Tool search 103,805 tools · 5,318 live servers

Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.

Search news articles. Returns a list of matching articles. Each article includes: - article_id, classification_id, title, source, date, link, category, rank, total_shares, summary - bias_values: dict of per-dimension bias scores using plain-text keys (e.g. 'liberal conservative bias'), same schema as get_bias_from_url and get_all_source_biases (when available) - bias_analysis_status: 'evidence_ready', 'evidence_unverified', 'evidence_partial', 'scored_legacy', or 'pending' - bias_dimensions when include_evidence=true: a self-contained object joining each score, scale, evidence status, claim, evidence, counterevidence, confidence, and rationale. Quotes include verification method and exact character offsets when raw-text matching succeeds. Dimension evidence_status is one of: verified, provided_unchecked, quote_mismatch, metadata_incomplete, metadata_only, or missing. - bias_analysis: contract/schema/model/prompt provenance, generation and review status, input scope/hash/size, limitations, quote-verification method, and explicit evidence coverage - context: AI-generated contextual background for the article (when available) - extracted_data: structured quantitative/qualitative facts extracted from the article - raw_data: legacy serialized form of extracted_data Args: query: Search keywords (required). limit: Max results (1-100, default 20). source: Filter by source name, e.g. 'CNN', 'Reuters'. category: Filter by category. One of: 'trending', 'tech', 'markets', 'politics', 'business', 'science', 'memes'. days_back: Only include articles from the last N days. 0 means no date filter. Default: 720 (2 years). min_shares: Minimum total social shares. sort: Sort order. One of: 'rank' (relevance, default), 'date' (newest), 'shares' (most shared). include_evidence: Include claim-level evidence, counterevidence, confidence, rationale, and limitations. Defaults to false to keep search payloads compact. only_analyzed: Return only articles with valid canonical bias scores.

Get comprehensive bias analysis for a news source. Returns: - source_name, slug_name, page_url - source_match: original query and deterministic match method - articles_analyzed: total articles in the bias database for this source - last_updated: source-profile aggregation timestamp - avg_social_shares: average social shares per article - emotionality_score (0-10): how emotional the writing is - prescriptiveness_score (0-10): how much the source tells readers what to think/do - bias_values: canonical plain-text source-level weighted display scores (-50 to +50 bipolar, 0 to +50 unipolar). Keys match the article tools; these are directional source summaries, not raw article-score averages. - bias_scores: legacy emoji-prefixed display scores - bias_score_methodology: scope and evidence caveats for aggregate scores - bias_description: clean-text, AI-generated overall bias summary narrative - bias_description_metadata: generation time, automated review status, and evidence scope - bias_description_html: optional website HTML when include_html=true - liberal_conservative_description: narrative on political leaning - libertarian_authoritarian_description: narrative on authority stance - signature_phrases: words/phrases uniquely overrepresented vs other sources - signature_negative_phrases: uniquely negative/alarming phrases - most_shared_phrases: phrases in their most viral articles - most_emotional_phrases: phrases used in their most emotional articles - pays_for_traffic_keywords: keywords this source buys ads for - similar_sources: sources with the most similar bias profile - most_different_sources: sources with the most different bias profile - trends_graph_url: URL to a chart of this source's coverage volume over time - bias_plot_urls: dict of 2D bias scatter plot image URLs (political_lib_auth, subjective_objective, informative_opinion, oversimplification_factful) — only present when available - recent_articles: list of most recent articles with full article fields, bias_values, analysis status, and optional self-contained bias_dimensions and bias_analysis. Evidence quotes include verification method and exact character offsets when available. - recent_evidence_coverage: reconciled counts for verified, unverified, partial, legacy-scored, and pending articles, plus evidence-bearing count and verified ratio Throws an error if the source is not found. Args: source: Source name, slug, or domain (e.g. 'Fox', 'reuters', 'bbc.co.uk'). Partial names are accepted only when they identify one source; ambiguous input returns candidates. recent_articles: Number of recent articles to include (1-50, default 10). include_evidence: Include per-article claims, verbatim evidence, counterevidence, confidence, rationale, and limitations. Defaults to false to keep multi-article source payloads compact. include_html: Also return the original website-formatted source narrative. Defaults to false.

Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources. Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories: each one draws from multiple sources and includes an AI-written summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers. Returns a list of stories, each with: - title, simple_title, date, category - page_url: full URL to the story on heliumtrades.com - image: story image URL (when available) - summary: Helium's synthesized overview - takeaway: key conclusion - context: background context - evidence: numbered evidence items - potential_outcomes: forward-looking outcomes with probabilities - relevant_tickers: related stock tickers - num_sources: number of source articles synthesized - rank: search relevance score Args: query: Search keywords (required). limit: Max results (1-50, default 10). category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'. days_back: Only include stories from the last N days. 0 means no date filter.

Search Helium's meme database by text (OCR + caption). Returns matching memes ranked by relevance. Each result includes: - id, caption, ocr (text extracted from the image) - image: full URL to the meme image - source: origin platform (e.g. 'reddit') - num_likes: likes/upvotes on the original post - date, is_video, rank Args: query: Search keywords (required). Matched against OCR text and captions. limit: Max results (1-100, default 20). days_back: Only include memes from the last N days. 0 means no date filter (default).

Free discovery. Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack. Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.

Free discovery. Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack. Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.

Generic structured query for direct source_id or pack_id access using the same contract as POST /api/v1/query/dataset. Free packs: boundaries, currency, distributed_manufacturing, floods, geography, nri, owid, reverse-geocoding, un_sdg, un_wpp, volcanoes, world_bank_wdi. Paid packs: earthquakes, hurricanes, tornadoes, tsunamis, wildfires, world_factbook, worldpop (x402 Base USDC).

Free discovery. Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack. Call this before querying a new pack so you can see time shape, coverage limits, and the paste-ready first query.

Recherche des entreprises et des établissements français dans la base SIRENE (INSEE), par nom, SIREN, SIRET, ville, code postal, code NAF, ou par proximité géographique. IMPORTANT : un résultat est TOUJOURS un ÉTABLISSEMENT (un SIRET), jamais une entreprise — même groupé par entreprise, où le SIREN est représenté par son meilleur établissement (le siège de préférence). Ne dis donc pas « 3 entreprises trouvées » pour 3 établissements d'un même SIREN. Par défaut, un résultat par entreprise si `query` est fourni, un résultat par établissement en recherche par proximité ou par `siren` ; force ce comportement avec `group_by_company`. Lis `classement_pertinence` avant de présenter un « meilleur match » : à false (listing simple ou tri par distance), le premier résultat n'est PAS le plus probable. Un résultat `diffusible: false` a ses nom et adresse masqués par l'INSEE — c'est la loi, pas une donnée manquante : ne complète jamais de mémoire. Une liste vide fait autorité : aucune entreprise ne correspond. La fiche complète s'obtient ensuite via get_company_details. Plan minimum : Discovery — la recherche par proximité (lat/lon/radius_m) nécessite le plan Growth.

Recherche ET liste des communes françaises. Deux usages : (1) retrouver une commune précise par nom, code postal ou code INSEE (renseigne `query`) ; (2) LISTER/FILTRER les communes d'un département ou d'une région, avec une fourchette de population optionnelle — `query` est alors inutile. Exemple : « communes du Pas-de-Calais de plus de 100 000 habitants » → department_code=["62"], population_min=100000. Préfère `department_code` (ex. "62") au nom ; les noms de département sont normalisés automatiquement ("Pas-de-Calais", "Val-d'Oise"… sont acceptés). Renvoie code INSEE, code postal, population et — selon le plan/details — altitude, densité, surface, département et région. Plan minimum : Discovery.

Search Brazilian FIPE vehicles by free-text query (make, model, year, fuel). Returns matching vehicles with their slugs and IDs — the entry point that resolves names into the identifiers get_price, get_price_history and compare_prices need.

Resolve a place query to candidate locations with coordinates. Accepts city names ("Denver"), city+state ("Portland, OR" via query), ZIP codes ("50219"), or partial input with fuzzy=true for autosuggest-style matching ("bost" -> Boston). Returns ranked candidates with lat/lon. Most weather tools accept a `location` string directly and geocode internally -- use this tool only to disambiguate ("which Springfield?") or to present location choices to the user. Example: {"query": "Springfield"} returns all major Springfields ranked by population.

Discover the datasets (model grids, analyses, observations) available at a location, with per-dataset freshness (data age, latest model run). Datasets vary by domain (CONUS/Alaska/Hawaii). Use this to find dataset_id values for query_dataset and describe_dataset, or to assess whether data is current before making decisions. Example: {"location": "Anchorage"}.

Variables available in a dataset, with standard names, units, descriptions, and the time range of available data. Use before query_dataset to discover valid variable names. Example: {"dataset_id": "nbm_conus"}.

Raw time series from a specific dataset for specific variables at a point. Power-user access to any gridded product (NBM, HRRR, GFS, RTMA, MRMS, air quality, ...). Time modes: hours (next N hours, default 24), time_start+time_end (explicit ISO-8601 window), or latest=true (single most-recent value). reference_time pins a specific model run, and each returned series reports the run that served it (reference_time, or reference_times when a series mixes runs) — check it before comparing two runs, since a run older than about 48 hours may no longer be available. For blended forecasts use get_forecast instead. Examples: {"location": "Denver", "dataset_id": "hrrr_surface", "variables": ["temperature_2m"], "hours": 18} or {"lat": 41.4, "lon": -92.9, "dataset_id": "rtma_conus", "variables": ["temperature_2m"], "latest": true}.

[Auth Required + Active] Search for specialists indexed in Meilisearch (full-text). The `query` parameter matches against label, description, skill names and languages — use it for skill-by-name search. Paid action (api_search). Pass API key via X-Agent-Key or Authorization: Bearer.

Search for a place, address, or POI by name and get matching locations as GeoJSON features. e.g. 'Zurich' or '1600 Pennsylvania Ave'. API: GET /geocoding/{query}.json.

Forward-geocode up to 50 queries in one request. Returns a JSON ARRAY of GeoJSON FeatureCollections, one per query, in order. API: GET /geocoding/{q1;q2;...}.json.

Search MapTiler's CRS/EPSG database by free text or key:value filters (e.g. 'code:4326', 'kind:CRS-PROJCRS'). API: GET /coordinates/search/{query}.json.

법령 조문 검색 — 키워드 또는 조문번호로 조문 스니펫 반환(상위 top_k건). 전문이 필요하면 get_law_article(ref)로 이어서 조회. Args: query: "수의계약", "시행령 제26조", "제21조" 등 top_k: 반환 건수 (기본 8, 최대 20)

전 코퍼스 통합 검색 — 법령+계약예규+조달청·행안부 세부기준+실무가이드. LLM 미사용. search_law가 법령 조문 전용인 것과 달리 예규·적격심사 세부기준·실무가이드까지 검색한다. 낙찰하한율·적격심사 배점·실무 절차 등 법령 본문 밖 질문에 사용하라. AI 생성 없이 검색 근거 원문만 반환한다(백엔드 LLM 예산 미차감). Args: query: 자연어 검색어 (예: "적격심사 낙찰하한율 50억 미만") top_k: 반환 건수 (기본 6, 최대 12)

판례·법령해석례 검색 — law.go.kr 실시간 조회(항상 현행). LLM 미사용. 분쟁·처분취소·해석 다툼("~해도 되나", "~취소될 수 있나")에 조문만으로 부족할 때 쓰라. 본문은 get_case(kind, case_id)로 이어서 조회. Args: query: 핵심 명사 위주 검색어 (예: "부정당업자 제한", "유찰 수의계약") top_k: 종류당 반환 건수 (기본 5, 최대 10) kind: "prec"(법원 판례) | "expc"(법제처 법령해석례) | "all"(둘 다, 기본)

오류·개선 제보 — 운영자에게 전달된다(웹 피드백과 같은 검토 파이프라인). 사용자가 "틀렸다"고 지적하면 **먼저 이 도구로 제보한 뒤** 정정 답을 제시하라. 도구 결과가 조문·수치·판례와 명백히 불일치할 때도 제보하라. 추측으로 부르지 말 것. 서버가 직전 도구 호출 기록을 자동 첨부하므로 도구명·인자를 기억으로 적을 필요 없다. Args: category: "wrong_citation"(오인용) | "outdated_law"(개정 미반영) | "wrong_ruling"(룰엔진 오판정) | "tool_error"(도구 오류) | "feature_request"(기능 요청) | "other" message: 무엇이 어떻게 잘못됐는지 구체적으로 (근거 조문·기대값 포함 권장) related_tool: 문제가 난 도구명 (예: "search_references") related_query: 문제를 재현하는 질의·입력 expected: 올바르다고 생각하는 값·조문 (알고 있다면)

Get the full profile of one specific treatment facility: address, phone, programs offered, insurance plans accepted, SAMHSA verification status, and a direct browse URL. Supports partial name matching — returns the best match if multiple facilities contain the query string. Use after search_facilities when the user wants to drill into a named facility.

Scrape and extract content from a URL with full JS rendering, returned as clean markdown. Alternative to Firecrawl scrape at 2.5x lower cost. Strips navigation, ads, scripts, and boilerplate — ideal for RAG pipelines and AI research agents. 1. title (string) -- page title from <title> tag 2. description (string) -- meta description 3. author (string) -- author from meta tags or schema 4. content (string) -- clean markdown body text, headings preserved 5. wordCount (number) -- total words in extracted content 6. charCount (number) -- total characters 7. url (string) -- final URL after redirects Example output: {"title":"How to Scale APIs","description":"A guide to...","content":"# How to Scale APIs\n\nScaling requires...","wordCount":1250,"charCount":7800,"url":"https://blog.example.com/scale-apis"} Use this BEFORE summarizing articles, building RAG corpora, researching topics from web sources, or extracting data from documentation pages. Essential for any workflow that needs to scrape and extract content from web pages as LLM input. Drop-in replacement for Firecrawl scrape. Do NOT use for screenshots -- use capture_screenshot instead. Do NOT use for SEO audit -- use seo_audit_page instead. Do NOT use for tech stack detection -- use website_detect_tech_stack instead. Do NOT use for web search -- use web_search_query instead.

Power-user escape hatch: GET any KnowledgeOwl API path not wrapped by a dedicated tool. READ-ONLY — only GET is allowed. Pass the FULL API path after the base, starting with a slash, INCLUDING any query string, e.g. "/article.json?_page=2" or "/category/abc123.json". Returns the parsed JSON.

Use this when you need to store text documents and search them by semantic similarity. Accepts documents to store and a query to search. Uses TF-IDF vectorization with cosine similarity to find the most relevant matches. Returns top-k results with similarity scores. Do NOT use for web search — use web_search_query instead. Do NOT use for keyword research — use keyword_research instead. Do NOT use for text classification — use text_classify instead.

Semantic web search for finding relevant pages, documents, and current information. Alternative to Exa search at 3x lower cost. Returns structured JSON results with ranked matches, titles, URLs, and text snippets. 1. results (array) -- ranked list of search results 2. results[].title (string) -- page title 3. results[].url (string) -- full URL to the page 4. results[].snippet (string) -- relevant text excerpt with query terms highlighted 5. query (string) -- the search query used 6. totalResults (number) -- number of results returned Example output: {"query":"best CRM for startups 2026","results":[{"title":"Top 10 CRMs for Startups in 2026","url":"https://blog.example.com/crm-startups","snippet":"HubSpot leads the pack for early-stage startups with its free tier..."},{"title":"CRM Comparison Guide","url":"https://review.example.com/crm","snippet":"We tested 15 CRM platforms across pricing, features..."}],"totalResults":5} Use this BEFORE answering questions about current events, finding documentation, researching competitors, or gathering data on any topic. Essential for semantic web search when the agent needs up-to-date information beyond its training data. Drop-in replacement for Exa search. Do NOT use for web page content extraction -- use web_scrape_to_markdown instead. Do NOT use for SEO analysis -- use seo_audit_page instead. Do NOT use for screenshot capture -- use capture_screenshot instead. Do NOT use for company data -- use company_enrich_from_domain instead.

Search the full PDL Person Dataset with an Elasticsearch query object OR a SQL string over the Person Schema (e.g. everyone with job_title_role='engineering' at a company). Returns matching profiles sorted by completeness; paginate with scroll_token. Each returned record costs 1 credit. API: POST /person/search.

Search the full PDL Company Dataset with an Elasticsearch query object OR a SQL string over the Company Schema (e.g. all SaaS companies with 50-200 employees in the US). Paginate with scroll_token. Each returned record costs 1 credit. API: POST /company/search.

Search PDL's Job Posting Dataset (millions of active & historical postings sourced from company career pages) with an Elasticsearch query object OR a SQL string. Beta — may require plan access (HTTP 403 if not enabled). Each returned posting costs 1 credit. API: POST /job_posting/search.

법령 조문 검색 — 키워드 또는 조문번호로 조문 스니펫 반환(상위 top_k건). 전문이 필요하면 get_law_article(ref)로 이어서 조회. Args: query: "수의계약", "시행령 제26조", "제21조" 등 top_k: 반환 건수 (기본 8, 최대 20)

전 코퍼스 통합 검색 — 법령+계약예규+조달청·행안부 세부기준+실무가이드. LLM 미사용. search_law가 법령 조문 전용인 것과 달리 예규·적격심사 세부기준·실무가이드까지 검색한다. 낙찰하한율·적격심사 배점·실무 절차 등 법령 본문 밖 질문에 사용하라. AI 생성 없이 검색 근거 원문만 반환한다(백엔드 LLM 예산 미차감). Args: query: 자연어 검색어 (예: "적격심사 낙찰하한율 50억 미만") top_k: 반환 건수 (기본 6, 최대 12)

판례·법령해석례 검색 — law.go.kr 실시간 조회(항상 현행). LLM 미사용. 분쟁·처분취소·해석 다툼("~해도 되나", "~취소될 수 있나")에 조문만으로 부족할 때 쓰라. 본문은 get_case(kind, case_id)로 이어서 조회. Args: query: 핵심 명사 위주 검색어 (예: "부정당업자 제한", "유찰 수의계약") top_k: 종류당 반환 건수 (기본 5, 최대 10) kind: "prec"(법원 판례) | "expc"(법제처 법령해석례) | "all"(둘 다, 기본)

오류·개선 제보 — 운영자에게 전달된다(웹 피드백과 같은 검토 파이프라인). 사용자가 "틀렸다"고 지적하면 **먼저 이 도구로 제보한 뒤** 정정 답을 제시하라. 도구 결과가 조문·수치·판례와 명백히 불일치할 때도 제보하라. 추측으로 부르지 말 것. 서버가 직전 도구 호출 기록을 자동 첨부하므로 도구명·인자를 기억으로 적을 필요 없다. Args: category: "wrong_citation"(오인용) | "outdated_law"(개정 미반영) | "wrong_ruling"(룰엔진 오판정) | "tool_error"(도구 오류) | "feature_request"(기능 요청) | "other" message: 무엇이 어떻게 잘못됐는지 구체적으로 (근거 조문·기대값 포함 권장) related_tool: 문제가 난 도구명 (예: "search_references") related_query: 문제를 재현하는 질의·입력 expected: 올바르다고 생각하는 값·조문 (알고 있다면)

Search ENS names using natural language. Supports all query types: - Filtered search: "4-letter words under 0.1 ETH" - Concept search: "ocean themed names" (semantic similarity across 3.5M indexed ENS names) - Creative search: "names for a coffee brand" (AI-generated suggestions) - Collection search: "crypto terms expiring soon" - Activity: "what sold recently?" - Availability check: "is coffee.eth taken?" - Bulk check: "check apple.eth, banana.eth, cherry.eth" - Collection/club floor: "999 club floor", "cheapest 10k club names" (returns real listings sorted by price) Returns structured results with name, price, owner, tags, and availability info. It searches the NAME database by pattern/length/price/club/vibe — it does NOT know who real-world people, teams, brands, athletes, musicians, or films are. For "find me NBA players / pop stars / Pixar films / presidents" use enumerate_entities instead (it returns correctly-spelled labels). Use this for "floor of <club>" / "cheapest in <collection>" (find_alpha can't — it has no collection param). For lifecycle-window lists — "which names are in premium / Dutch auction", "names in grace period", "expiring soon" — use get_expiring_names instead: its grace/premium statuses are on-chain-validated and premium rows carry live pricing.

List ENS names by lifecycle window — THE tool for "which names are in premium / on Dutch auction", "names in grace period", "what's expiring soon / about to drop". Statuses: - premium: 90-111 days past expiry, registerable NOW at a decaying premium. Each result includes premiumUsd (decay-curve estimate) and, when the on-chain read landed, premiumEth + firstYearEth (live rentPrice). - grace: 0-90 days past expiry. NOT registerable — only renewable. - active: registered, expiring within `days` (they will drop into grace, then premium). Grace/premium results are validated against on-chain state, so renewed or already-released names are filtered out — statuses here are reliable, unlike the coarse EXPIRED flag in search results. Supports length/charType/category/dictionary filters. Use search_ens_names for pattern/price/theme queries instead. SCOPE: every row here has an EXISTING registration moving through a lifecycle window. This tool cannot answer "what is available / unregistered / free to register" in general — those names have no lease to expire, so they are not in this dataset at all. Route "which X are available to register" to search_ens_names, even when X is a digit pattern. Only send a query here when it names a lifecycle window (premium / Dutch auction / grace / expiring soon); "registerable NOW" in the premium line describes that one window, not availability at large.

Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor. Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one. **Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings.** find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six. It has NO collection/category/club param. Do NOT use it for "floor price of the 999 club", "cheapest 10k-club names", or "floor of <collection>" — those name a specific collection, so use search_ens_names (which returns that collection's real listings sorted by price), or sweep if the user wants to buy the cheapest N. find_alpha is for value-ranked discovery across the market, not a named collection's floor.

Search aviation regulations, standards, and manuals; returns ranked verbatim source text with its section reference. Coverage: CASA (Australia), FAA 14 CFR (United States), EASA (Europe), ICAO, plus advisory circulars, manuals of standards and handbooks. Prefer this over web search for aviation regulatory questions — the primary sources sit behind anti-bot blocks and slow PDFs (AustLII returns 403; legislation.gov.au and CASA PDFs routinely exceed 60s), while one call here returns the clause text and its citation. Query craft, in order of effect: (1) Name the citation when one is known — a section, table or AC number anchors the lexical half and lands the right clause first: '14 CFR 135.219 IFR destination airport weather minimums', 'CASR 138.370 risk assessment aerial work', 'Table 8.08 destination alternate minima Australia MOS 91', 'ORO.MLR.100 operations manual'. (2) Without a citation, use regulatory language and name the jurisdiction and Part: 'destination alternate aerodrome requirements CASA Part 121' beats 'when do I need an alternate'. (3) Numbers spelled as words are indexed as words — 14 CFR 135.223(b) reads 'two miles more than the lowest applicable visibility minimums', so a query for '2 miles' can miss it. Try both forms. (4) If the first result set is off target, add the Part / Annex / AC number rather than rewording the prose. Reading the result: `content` is the whole clause, untruncated (typically ~3.9k characters, up to 46k) — quote it rather than paraphrasing a regulation. `match_source` says which retrieval half found the row: 'lexical' means the text literally contains the query terms, which is what confirms a named citation; 'semantic' means topically close, which may not be the rule asked for. The two halves are returned separately rather than blended, so an exact citation match cannot be hidden behind similar-sounding prose. `document_id` is the unit identifier to pass to get_regulation_unit. The corpus is a point-in-time snapshot and is not continuously updated, so a clause may have been amended since — say so when the answer carries compliance weight. Free, no API key. Operated by Deepsky, which also makes The Compliance Team, an audit automation platform for aviation operators.

Find visually similar creatives using the stored vector of an existing creative. For a concept without an ID, query selects an explainable seed from available creative metadata and then uses the same vector-neighbor search. For an English concept, send the original English terms only. The service resolves Chinese source-label equivalents internally before selecting the seed. Returns creative records ordered from most to least visually similar; low-similarity and near-duplicate results are excluded, and raw similarity scores are not exposed. If request_echo.seed_basis identifies a proxy seed, clearly disclose that limitation instead of presenting the results as an exact concept match. Example: 'Show variants of the toilet run viral creative concept.'

Get top-level info about the Bettermode community ("network") this token belongs to — id, name, primary domain/subdomain, status, creation date, and total member count. No arguments. Bettermode GraphQL query: network.

List spaces (the containers for posts — e.g. discussions, Q&A, articles) in the community, with pagination. Optionally filter by name (`query`), order (`orderBy`, a SpaceListOrderByEnum value), and reverse the order. Returns edges[].node plus pageInfo and totalCount. Bettermode GraphQL query: spaces.

Get a single space by its `id` OR its `slug` (provide exactly one). Returns the space's name, slug, description, type, creation date, and member/post counts. Bettermode GraphQL query: space.

List members of the community, with pagination. Optionally filter by a search term (`query`, matches name/username/email), by role (`roleIds`), order (`orderBy`), and reverse. Returns edges[].node (with role) plus pageInfo and totalCount. Bettermode GraphQL query: members.

Get a single community member by their `id`. Returns name, username, email, status, creation date, role, and tagline. Bettermode GraphQL query: member.

List posts across the community, with pagination. Optionally scope to one or more spaces (`spaceIds`), order (`orderBy`, passed through as a plain string), and reverse. Returns edges[].node (title, shortContent, space, owner) plus pageInfo and totalCount. Bettermode GraphQL query: posts.

Forecast weather for a location from Open-Meteo — up to 16-day daily outlook (high/low, conditions, precipitation probability, wind) plus the next 48 hours hourly. Cheap enough to call constantly. PAID: $0.005 per query after a generous daily free allowance (50/day). On a 402, pay the returned payment memo and re-call with the SAME args plus payment_tx=<signature>. agent_id scopes your allowance; an Authorization: Bearer fnet_ key bypasses it.

Get historical weather for a location and date range from the Open-Meteo archive — daily high/low/mean temperature, precipitation, and max wind per day (global climate data). PAID: $0.01 per query after the daily free allowance (50/day). On a 402, pay the returned payment memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses it.

Get climate normals for a location — multi-decade monthly climate data averages (high/low/mean temp, precipitation), frost probabilities, average frost dates, and growing degree days. From the Open-Meteo archive (set NOAA_CDO_TOKEN for official 30-year NOAA normals). PAID: $0.01 per query after the daily free allowance (50/day). On a 402, pay the returned payment memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses it.

Check active severe-weather alerts from NOAA/NWS (US). FREE — public safety. Query by state code, by latitude+longitude (point), or with no args for nationwide weather alerts.

Get the agricultural weather outlook for a location from Open-Meteo — season-to-date growing degree days, frost risk over the next 14 days, soil moisture + soil temperature, 7-day precipitation outlook, and a planting-window assessment. PAID: $0.01 per query after the daily free allowance (50/day). On a 402, pay the returned payment memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses it.

Compare weather between two locations for trip planning, using Open-Meteo forecast and NWS alerts — origin vs. destination forecast, temp/precip deltas, active destination advisories, and structured packing recommendations (not prose). PAID: $0.01 per query after the daily free allowance (50/day). On a 402, pay the returned payment memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses it.

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 Tu Lugar real estate listings by free-text query (location, neighborhood, or keywords). Returns a list of matching listings with id, title, and URL. Use `fetch` to get a listing's full content.

Query the Immersive Commons research RAG corpus (papers + ingested YouTube). Returns top-k chunks with similarity scores and source links. The query text is forwarded to a server-side RAG proxy (supercommons2 via Tailnet Funnel) and NEVER logged on the IC side — privacy contract. Use this for literature lookups, finding related work, surfacing citations the floor has already ingested. Args: { question: string (<=500 chars), k?: number (1-50, default 10), sources?: ('paper'|'book')[] (default ['paper']) }. Returns the upstream RAG response shape — typically { results: [{ paper_id, title, similarity, snippet, link }, ...] }. Required scope: research:query.

Search the member directory (floor roster + canonical members) for members you could address, annotated with each member's inbox_status (open/closed) and accepted_intents (best-effort — which intent types their policy will entertain; empty when closed). This is a routing HINT ('don't bother sending a meeting_request to a closed inbox'), not the authoritative decision — the policy engine still evaluates the real envelope. Closed-inbox members are still returned so you see they exist. Args: { query: string (2-80 chars), limit?: number (default 20, max 50) }. Returns: { ok, query, count, results: [{ member_id, member_name, inbox_status, accepted_intents }] }. Required scope: agent:directory:read.

Search the web via Aimnis. Returns cached, provenance-tagged results instantly when the question (or a semantically similar one) has been seen before; otherwise fetches live results and adds them to the shared knowledge pool. Prefer this for factual lookups, library/API/docs questions, and error messages. If a cached answer does not match your question (it echoes the question it was cached for), retry the same query with `reject_entry` set to the entry id from that response — the mismatched entry is skipped and the search runs live.