Tool search 101,857 tools · 5,224 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.

ChatGPT Deep Research compatibility tool. Returns a ranked list of Swiss court decisions matching a query, each as {id, title, url, snippet}. Pair with `fetch` to retrieve a decision's full text by id. General (non-deep-research) clients should prefer `search_decisions`, which exposes filters (court, canton, date, language) and richer metadata.

Use this tool to find COURT DECISIONS (Rechtsprechung): 1,050,000+ Swiss federal and cantonal decisions plus ~2,800 ECtHR decisions concerning Switzerland. Full-text search with keywords, phrases (in quotes), Boolean operators (AND, OR, NOT), prefix matching (word*), docket lookup (6B_1234/2025) and column-scoped search (regeste:keyword). Filter by court, canton, language, date range, chamber. Results are relevance-ranked and enriched with citation counts, statute references and is_leading_case. Use offset to page. Most recent decisions: empty query + sort='date_desc' + court filter. Not for statutes (search_laws), commentary (search_scholarship), or administrative guidance (search_practice).

Find the most-cited decisions for a topic or statute. Authority ranking based on citation graph. Filter by statute (law_code + article), topic query, court, and date range. Top-3 results auto-attach a `pinpoint` field {e_number, matched_sentence, confidence, url, score, source} anchoring the most-relevant Erwägung — see system instructions U3 for how to surface it. Each result also carries citation_string_{de,fr,it} + canonical_url + is_leading_case + citation_count for ready-to-quote Swiss-format citations.

Year-by-year decision counts showing jurisprudence evolution. Use with a statute reference (law_code + article), a text query, or both. Returns yearly counts with visual bar chart.

Get the canonical Swiss citation string for a decision reference. CALL THIS BEFORE writing any case citation in your response. Returns ready-to-embed citation_string (DE/FR/IT variants plus a canonical URL) and a verbatim rule_statement. If the reference doesn't exist, returns exists=false plus close_matches for typo-correction — DO NOT guess or construct citations yourself; if you get exists=false, either re-query with a close match or skip the citation entirely. Accepts any Swiss reference form: decision_id (bger_4A_747_2012), BGE reference (BGE 140 III 86), or docket number (4A_747/2012). Optional pinpoint ('2.3') generates the Erwägung-anchored citation and URL.

Use this tool to find STATUTE ARTICLES by topic — full-text over every locally indexed article, federal (Fedlex mirror) AND cantonal (LexFind mirror, all 26 cantons), interleaved. Not for whole laws by name (search_legislation) or court decisions (search_decisions). BM25-ranked per corpus, merged by interleaving so each response surfaces both jurisdictions. Returns ranked snippets with article number, heading, law title, canton, and level ('federal' | 'cantonal'). Use this as the DEFAULT entry point when the user asks about any Swiss legal topic and you don't know which law or which jurisdiction applies. Filter with canton='ZH' (etc.) for cantonal-only, or jurisdiction='federal'/'cantonal' for explicit scoping. Examples: search_laws(query='Verjährung') — find statute-of-limitations provisions in federal + cantonal laws; search_laws(query='Hundehaltung', canton='ZH') — ZH dog-keeping rules; search_laws(query='Mietrecht', jurisdiction='federal') — federal-only tenancy law.

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.

[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.

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}.

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.

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.

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)

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.'

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)

A standard layer for macro, institutional, and real-time market data. Call this tool when curated coverage is thin, empty, or when the query is explicitly demand/attention-shaped (e.g. to get search volume, economic series, or census data). It retrieves data from 80+ authoritative sources (Google Trends, FRED, BLS, Census, etc.) fanned out in parallel. Returns categorized data blocks with source attribution and metadata. Note: call after search_graph indicates thin/empty coverage via its coverage annotation. Uses 5 tokens ($2.50 via SPT) per standalone use.

Create a new CatchAll processing job from a natural-language query. Use when: - You want to start a new CatchAll web research run from a user query. - You want the API to fetch/process sources and then return structured results. Do not use when: - You want status for an existing job (use `get_job_status`). - You want records for an existing job (use `pull_results`). Key rules: - `query` is required. - You can submit with only `query`; omitted optional fields (`validators`, `enrichments`, `start_date`, `end_date`) are auto-selected/generated by the API. - Optional fields are independent: you can pass any subset (for example, custom `validators` but no `enrichments`), and omitted fields are still auto-selected/generated. - When `connected_dataset_ids` is set, the `query` must describe the **topic or event type only** (e.g. "M&A activity", "regulatory filings", "executive changes"). Do NOT write things like "for my companies", "for the selected list of companies", or "news about my watchlist" — the entity filtering is applied automatically by the connected dataset. Mentioning companies in the query when a dataset is attached is redundant and degrades retrieval quality. - When `connected_dataset_ids` is set, entity-relevance validators (e.g. `company_is_primary_subject`) are generated automatically by the API. Do NOT add them manually to `validators` — they are redundant and may conflict with the auto-generated ones. Only pass validators that describe the **event or topic**, not entity filtering. - `start_date` and `end_date` filter by web page discovery date, not event date. - Discovery dates and extracted event dates can differ. For event-time accuracy, use event-focused validators/enrichments and verify `event_date` in pulled results. - `end_date` must be after `start_date`. - Dates outside your plan lookback limits return API 400. - `limit` controls processed record count (cost-affecting). Omit it to retrieve everything up to your plan's maximum. If provided, must be >= 10. - `validators` / `enrichments` may be passed either as arrays or as JSON-string arrays (for client compatibility). - `validators[].type` must be `boolean` (if omitted, it defaults to `boolean`). - `enrichments[].type` supported values: text, number, date, option, url, company. Basic examples: - validators: `[{"name":"is_acquisition_event","description":"true if page describes an acquisition","type":"boolean"}]` - enrichments: `[{"name":"acquiring_company","description":"Extract acquiring company","type":"company"},{"name":"deal_value","description":"Extract announced deal value","type":"number"}]` Next step: - Save the returned `job_id`. - Poll `get_job_status` and call `pull_results` (partial results can appear before completion).

Preview suggested validators, enrichments, and date ranges before submitting. Use when: - You want to inspect/edit auto-generated validators/enrichments before submitting. - You want to preview date adjustments via `date_modification_message`. Do not use when: - You want to start processing immediately with final inputs (use `submit_query`). Key behavior: - Preview-only endpoint: does not create a job and does not start processing. - Suggestions are LLM-generated and not deterministic across calls. - To reuse suggestions, pass them explicitly to `submit_query`.

Check the status of a submitted job. Call this after submit_query to see if your job is ready. Status progression: submitted -> analyzing -> fetching -> clustering -> enriching -> completed/failed IMPORTANT: Jobs take several minutes to process. First check after ~1-2 minutes, then poll every 30-60 seconds. Broad searches can take 10-30+ minutes; for long jobs, poll every 60-120 seconds. Do NOT call this tool in a tight loop. Stop polling when status is `completed` or `failed`. Treat `submitted`, `analyzing`, `fetching`, `clustering`, and `enriching` as active states and continue polling. You don't need to wait for completion to pull results. Partial results are available during `enriching` — call pull_results after ~2 minutes, then poll status every 30-60 seconds and pull again for fresher results. Do not stop pulling just because an intermediate pull is empty/unchanged. Use `progress_validated` vs `candidate_records` to track whether more results may still appear (`progress_validated < candidate_records`). If transport/session fails, resume using the same `job_id`.

Check the quality of a query before submitting a job ("Check Query Quality"). Use when: - You want quick feedback on whether a query is well-formed for CatchAll before spending credits on a job. - You want concrete suggestions to improve a vague or overly broad query. Do not use when: - You want to preview auto-generated validators/enrichments (use `initialize_query`). - You want to actually run a search (use `submit_query`).

Create a recurring monitor from a completed job. Monitors re-run a job's query on a schedule. Use the explore -> refine -> automate pattern: submit a job, refine until results match, then create a monitor. The schedule is defined in natural language (e.g., 'every day at 9 AM EST'). Always include a timezone (in the schedule text or via the `timezone` arg). API-enforced constraints apply: - If `backfill=true`, reference job end_date must be within the last 7 days - If `backfill=false`, reference job age does not matter - Minimum schedule frequency depends on your plan Webhooks are now centralized: register them with `create_webhook`, then pass their IDs here via `webhook_ids` (there is no inline webhook config anymore).

List all your monitors. Returns all monitors with their schedule, status, reference query, and webhook config.

Full-text + fuzzy search across 12 367 Czech audiobooks (title, author, narrator). Use when an agent needs to resolve a free-form query into one or more audiobook records.

Multi-facet search of audiobooks: combine genre, narrator, max price, year range. Use when an agent has structured constraints rather than a free-form query. V1 supports genre + narrator + price_max + year filters; partner filter coming in V2.

List indexed documentation projects. Use this first to discover what's available. Supports a forgiving fuzzy `query` over slugs (ranked by relevance) plus `limit` and `skip` for pagination.