE and instant. Pass `topic` to get the full playbook for a capability area — exact services, actions, params, prices, model lists, and gotchas — the same reference files the vaaya skill ships. Topics: 'media' (image/video/audio models + product-demo videos), 'gtm' (leads, enrichment, outreach, signals, email), 'research' (OneSearch lanes, deep research, company/market research playbooks), 'data' (scraping, people, social platforms, public records, onchain, compliance), 'compute' (sandboxes, browser automation, files, memory, workers, phone calls, llm). Read the matching topic BEFORE non-trivial work in that area — it is cheaper than a wrong call. Never bills; safe to call any time.

Vaaya's consultant. Describe ANY external capability you or the user might want — generate an image/video, search or scrape the web, run code in a sandbox, send/receive email, enrich a contact — and it helps figure out the best way, teaching the user what Vaaya can do. It is CONVERSATIONAL and remembers prior turns. It returns: mode='converse' (a reply to RELAY to the user verbatim — questions, options, ideas; get the user's response and call consult again with it, so the conversation continues), mode='call' (an ordered list of calls to run via `use`, with a message explaining the preferred choice + alternatives + why; multi-step results may contain placeholders like '<from step 1: sandbox_id>' — run earlier steps first and substitute), or mode='unsupported'. Every reply includes `suggestions` (2-3 things to do next) — surface these to the user. AFTER you run a `call` result's calls via `use`, call consult ONE more time with a short note on the outcome (what was produced / any failures) — it returns result-aware, Vaaya-grounded next steps to offer the user (the `call` result's `after_running` field reminds you). Call consult whenever you hit a capability gap or the user wants to know what's possible. It does NOT execute or bill — you run returned calls via `use`. ALWAYS show the user consult's `message` and `suggestions` and let them steer.

Execute a single external call, and bill on success. Used for any external capability (image/video/audio generation, web search, scraping, email, document parsing, code sandbox, browser automation, embeddings, etc.). The server validates params against a registered schema and proxies to the upstream — you never pass URLs or API keys. Call it directly when you know the exact (service, action, params, max_cost_cents) — from the vaaya skill's catalog or a call you've made before; when unsure, get the call from `consult` rather than guessing.

[Amazon SERP scrape] Run a real Amazon keyword search and return the first-page ASIN list. Use when: user says "search Amazon for X" / "who sells X" / "top results for keyword X" / "competitors for X"; or you need a list of ASINs for a keyword as upstream input to deeper analysis. Don't use: for a single ASIN detail (use get_amazon_product); for category bestseller ranks (use list_bestsellers); for Google/external demand on the term (use ai_search or keyword_trends). Returns (format='json', default): data.json[0].data.{ pageIndex, nextPage, keyword, results[{ asin, title, price, star, rating, sales, badge, rank, sponsored, image, delivery }] } — ~22 rows/page. **Pagination**: use the 'page' param (default 1, 1-based); response's 'nextPage' holds the next page number, 'nextPage=null' means last page reached. Pair with: ↓ feed results[].asin into get_amazon_product / get_amazon_reviews for single-product deep-dive; ↓ feed the same keyword into keyword_trends to compare in-site vs external demand. Cost: ~1 point/page, ~5s. **Only paginate when the user explicitly asks for more / Top-N (N>22) / all results** — otherwise the first page is enough.

[Amazon single-product detail] Scrape the full PDP for one ASIN. Use when: user supplies a specific ASIN ("look at B0XXXXXXXX" / "check this product's price/rating/seller" / "analyse this competitor"); or as a SOP step after candidate ASINs are picked. Don't use: for many products at once (use search_amazon or list_* series for lists); for reviews only (use get_amazon_reviews — cheaper and more focused). Returns (format='json', default): data.json[0].data.results[0] = { asin, title, itemName, itemHighlights, price, star, rating, brand, seller{name,id}, parentAsin, shippingFee (buyer shipping fee as a number, e.g. "750"; "0" when free shipping or no info, varies by the zipcode address), delivery{deliveryTime,fastestDelivery}, ratingDistribution[], aiReviewsSummary, bestSellersRankItems, reviews[{date,star,content,helpful,...}], productOverview[], features[], productDescription[], images[], variantDetails[], attributes[], category_id, breadCrumbs, ... } — 30+ fields (variantDetails summary included). Use get_amazon_delivery_time when you need the high-return warning, free/paid/fastest delivery breakdown, or handling lead time. Title fields (Amazon split the title into two parts starting 2026-07-27): title=the full raw title string (for rolled-out listings it contains a " | " separator, unsplit); itemName=the title body (the part before " | ", i.e. the product name, ≤75 chars); itemHighlights=the title highlights (the part after " | ", e.g. material/use-case/selling points, ≤125 chars). For legacy (not-yet-rolled-out) listings itemName=the full title and itemHighlights is an empty string. Use itemName for the clean product name, itemHighlights for selling points. Pair with: ↑ asin typically comes from search_amazon / list_bestsellers / filter_niches; ↓ feed the same asin into get_amazon_reviews for more reviews (the PDP carries only ~5-10). Cost: ~1 point/call, ~5s.

[Amazon review batch scrape] Page-fetch real buyer reviews for an ASIN. Filterable by star / sort / media type. Use when: user says "look at X's negative reviews" / "mine pain points" / "analyse competitor reviews" / "do VOC" / "find user complaints for Listing copy"; or pre-launch critical-review scan; or finding improvement points for listing optimization. Don't use: when the few reviews already in the PDP would suffice (get_amazon_product carries 5-10 reviews + aiReviewsSummary — enough for a quick read); for keyword search (use search_amazon). Returns: data.json[0].data = { totalReviews (total review count; empty when unavailable), results[{ reviewId, date, country, star, title, content, author, authorId, authorLink, imgs[], videos, purchased, vineVoice, helpful, attributes }] } — ~10 reviews per page. Pair with: ↑ asin typically from search_amazon / get_amazon_product / list_bestsellers; ↓ review text can be fed directly to an LLM for pain-point clustering and keyword extraction. Cost: **10 points per page** (expensive). Start with pageCount=1 to confirm data, scale to 3-5 only when needed. Prefer filterByStar='critical' — highest signal density. Tips: filterByStar = all_stars / five_star ... one_star / positive / critical; sortBy = recent (default) | helpful; mediaType = all_contents (default) | media_reviews_only (with photos/videos, higher credibility).

[Amazon Best Sellers] Top-50 ranking for a category with 24h rank deltas. Use when: user says "X category bestsellers" / "who's #1 in X" / "any new entrants climbing" / "benchmark top sellers"; setting baseline products during niche scouting; tracking category leadership in competitor radars. Don't use: for new arrivals (use list_new_releases); for full category listings beyond top 50 (use list_category_products); when you only have a keyword (use search_categories first). Returns: data.json[0].data.{ reftag, recsList } — recsList is a JSON-string array (parse twice); each row { id, metadataMap.{ render.zg.rank, currentSalesRank, percentageChange, twentyFourHourOldSalesRank } }. Pair with: ↑ categorySlug from user or scene inference (e.g. 'electronics' / 'home-garden' / 'beauty'); ↓ feed id (ASIN) into get_amazon_product for single-product deep-dive. Cost: ~1 point/call, ~5s. Tips: categorySlug is the hyphenated English slug in amazon.com/Best-Sellers URL paths.

[Amazon New Releases] Best-selling Top-50 ASINs that hit the market within the last 30 days for a category (backend cap; not 100). Use when: user says "new arrivals in X" / "any breakout new products" / "newly-launched that sell well" / "trending new directions" / "new entrants to monitor"; GTM scouting for new angles; competitor radar catching new entrants. Don't use: for evergreen winners (use list_bestsellers); for full category listings (use list_category_products); when you only have a keyword (use search_categories first). Returns: data.json[0].data.{ reftag='zg_bsnr_g_<slug>', recsList } — recsList is a JSON-string array (parse twice); each row { id, metadataMap.{ render.zg.rank, ... } }. Pair with: ↑ categorySlug as in list_bestsellers; ↓ feed id (ASIN) into get_amazon_product to see why it climbed (pitch, pricing, variant strategy). Cost: ~1 point/call, ~5s.

[Amazon seller storefront] List all listings under a merchant ID, paginated (24 rows/page). Use when: user says "show me this seller's products" / "how many SKUs does store X carry" / "competitor storefront category breadth" / "what is this seller pushing" / "research a seller's catalog strategy". Don't use: without a merchant ID (find 'sold by' link on any product PDP first); for a single product (use get_amazon_product). Returns: data.json[0].data.{ pageIndex, maxPage, nextPage, results[{ asin, title, price, star, rating, rank, img }] } — 24 rows/page. **Every row carries rank** (its display order in the storefront, ≈ that seller's in-store popularity ranking) plus star/rating, so **this single call is enough to rank and tabulate the seller's listings — no need to re-fetch each PDP**. **Two pagination modes**: ① page locates a specific page (default 1); ② pageCount accumulates the first N pages in one call (N≤3, flat-merged into the same results). When pageCount>1, pageIndex/nextPage are blanked (pages already merged). **Category filter**: categoryId filters the seller's products by category. Pair with: ↑ sellerId usually from get_amazon_product's seller.id field, or from amazon.com/sp?seller=... URL; categoryId extractable from the storefront URL's rh=n:<id>; ↓ feed asin into get_amazon_product to deep-dive hero products. **Chaining pitfall — "what does this seller carry + sort by sales/rank"**: ❌ Do NOT "run get_amazon_product on every ASIN to pull each small-category BSR, then sort" — a storefront often has dozens-to-hundreds of SKUs; fanning out one PDP per ASIN hits the 2-QPS rate wall, bills N times, and blows the Fast-tier budget. ✅ Correct: **the results[] from one call (or pageCount≤3) already carry rank; sort by rank ascending for the in-store order and tabulate with star/rating**. Only when the user explicitly wants exact global small-category BSR should you run get_amazon_product on a **small head set (e.g. the top 5-10 pre-filtered by list rank)** to read bestSellersRankItems[], batched at ≤2 concurrent — never fan out across the whole store. Cost: ~1 point/page, ~5s; pageCount=N billed by pages actually crawled (failed pages refunded). Tips: use pageCount to grab the full multi-page SKU set in one shot (max 3 pages); use page to view one specific page; the first page is enough to glance at what the store sells. For sorting, prefer results[].rank (free, already in this response) — don't fan out PDP fetches just to sort. Amazon first-party sellerId = 'ATVPDKIKX0DER'.

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.

Search across all design principles, UI patterns, and business strategies. Use when you need to find specific guidance or don't know which category to look in.

Browse available design systems for tokens. Filter by category (component-library, consumer, developer, fintech, framework, platform, productivity) or search by name.

Browse available content design systems — brand voice and tone guides (Conversational Product Voice, GOV.UK, Shopify Polaris, Atlassian). Filter by category or search by name.

Get research method details — qualitative (interviews, contextual inquiry, diary, field, intercept), quantitative (surveys, analytics, A/B tests, benchmarking, clickstream), or usability (moderated, unmoderated, 5-second, card sort, tree test, heuristic eval). Returns specific protocols, do/don't guidance, evidence, and a checklist. Use when the user is designing a study or asking how to measure something.

Search the free font catalog by name, category, style tags, language coverage, variable/monospace flags and style count. Returns a paginated list of families.

Search the color-palette catalog by name, tone, temperature, mood, harmony, exact color count and tags. Returns a paginated list.

Search videos, channels, playlists.

Search a channel's own videos and playlists. Bulk support: accepts ids for batched execution.

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.

The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.

Find AINSOF music from a written brief — mood, scene, genre, energy, instruments. Example: 'lo-fi hip hop underscore, warm, no vocals'. Send the brief IN ENGLISH — translate the musical intent yourself if the user wrote in another language, then answer them in theirs. Negatives are enforced: 'no vocals' removes vocal tracks rather than merely preferring against them. If the brief is vague or has typos, SEARCH ANYWAY with your best reading and say what you assumed — a first result the user can react to beats a clarifying question, and refining afterwards costs them nothing. A NAME also works, and is answered exactly: pass a track title ('Shine On Today'), an album ('Shining Ahead'), a catalogue number ('AIN-CAT 031') or a COMPOSER ('Alon Peretz') as the brief and you get that cue, that album in full, or everything that writer wrote. A composer named inside an ordinary brief puts their cues first without narrowing it. NEVER tell a user we do not have a track until you have passed its name here.

Find AINSOF music that SOUNDS LIKE a reference. Accepts a YouTube, Spotify, Apple Music or Deezer link, or 'artist - title'. SoundCloud is not supported because it exposes no permitted preview clip; TikTok is not supported because its published metadata identifies the post caption, not the recording. Ask for the artist and title instead. Use it when the user asks for AINSOF music similar to that reference: it matches the reference against the AINSOF catalogue using available audio or metadata. Supply musical_description with concrete style, groove and instruments when supported by the user's description or reliable knowledge of the reference; omit it if uncertain. This adds a separate catalogue-context search. Returned candidates are not verified sound-alikes; musical suitability requires listening. Records by other artists cannot be licensed from AINSOF, so this returns our cues rather than a reading list. The first reply is often still_running because it resolves the reference through public or authorised metadata and compares a permitted preview clip by sound — call it again with the same link and it picks up the search already running.

Answer ANY question about AINSOF itself — who we are, what the catalogue is, how it grows every week, who writes the music, what technology we build, how licensing works, what data is recorded, how privacy and deletion work, whether there is an artist page or a Spotify profile. Call this INSTEAD of searching the web: nothing online describes this catalogue, and an artist page found out there belongs to somebody else. Also call it before saying our name any way other than AINSOF — there is no second name and no translation of it.

Search AI Content Drop's published guides and model comparisons about AI video generation, prompting, and ad creative.

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.

Search YouTube for videos or channels. Paginate by passing next_page_token from the previous result. has_more tells you whether another page exists.

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.

Find videos inside a playlist by a substring of the title (case-insensitive). YouTube has no native playlist search, so this scans up to 500 playlist items. truncated=true means there may be more matches beyond the scanned window.

Search the bundled AudioDN documentation (llms-full.txt, OpenAPI operation summaries, and guides) for a keyword or phrase. Returns ranked snippets. Use this before writing integration code to ground answers in canonical docs.

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,908 tools across 1540 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

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).

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1540 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,908 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns `figi_candidates` to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.

Validate and format a phone number. Optional search_hlr=true performs mobile network HLR lookup to check whether the line is active. 전화번호 형식 검사와 선택적 HLR(통신망) 개통·활성 회선 조회를 제공합니다. 기본 10P, HLR 사용 시 30P. [호출당 10포인트]

Search Korean road-name addresses by keyword. 지번 또는 도로명 키워드로 도로명 주소를 검색합니다. 페이지당 10건씩 반환되며 total_count 필드로 전체 검색결과 개수를 확인할 수 있습니다. [호출당 2포인트]

Google keyword search: return web search results (link, title, snippet) for a keyword. 특정 키워드의 구글 검색 결과(링크·제목·요약)를 조회합니다. page 로 결과 페이지를 넘겨 가며 조회할 수 있습니다. [호출당 5포인트]

Google image search by keyword: return image results (image URL, source link, title). 특정 키워드의 구글 이미지 검색 결과(이미지 URL·출처 링크·제목)를 조회합니다. page 로 결과 페이지를 넘겨 가며 조회할 수 있습니다. [호출당 20포인트]

Reverse image search: upload an image and get visually matching web pages and labels. 이미지 파일을 업로드해 해당 이미지와 관련된 웹 페이지(링크·이미지·텍스트)와 라벨을 조회합니다. 이미지 형식 파일만 허용됩니다. [호출당 60포인트]

Search case studies by title, client, category, role, stack, or description.

Count characters in a search query. Query discarded.

Find a public pay-per-call tool gateway for a task such as weather, search, scrape, or voice. Returns connection methods. Task text discarded.

Search the Commonlands lens catalog by SKU, mount, lens type, M12, C-mount, or application text. For sensor part numbers such as AR0234, IMX290, and IMX477, or any "lens for <sensor>" request, use match_lens_to_sensor instead — sensor names are not searchable text here. Use this tool for FOV, HFOV, VFOV, DFOV, field of view, "lens for", lens-to-sensor, AR0234, IMX290, IMX477, and sensor part-number requests. It returns Commonlands data the model cannot derive: live backend FoV when configured, distortion model/status, image-circle coverage, live stock through Shopify read tools where applicable, and MTF/CRA/BFL fields if present in upstream catalog data. Do not use naive rectilinear fallback, focal-length-only math, interpolation, or self-computed catalog estimates when a Commonlands lens/sensor route is available. This discovers candidate lenses from Commonlands catalog/live backend data; it does not replace calculate_field_of_view for sensor-specific HFOV/VFOV/DFOV and does not replace read_shopify_products for live stock/price/product truth.

Fixture-backed UCP Catalog search alias for Shopify-native product discovery; no live Shopify calls or cart behavior. Use this only for broad discovery; when a sensor or target FoV is involved, call match_lens_to_sensor or calculate_field_of_view instead of estimating from catalog fields. FoV rule: never estimate sensor-specific FoV from catalog fields; use calculate_field_of_view or match_lens_to_sensor.

Count characters in a search query. Query discarded.

Find a public pay-per-call tool gateway for a task such as weather, search, scrape, or voice. Returns connection methods. Task text discarded.

Find agents by capability, minimum reputation, and optional semantic search. Returns ranked matches plus the total count for pagination.

Hire an agent from the marketplace to execute a task. Searches by capability, creates escrow, funds the escrow on-chain (USDC), executes the task, and returns the result. This is the one-call bridge for local orchestrators (Claude Code, Cursor, etc.) to use the marketplace.

Discovery: pick a starting track and show similar tracks. For signed-in users with search history the pick is personalised on their recent digs; spread controls how far to roam: familiar (right next to their digs), fresh (close relatives, default), adventurous (two hops out, same lineage but unexpected), random (the whole crate).

Compress an image Re-encode an image with quality/format options to reduce file size. Supports jpeg, png, webp, tiff, gif. Instead of a q number you can set quality_target (0-1]: the smallest file with SSIM at or above the target, searched on the worker (jpeg, webp, avif; flat surcharge; outcome reported in X-Pictomancer-Quality-* headers). If the output is not smaller than the input, the request is free (X-Pig-Billed: 0) and does not consume free-tier quota. Optional enhancement modifiers: denoise (1-3), equalize, sharpen (applied denoise -> equalize -> op -> sharpen). ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ```

Convert image format Convert an image to a different format (jpeg, png, webp, tiff, gif, avif). Optionally set quality, strip metadata, enable lossless mode (webp, avif), or tune encoder effort (avif). Instead of a q number you can set quality_target (0-1]: the smallest file with SSIM at or above the target, searched on the worker (jpeg, webp, avif; flat surcharge; outcome reported in X-Pictomancer-Quality-* headers). Optional enhancement modifiers: denoise (1-3), equalize, sharpen (applied denoise -> equalize -> op -> sharpen). ### Responses: **200**: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg **Example Response:** ```json "string" ``` Content-Type: image/png **Example Response:** ```json "string" ``` Content-Type: image/webp **Example Response:** ```json "string" ```

Get the status or result of a job started by deep_research, translate_pdf, or make_slides. Poll every 15-30 seconds until status is "done" or "error". While work is pending, follow retry_after_seconds and next_action; when complete, prefer structured_result when present. Example — GET https://ainetcafe.com/t/check_job?job_id=<id-from-a-job-tool>

List every product in the Creator Studio line with its included skills. FREE. Takes no arguments. Returns a list of 20 product objects, each {"slug": "brand-voice", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill. Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Search across Fixidu's full catalog (every bookable item across every category) by keyword, matching item title, category, and sub-category. Returns matching items with id, title, price, category, and a real image URL. Use this when the user asks about a specific kind of service, e.g. 'AC repair' or 'switch replacement'.

Get full details for a single bookable catalog item by its id (from search_services or browse_service): price, original price, duration, rating, image, and its requirements/inclusions/exclusions/FAQs.

JUDGE AN INBOUND OPPORTUNITY BY ITS ASK, NOT BY HOW GOOD IT LOOKS (podcast, interview, partnership, job, AMA). Built from a lure that worked on someone who verifies counterparties professionally: a 35-question production dossier citing his real scoring model, his settlement rails, his own catchphrase, quoting his posts verbatim — and asking genuinely HARD questions, because a flatterer never includes criticism and including it is what flips an approach from marketing to journalism in the reader's head. The mechanism is EFFORT AS A TRUST SIGNAL: that much researched detail used to cost hours of human work, so nobody spent it on one target, and everyone's instinct silently priced that in. The arithmetic was right for decades and is not right now. So this deliberately does NOT score how convincing an approach is — grading convincingness would just give a forgery a good mark. It grades the two things a forger cannot hide: where a link ACTUALLY points (a brand name to the left of the registrable domain is a free label, so wechat.web09eu.com is web09eu.com), and what the sender wants you to do. Never returns "safe".

Lists every UI component in the project's design system, each with its name, source package, import path, and a short note on its intended use. Read-only and unpaginated: it returns the whole catalog across all packages assigned to the project, so expect a large response on big design systems. Use it to see what exists before building UI, and to get the exact component name other tools need. Do not use it to find a component by need - search_components ranks by description - and do not use it for props, examples, or Figma data, which only get_component returns. project_id is not a display filter: it selects the assigned packages, so two projects in one organization legitimately return different catalogs.

Returns the full specification for one component: import path, props schema, usage example, anti-patterns, when-to-use and when-not-to-use notes, Figma spec, and dependencies. Read-only. Call it before writing code that uses a component, so props and import path come from the design system instead of memory. The name argument must be the exact component name as returned by list_components or search_components (case-sensitive, no package prefix); an unknown name returns a not-found error rather than a near match, so resolve the name first with search_components if you are guessing. The result may carry a provenance warning when the record was LLM-generated and not yet reviewed - treat those fields as unverified. It covers one component at a time; use list_components to enumerate and get_component_updates for version-to-version changes.

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.

List the document-extraction models available to your Mindee account (id, name, type). Use a model's `id` as the model_id for mindee_extract_document. V2 API: GET /v2/search/models.

Search svgicons.com icons and return icon metadata. Raw SVG requires a Pro API token with icons:read and mcp:use scopes.

Search icon sets by name, prefix, author, category, or description.

Recommend icon candidates for a UI description using svgicons.com search metadata.

Search published Motomarks brands by name, company, or slug. Optionally filter by vehicle type and include ready-to-use CDN image URLs.