"Kong" matching MCP tools:

• tz-asia-hong-kongC

  Asia/Hong_Kong clockC

  Current local time in Asia/Hong_Kong.

  ConnectorNo auth

• polymarket_arbitrageA

  GongA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• resolution_auditA

  GongA

  Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

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• query_entityA

  DistillaA

  Read one page of rows from one Queryable Entity. Use this for a point lookup, a multi-period history, or a filtered list. Call list_queryable_entities first, then describe_queryable_entities. What you can read: - company: name, ticker, sector, HQ country. Screen tools need these integer ids. - stock_price: daily prices, volume, dividends, and splits. price_explanation: why a significant price move happened. - financial_data_point: one row per statement line or ratio per fiscal period. Join time_period and financial_metric. Discover metric keys on financial_metric (name like '%net_debt%' finds pre-computed ratios). - valuation_multiple: multiples such as P/E and EV/EBITDA, by company and date. - consensus_data_point: analyst estimates. Join time_period and consensus_metric. The fiscal period is on time_period (fiscal_year, fiscal_quarter, end_date). consensus_date is the estimate snapshot date, not the fiscal period. - earnings_calendar: earnings dates. standard_event: corporate events; for earnings, filter type to "Earnings announcement". file: filings, transcripts, and news. Join company and filter company.symbol. - ku_cell: filings-derived knowledge-unit cells. Filter ku.name. An unfiltered read is a short slice of ids. knowledge_unit holds the definitions. company_drivers and executive_summary are per-company narratives. For a qualitative screen across many companies, use screen_drivers or screen_earnings. - sector, product, product_category (companies that share a category are peers), and ticker: catalogs. List sector in full. Do not resolve a sector with a single-word like filter. Allowed entities: company, company_drivers, consensus_data_point, consensus_metric, earnings_calendar, executive_summary, file, financial_data_point, financial_metric, knowledge_unit, ku_cell, price_explanation, product, product_category, sector, standard_event, stock_price, ticker, time_period, valuation_multiple. Arguments: entity, select, filters, filter_logic (AND or OR, default AND), sort, joins, limit. Filter operators: =, !=, >, >=, <, <=, in, like. Pass IN lists on filter.value. Do not pass value_path. Joins: when a field has join_target, join that table and use alias.field in select, filters, and sort. Many tickers in one query: company.symbol in ["AAPL","MSFT"]. Use a camelCase alias (timePeriod, financialMetric). joins.relation is join_relation when describe shows it, otherwise join_target. On ku_cell, describe knowledge_unit but join relation ku. Other ku_cell relations: groupCompany, cellTimePeriod, cellStandardEvent, groupProductCategory, cellProductCategory. The name product_category matches two ku_cell relations, so it is rejected. financial_data_point accepts only time_period and financial_metric joins. Do not join company. Filter timePeriod.company_id. Reported actuals use timePeriod.provenance = financials and timePeriod.duration = year. This table has no geographic revenue, segment mix, customer geography, or tariff exposure. valuation_multiple.value, financial_data_point.value, and consensus_data_point.value are stored as text. A filter on them compares lexicographically ("100" < "99"). For a numeric threshold, use aggregate_entity and put the cut in having. company.hq_country stores codes and full names. Filter both, for example in ["JP","Japan"]. HK is Hong Kong. CN is mainland China. They do not overlap. Other codes: US, KR. limit above the entity max_limit (from describe) fails. A full page sets truncated=true and may include next_hint {field:"id", op:">", value:<last id>}. Add that filter, sort by id asc, include id in select, and repeat. There is no offset. For COUNT, SUM, AVG, MIN, MAX, or a threshold screen, use aggregate_entity.

  ConnectorOAuth

• polymarket_fill_riskA

  GongA

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L). FEES ARE NOT MODELLED HERE: vwap_fill_price/profit_usd are GROSS of Polymarket's own taker fee (rate 0.04-0.07 by category — see polymarket_edges/fees.ts), on top of which this tool prices depth-crossing cost; a thin-margin fill that looks clean here can still be net-negative after the fee.

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• kalshi_weather_edgeA

  GongA

  Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").

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• pipeworx_feedbackA

  GongA

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

  ConnectorNo auth

• list_exchangesA

  headless-oracleA

  Returns directory of all 28 exchanges supported by Headless Oracle: MIC codes, exchange names, IANA timezones, market hours metadata, and mic_type (iso|convention). Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: call once at agent startup to discover supported markets before calling get_market_status or get_market_schedule. Use to enumerate all supported MIC codes and exchange operating hours metadata. Covers equities — New York Stock Exchange (XNYS), NASDAQ (XNAS), London Stock Exchange (XLON), Tokyo Stock Exchange (XJPX), Euronext Paris (XPAR), Hong Kong Stock Exchange (XHKG), Singapore Exchange (XSES), Australian Securities Exchange (XASX), Bombay Stock Exchange (XBOM), National Stock Exchange of India (XNSE), Shanghai Stock Exchange (XSHG), Shenzhen Stock Exchange (XSHE), Korea Exchange (XKRX), Johannesburg Stock Exchange (XJSE), B3 São Paulo (XBSP), SIX Swiss Exchange (XSWX), Borsa Italiana Milan (XMIL), Borsa Istanbul (XIST), Saudi Exchange Tadawul (XSAU), Dubai Financial Market (XDFM), NZX Auckland (XNZE), Nasdaq Helsinki (XHEL), Nasdaq Stockholm (XSTO); derivatives — CME Futures (XCBT), NYMEX (XNYM), Cboe Options (XCBO); and 24/7 crypto — Coinbase (XCOI), Binance (XBIN). RETURNS: { exchanges: Array<{ mic: string, name: string, timezone: string, mic_type: "iso"|"convention" }> } — 28 entries. Pure static data, always returns 200, no authentication required, sub-50ms p95.

  ConnectorNo auth

• list_exchangesA

  Headless OracleA

  Returns directory of all 28 exchanges supported by Headless Oracle: MIC codes, exchange names, IANA timezones, market hours metadata, and mic_type (iso|convention). Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: call once at agent startup to discover supported markets before calling get_market_status or get_market_schedule. Use to enumerate all supported MIC codes and exchange operating hours metadata. Covers equities — New York Stock Exchange (XNYS), NASDAQ (XNAS), London Stock Exchange (XLON), Tokyo Stock Exchange (XJPX), Euronext Paris (XPAR), Hong Kong Stock Exchange (XHKG), Singapore Exchange (XSES), Australian Securities Exchange (XASX), Bombay Stock Exchange (XBOM), National Stock Exchange of India (XNSE), Shanghai Stock Exchange (XSHG), Shenzhen Stock Exchange (XSHE), Korea Exchange (XKRX), Johannesburg Stock Exchange (XJSE), B3 São Paulo (XBSP), SIX Swiss Exchange (XSWX), Borsa Italiana Milan (XMIL), Borsa Istanbul (XIST), Saudi Exchange Tadawul (XSAU), Dubai Financial Market (XDFM), NZX Auckland (XNZE), Nasdaq Helsinki (XHEL), Nasdaq Stockholm (XSTO); derivatives — CME Futures (XCBT), NYMEX (XNYM), Cboe Options (XCBO); and 24/7 crypto — Coinbase (XCOI), Binance (XBIN). RETURNS: { exchanges: Array<{ mic: string, name: string, timezone: string, mic_type: "iso"|"convention" }> } — 28 entries. Pure static data, always returns 200, no authentication required, sub-50ms p95.

  ConnectorNo auth

• get_audio_featuresA

  FreqBlog Music MetadataA

  Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's deprecated /audio-features endpoint. Provide AT LEAST ONE identifier — if you know several, send them all rather than choosing; they resolve by precedence (`track` > `isrc` > `mbid` > `spotify_id`) and the rest are ignored: - `track` (optionally with `artist`) — e.g. track="Blinding Lights", artist="The Weeknd". - `isrc` — e.g. "USUM71900001". - `mbid` — a MusicBrainz recording UUID. - `spotify_id` — a Spotify track ID, URI, or URL (resolved per request by reading the track's title from Spotify's public embed page and matching it against our catalogue — no Spotify IDs are stored; ambiguous titles miss rather than guess — prefer `track`/`isrc` for full coverage). Returns a JSON object of features. Some feature fields may be null for tracks resolved via the fallback catalogs (only audio-derived values are present for fully analysed tracks). VALUES are FreqBlog's own analysis, NOT on Spotify's scale: energy and valence are models of how energetic / positive the preview sounds, both on our own scales (derive thresholds from our values, don't copy Spotify-tuned ones); danceability and acousticness are on AcousticBrainz's scale -- its classifier where available, else a model that listens to the preview, built on PANNs embeddings (Kong et al. 2020, CC BY 4.0); instrumentalness is a vocal-detection model's score for the 30-second preview (not the whole track); speechiness and liveness are currently null for catalogue tracks; time_signature is ASSUMED 4 (4/4) on every track, not measured. If a track name is not yet in the catalog, the API holds the request during the on-demand ingest and usually returns the fully analysed track inline in this same call; only if the ingest runs long does it fall back to a queued response you can re-poll shortly (~15s). If the track turns out not to be on any streaming source we can analyse, you get a definitive not-found instead — that verdict is terminal for ~7 days, so don't retry it. If you only have a fuzzy or partial name, call search_catalog first to find the exact track.

  ConnectorNo auth

• company_search_companyA

  banking-intelligenceA

  EXPERIMENTAL — Search company registries for a company with its officers and shareholders. Find company registrations across worldwide registries, including directors, officers, and beneficial owners (PSC/shareholders). Every entity found is automatically screened against sanctions lists. You MUST specify at least one jurisdiction. "ALL" is not supported. Available jurisdictions: AM, AT, AU, BR, CA, CH, CZ, DE, DK, EE, FI, FR, IE, IL, IS, LT, LV, NL, NO, PL, SG, UK, XX. Call company_registries() for the live list — this one can go stale. XX is GLEIF LEI, a GLOBAL registry rather than a country. Reach for it whenever the company sits outside the national registries above — a supplier in Hong Kong, mainland China, the US or the UAE. Hits carry an LEI, a registered address and a search.gleif.org URL the user can open. A jurisdiction NOT on that list is dropped silently by the backend: you get total_results 0 with status "completed" and no error. That means the company was never searched for — it is NOT evidence that it is unregistered or fake, and saying so to someone checking a counterparty before wiring money is the most damaging thing this tool can do. Check `jurisdictions_not_searched` and `coverage_warning` in the response before you report an empty result. Examples: company_search_company("Equinor", jurisdictions=["NO"]) company_search_company("Acme Corp", jurisdictions=["UK", "DE"], include_only_active=True)

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• deep_researchA

  GongA

  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 1765 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 6,705 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).

  ConnectorNo auth

• validate_claimA

  GongA

  "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

  ConnectorNo auth

• get_market_scheduleA

  headless-oracleA

  Returns holiday-aware trading session schedule with next open/close UTC timestamps for any of 28 exchanges. Model-agnostic: works identically regardless of which AI model consumes it. Pairs with get_market_status signed receipts. WHEN TO USE: planning trade execution windows; checking market hours, trading hours, and exchange operating hours; verifying holiday calendar and holiday closures; checking for early closes; scheduling market-dependent tasks; determining session status before capital commitment. Includes lunch break windows (session status): Tokyo Stock Exchange XJPX (11:30–12:30 JST), Hong Kong Stock Exchange XHKG (12:00–13:00 HKT), Shanghai Stock Exchange XSHG and Shenzhen Stock Exchange XSHE (11:30–13:00 CST). Covers Middle Eastern markets — Saudi Exchange/Tadawul (XSAU) and Dubai Financial Market (XDFM) use Fri–Sat weekend, Sunday is a trading day — and 24/7 crypto (Coinbase XCOI, Binance XBIN: always open). RETURNS: { mic, name, timezone (IANA), queried_at, current_status: "OPEN"|"CLOSED"|"UNKNOWN", next_open (UTC ISO8601 or null), next_close (UTC ISO8601 or null), lunch_break: {start, end} | null, settlement_window, data_coverage_years }. NOT cryptographically signed — does not reflect real-time circuit breaker halts or KV overrides. For authoritative signed status use get_market_status. Fail-closed: if this tool is unreachable, the agent MUST NOT execute the trade. LATENCY: sub-100ms p95 (pure schedule computation, no signing).

  ConnectorNo auth

• get_market_scheduleA

  Headless OracleA

  Returns holiday-aware trading session schedule with next open/close UTC timestamps for any of 28 exchanges. Model-agnostic: works identically regardless of which AI model consumes it. Pairs with get_market_status signed receipts. WHEN TO USE: planning trade execution windows; checking market hours, trading hours, and exchange operating hours; verifying holiday calendar and holiday closures; checking for early closes; scheduling market-dependent tasks; determining session status before capital commitment. Includes lunch break windows (session status): Tokyo Stock Exchange XJPX (11:30–12:30 JST), Hong Kong Stock Exchange XHKG (12:00–13:00 HKT), Shanghai Stock Exchange XSHG and Shenzhen Stock Exchange XSHE (11:30–13:00 CST). Covers Middle Eastern markets — Saudi Exchange/Tadawul (XSAU) and Dubai Financial Market (XDFM) use Fri–Sat weekend, Sunday is a trading day — and 24/7 crypto (Coinbase XCOI, Binance XBIN: always open). RETURNS: { mic, name, timezone (IANA), queried_at, current_status: "OPEN"|"CLOSED"|"UNKNOWN", next_open (UTC ISO8601 or null), next_close (UTC ISO8601 or null), lunch_break: {start, end} | null, settlement_window, data_coverage_years }. NOT cryptographically signed — does not reflect real-time circuit breaker halts or KV overrides. For authoritative signed status use get_market_status. Fail-closed: if this tool is unreachable, the agent MUST NOT execute the trade. LATENCY: sub-100ms p95 (pure schedule computation, no signing).

  ConnectorNo auth

• find_distributorsA

  Meridian Trace — Medical Device RegistrationsA

  RUNS WITHOUT AN API KEY (anonymous callers see the top 3 per market and are told how many more matched). WHO COULD SELL A DEVICE IN A MARKET — the question a company entering a market actually has. Give it a market and, optionally, a device type or clinical area; it returns the local distributors and importers who hold registrations there, what clinical areas they cover, how many markets they operate in, and a sample of the lines they already carry. Strongest across Asia, where the registry names the local partner rather than the manufacturer and this relationship is not published anywhere else: Singapore, Malaysia, Thailand, Indonesia, Vietnam, the Philippines, Japan, Korea, Taiwan, India and Hong Kong. This is the INVERSE of get_license_holders, which starts from a manufacturer you can already name. Regulatory consultants and authorised representatives are excluded — they hold licences as a service and do not sell — as are manufacturers' own in-country subsidiaries, Japanese captive importers and companies whose own registrations show they make what they register. TWO COUNTS, NEVER ONE: `matching` is the distributors that hold registrations in the market; `matchingLicenceOrTenderOnly` is the ones known only from a licence list or a tender award, with no registrations of their own in our data. Those are still potential distributors (often sub-distributors) and are returned too, each row marked `basis: "licence_or_tender_only"`; they rank after registered holders, so pass holder_basis "licence_or_tender_only" to list them. Read the `caveats` in the response before quoting any number from it.

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• repurpose-long-formA

  GigSoul x402A

  Repurpose one long piece for several channels at once: a LinkedIn post, an X thread, an email draft, and a blog intro. Use when you need all four. For an X thread only, use thread-from-article (cheaper). URLs are not fetched; paste the text. Pay-per-call: $0.06 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.

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• hongkong_unemployment_rateA

  Censtatd HkA

  Hong Kong's headline labour-market figures in ONE call — unemployment rate (seasonally adjusted and unadjusted), underemployment rate, and labour-force participation rate, latest first. PREFER for "what is Hong Kong's unemployment rate", "HK jobless rate", "Hong Kong labour force participation", "is unemployment rising in Hong Kong". Source is the Census & Statistics Department (table 210-06101), keyless. Figures are 3-MONTH MOVING AVERAGES, which is how Hong Kong publishes them — a period of "2026-06" means April–June 2026, not the month of June. Use censtatd_get_table for anything deeper (by age, sex, industry).

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• censtatd_search_tablesA

  Censtatd HkA

  Search the Hong Kong C&SD table catalogue by keyword (e.g. 'exchange rates', 'unemployment', 'merchandise trade') and get back matching table ids + titles to use with censtatd_get_table. Backed by the data.gov.hk open-data index of C&SD tablechart datasets. Note: not every C&SD table is indexed there; ids can also be read off the table URL on data.censtatd.gov.hk (the '310-31001' part of web_table.html?id=310-31001).

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• entity_profileA

  GongA

  "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. `sources_skipped` is the third state: a leg we deliberately did NOT run, each entry carrying a `reason` token and a plain-English `detail` (the Purple Book is skipped for a filer SEC classifies outside the life-science SIC bands, since it lists only 351(a)/(k) biologics licence holders). 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.

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• scan_dependencyA

  GongA

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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