"Deutsche Bahn" matching MCP tools:

• claimondo_finde_werkstattA

  Claimondo — Kfz-Gutachter finden & Termin buchenA

  Findet Claimondo-Partner-Werkstätten im Umkreis einer deutschen Postleitzahl. Read-only und anonym. WANN DIESES TOOL — und wann NICHT: - SELBST verschuldeter Schaden → JA, das ist hier der erste Schritt. Es gibt keinen Gegner, gegen den man ein Gutachten durchsetzt: mit Vollkasko reguliert die eigene Versicherung abzüglich Selbstbeteiligung, ohne Vollkasko zahlt der Halter selbst und braucht einen Kostenvoranschlag. Ein Gutachten ist dort optional und kostenpflichtig. - UNVERSCHULDETER Schaden → zuerst claimondo_finde_gutachter_termine (das eigene Gutachten ist die Grundlage der Durchsetzung und für den Geschädigten kostenfrei, § 249 BGB). Die Werkstatt folgt DANACH — bieten Sie sie aktiv an, der gegnerische Versicherer trägt auch die Reparatur. - Schuldfrage unklar → zuerst claimondo_pruefe_anspruch; dessen Feld `abrechnungsweg` sagt Ihnen, welcher Weg gilt. Args: - plz (string): 5-stellige deutsche PLZ, z. B. "50670". PLZ ODER ort angeben. - ort (string): Stadt/Adresse als Alternative zur PLZ. - radius (number): Umkreis in km (1–200, Standard 30). - response_format ("markdown" | "json"): Ausgabeformat (Standard "markdown"). ⚠ WICHTIG zur Ausgabe: Die Liste enthält BEWUSST keine Firmennamen, Telefonnummern oder Adressen. Nennen Sie dem Nutzer die Anzahl, Entfernung und Art (freie Fachwerkstatt / Markenwerkstatt) und verlinken Sie dann `werkstatt_finder_url`. Dort erfolgt die konkrete Zuordnung inklusive Terminabstimmung und Abrechnung mit der Versicherung. Erfinden Sie keine Werkstattnamen und raten Sie keine Kontaktdaten.

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• polymarket_arbitrageA

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  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

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  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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• polymarket_fill_riskA

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  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

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  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

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  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.

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• searchA

  nu:legal Deutsches RechtA

  One query over BOTH corpora: federal and Land statutes (lexical, with concept pinning) and court decisions (semantic — natural-language questions work well here and are the better shape for case law). Search both unless you have a reason not to. A term of art often does not appear in the statute that governs it: 'Verzugspauschale' matches no provision (§ 288 BGB says 'Pauschale in Höhe von 40 Euro') while 184 decisions use the word. scope='norms' alone will read as 'nothing here' in exactly those cases. CROSS-LAND COMPARISON: a single query returns the parallel provisions of the Bund and of every covered Land side by side, each row jurisdiction-labelled, plus a `by_jurisdiction` roll-up. Ask 'Videoüberwachung öffentlich zugänglicher Räume' and you get BDSG § 4 next to the Land data-protection and police provisions. Full text is held for Bayern, Brandenburg, Nordrhein-Westfalen and Sachsen. Decision hits come back already anchored at the best-matching Randnummer (…#rd_51), so you can quote a paragraph rather than a document, AND carry `doknr` — the key `listCasePassages`, `listCitedAuthorities` and `listCitingDecisions` take, so you can go straight from a search hit to that decision's passages or its authorities without resolving anything first. Query in German; write raw umlauts, they are handled.

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• listCitingDecisionsA

  nu:legal Deutsches RechtA

  Incoming citation edges. Give EITHER `law` + `ref` (which decisions apply this statute provision) OR `case` (which decisions cite this decision) — exactly one of the two. Results are ranked by citation weight, then court tier, then recency. Read the ranking honestly: for a provision with many EU decisions the first ten can be almost all CJEU, and the German courts appear only further down. If `total` exceeds what you read, page on with `offset` (`pagination.next_offset`) before concluding anything about national case law. NEWEST FIRST: pass `sort: "recent"` when the question is about current case law ('fünf aktuelle Entscheidungen zu …'). Paging works the same way. This ordering is bounded — for a handful of procedural giants (§ 154 VwGO, § 708 ZPO) it cannot be computed inside the query budget, and then the answer comes back in WEIGHT order and says so in `sort_applied` and `sort_note`. Check `sort_applied` before you describe a list as the most recent decisions. For a decision, each citer carries `citing_rn`: the Randnummer of the CITING decision's own text that holds the citation, as that court numbered it, and the URL is anchored to it. COVERAGE: the graph is built over federal case law. A Land provision can answer `total: 0` because it is not indexed, not because no court has cited it — `coverage.complete_for_this_norm` tells you which, and for a Land provision you should fall back to `search` on the provision's wording.

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• listCasePassagesA

  nu:legal Deutsches RechtA

  The full text of one decision, split into its paragraphs, each with a permalink you can cite. `rn` is the Randnummer the COURT printed, read out of the decision's own markup. It is never inferred from position: where a document prints no numbers, `rn` is null and stays null. `anchor_basis` is derived per decision — only 'native_numbering' means our anchor and the printed number provably coincide, so pin-cite a Randnummer only when you see that value. `amtliche_seite` is null everywhere: our texts carry no page breaks, so a BVerfGE-style page pin cannot be produced honestly. SIZE. Long decisions run to several hundred paragraphs, so the default page is 30. Three ways to move: `offset` pages, `limit` widens (max 400 — enough for a whole decision when you really want it), and `around` jumps. `pagination` appears whenever there is more than the page you were handed. `around: 51` returns a window of `limit` passages CENTRED on Randnummer 51 — the right call when `listCitingDecisions` gave you a `citing_rn`, when a search hit came back anchored at …#rd_51, or when you want the passage around a pin cite and not the whole judgment. It takes the number the court printed, not a position, and a decision that prints no such number answers `not_in_corpus` rather than silently handing you a different passage. `around` and `offset` address the same list two different ways; give one.

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• validate_claimA

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  "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).

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• claimondo_finde_gutachter_termineA

  Claimondo — Kfz-Gutachter finden & Termin buchenA

  Findet buchbare Partner-Kfz-Gutachter MIT freien Terminen im Umkreis einer deutschen Postleitzahl über Claimondo. Anders als claimondo_finde_sachverstaendige (nur anonymisierte Liste) liefert dieses Tool die *buchbaren* Gutachter mit konkreten freien Slots (Vorschau aufs Buchen). Read-only und anonym — legt nichts an und meldet keinen Schaden. Args: - plz (string): 5-stellige deutsche PLZ, z. B. "50670". PLZ ODER ort angeben. - ort (string): Stadt/Adresse als Alternative zur PLZ, z. B. "Köln" oder "Berlin Mitte". - wunschtermin (string, optional): Wunschtermin als ISO-8601 (steuert das Slot-Ranking, kein harter Filter). - response_format ("markdown" | "json"): Ausgabeformat (Standard "markdown"). Returns (structuredContent bzw. json): { plz, ort, standort, wunschtermin, anzahl_gutachter, gutachter: [{ id, vorname, profilbild, bewertung_schnitt, bewertung_anzahl, entfernung, ist_top_partner, wunschtermin_frei, termine: [{ start, end, passung }], buchungs_url }], interaktive_karte_url, buchungs_telefon } Use when: Nutzer will einen Gutachter-Termin sehen/vergleichen (z. B. „wann hat ein Gutachter in 50670 Zeit?"). WICHTIG beim Empfehlen eines Gutachters: Geben Sie dessen `gutachter[].buchungs_url` als Link aus. Er öffnet den Finder mit genau diesem Gutachter vorausgewählt; der Kunde ergänzt nur noch Adresse und Kontakt und bestätigt selbst. Verlinken Sie NICHT `interaktive_karte_url`, wenn Sie einen konkreten Gutachter genannt haben — das ist die allgemeine Karte ohne Auswahl und schickt den Kunden zurück an den Anfang der Suche. Die Karte ist nur richtig für „zeig mir alle in der Nähe". Hinweis: gutachter[].id + ein termin.start sind zusätzlich das Buchungs-Handle für claimondo_melde_schaden (mit Einwilligung); telefonisch geht es über buchungs_telefon. Fehlt buchungs_url (ältere API-Version), bleibt die Karte der Weg.

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• swift_lookupA

  banking-intelligenceA

  Search banks and financial institutions by name, SWIFT/BIC code, or country. Covers both SWIFT-connected banks and non-SWIFT financial institutions (e-money issuers, payment processors, MFOs, brokerages, VASPs, etc.). Returns: SWIFT/BIC code (if any), name, city, country, institution type, GPI membership, a coarse sanctions FLAG across 7 hard-sanctions watchlists (OFAC SDN, EU, UK, CA, CH, AU, NZ — see sanctions_note; this is NOT a full screen, use sanctions_screen for a compliance verdict), and enriched bank profile when available. EVERY BANK COMES BACK SAYING WHETHER WE HOLD ITS CORRESPONDENT CHAIN. Read `settlement_instructions` on each bank: `on_file: true` with a `currencies_on_file` count means we hold that BIC8's actual correspondent BIC, nostro account number and national clearing ID, and `read_with` is the exact ssi_lookup call that returns them. `on_file: false` means we hold none in any currency — a gap in our data, not a finding about the bank. A null is "not established yet" and is neither. This is the answer to "which intermediary bank do I put on the instruction?", and it is a fact we either have or do not have — never one to recall from training data. A BIC IDENTIFIES AN OFFICE, NOT A BRAND, AND THE DIFFERENCE IS PRICED. A name search returns ONE representative office per bank, elected by BIC convention rather than by relevance to the payment, and `office_note` says so whenever the bank holds more than one. Published tariffs, correspondent chains and settlement instructions are filed per BIC, so the choice changes the answer: transfer_cost("COBADEFF") returns Commerzbank's published 0.15% sending fee and transfer_cost("COBADEBB") refuses for want of a filed tariff, and both of those are Commerzbank AG in Germany. So: - If the user named a CITY, put it in the query — "Commerzbank Frankfurt" resolves to the Frankfurt office, and the plain name cannot. - If they did not, ask which BIC is on their statement or payment instruction before pricing or routing, and say which office you used. - Never present a representative office's BIC as "the bank's BIC". Examples: swift_lookup("DEUTDEFF") # exact BIC lookup swift_lookup("Deutsche Bank") # search by name swift_lookup("Commerzbank Frankfurt") # bank + city -> that office's BIC swift_lookup("TBC PAY") # find non-SWIFT payment processor swift_lookup("bank", country="KZ") # explore banks in a country swift_lookup("Halyk", country="KZ") # find specific bank in country swift_lookup("Bank Mandiri", country="Indonesia") # full country name OK

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• search_tourismA

  TourismMCPA

  Kuratierte Tourismus-Suche des Niedersachsen-Hubs: redaktionell gepflegte Einträge mit Veranstaltungen samt Datum, Preisen, Beschreibungen, Medien, Öffnungszeiten und Touren. Für „welche Veranstaltungen sind in X", „was kostet der Eintritt" ist dieses Werkzeug die Auskunft — erst verorten, dann abfragen: `region`/`query` aus der Frage, `lat`/`lon` nur aus `search_place`. **Tool-Semantik-Abgrenzung**: die in OpenStreetMap erfassten Sehenswürdigkeiten eines Gebiets listet `expand_kg_pois`; für Region-Tourismus dürfen beide laufen. Umkreis um einen Punkt → `nearby`, welcher Ort gemeint ist → `search_place`. **When to use**: Events — DE: „was ist diese Woche/am Wochenende in <Region> los", „Veranstaltungen in <Ort>". EN: „events in <Region> this weekend". Unterkünfte/Gastro — DE: „Hotels in <Ort>", „Restaurants in <Ort>". EN: „hotels/restaurants in <Ort>". Touren — DE: „touristische Radrouten/Radtouren in <Region>", „Radwanderwege/Wanderwege am <Ort>" → `type=tour`; jede Tour trägt dann `length_m`, `duration_min`, `ascent_m`/`descent_m`, `round_trip` und `activities` (wörtlich, als was der Datensatz sie führt: „Fahrrad", „E-Bike", „Wandern", „Kanu") sowie `trail_shape` (`round` | `line` | `ascent`, aus dem Verlauf gelesen — gilt vor `round_trip`) — DARAN auswählen, nicht am Namen. Bei mehreren die best-passende per `get_tourism_details` zeichnen, die anderen namentlich nennen und fragen, welche noch. **When NOT to use**: alle in OpenStreetMap erfassten Sehenswürdigkeiten → `expand_kg_pois`; Detail zu EINEM Treffer → `get_tourism_details`; Routing/ Abfahrten, auch „von A nach B mit dem Rad" → der Server MobilityMCP; Wetter/Tide → `get_current_weather`/`get_tide`. **Required args**: `region` ODER `query` (eines von beiden — Freitext-Region/ Name, z.B. region="Wangerland", query="Sielhafenmuseum"). Optional: `type` (genau eines von "event" | "poi" | "accommodation" (="hotel") | "gastro" (="restaurant") | "tour" — kuratierte Rad-/Wander-Routen, auch als "radtour"/"radroute"/"radwanderweg"/"wanderweg" akzeptiert; weggelassen = alle Typen; unbekannter Wert = alle. OHNE `type=tour` sind Touren praktisch nie unter den Treffern). Im Aktivitäts-Scope (type="poi"/"attraction"/"unternehmen"/…) sind Unterkünfte AUSGESCHLOSSEN, im Region- wie im Vicinity-Modus (ein Hotel ist keine Unternehmung); für Hotels explizit type="accommodation". `timeframe` (nur für type=event: "today" | "tomorrow" | "weekend" | "this_week" | "month" | ISO "YYYY-MM-DD" | Range "YYYY-MM-DD..YYYY-MM-DD"; ohne timeframe = kommende Events ab heute), `limit` (Default 8, clamped 1..=20), `family_only` (bool, Default false — nur die kuratierten Family-Kategorien, je mit der belegten `suitability`-Achse) und, nur zusammen damit, `indoor_only` (bool — davon nur die Schlechtwetter-tauglichen). `activity` (nur mit `type=tour`): wie eine Tour zurückgelegt wird — "wandern" | "fahrrad" | "kanu" + Synonyme ("wanderweg"/"e-bike"/ "paddeln"). Filtert serverseitig auf die GANZE Familie kuratierter Aktivitätswerte, also auch "Winterwandern"/"Nordic Walking"/ "Mountainbike" — 8 % der zu Fuß zurückgelegten Touren tragen keinen "Wandern"-Wert; weglassen oder unbekannt = alle. **Coord-Vicinity-Modus (Umkreis, POI-Anker)**: für „<Kategorie> am/bei <POI>" den POI ZUERST via `search_place` zu einer Coord auflösen, dann `lat`+`lon` setzen (beide nötig; + optional `radius_m`, Default 2000 m, clamped 100..=20000). `region`/`query` sind dann nur Pool-Hint, nicht mehr Pflicht; die Treffer sind auf den Umkreis gefiltert, nächster zuerst, je mit `distance_m` in Metern. NUR in diesem Modus kommt die OSM-Breite dazu, entdoppelt gegen die Kuration (kuratierter Treffer gewinnt): bei `type=gastro` die `amenity`-Gastronomie, im Aktivitäts-Scope (`type=poi`, „unternehmen") `tourism` ∈ attraction/museum/…, `historic` und `leisure` ∈ park/garden/… — AUSGESCHLOSSEN bleiben Unterkünfte (hotel/hostel) und Alltags-Versorgung (`shop`, Apotheke/Bank/Arzt), beides keine Ausflugsziele. **Typical chain**: `search_tourism(region=<R>, type=event)` → `get_tourism_details(id, query=<R>)` pro Treffer — der Regions-Hinweis ist nötig, sonst antwortet das Detail leer. **Multi-call**: ein Call pro Region/Typ. **Anti-Fab note**: Event-Namen, DATEN, PREISE, POI-Namen, Adressen kommen AUSSCHLIESSLICH aus `pois[]` / `events[]` dieses Aufrufs. `returned: 0` / `pois: []` / `events: []` heißt: für <Region> war nichts abrufbar — so wiedergeben. Datum eines Events ist tool-belegt (`events[].date` / `pois[].date`, ISO-8601 Europe/Berlin) ODER abwesend — NIE geschätzt. **DZT-Bündelung (zweite kuratierte Quelle, DE-weit)**: kuratierte Treffer AUSSERHALB Niedersachsens kommen aus der DZT-KG (Deutsche Zentrale für Tourismus); Dubletten werden de-dupliziert, in der NDS-Region gewinnt der Niedersachsen-Hub-Eintrag (regionale Tiefe). Bei `type=tour` bleibt sie AUSSEN VOR — sie führt keinen Touren-Inhaltstyp; kuratierte Touren gibt es nur für Niedersachsen, außerhalb ehrlich "keine Tour abrufbar". Returns {place, source:'niedersachsen-hub', type, returned, mode?, center?, radius_m?, pois:[{name,type,description?,address?,uri?,date?,price?,media?, coord?,opening_hours?,id?,distance_m?,source?,license?,hub_detail_url?, length_m?,duration_min?,ascent_m?,descent_m?,round_trip?,trail_shape?,activities?}], events:[{name,date,location,source?,license?}]}. Das Top-Level `source` bleibt shape-stabil; welche Quelle einen Treffer geliefert hat, sagt sein eigenes `source` ('osm' | 'niedersachsen-hub' | 'dzt'). `id` ist der Schlüssel für `get_tourism_details`; die Felder folgen schema.org. **`license` (additiv, PRO Treffer)**: die Lizenz, die der Datensatz selbst angibt — `{id?, url?, notice?, holder?}`: `id` = Lizenz-Kennung (z.B. 'CC-BY-SA-4.0', 'CC0-1.0', 'ODbL-1.0'), `url` = Lizenz-Text/Deed, `notice` = der WÖRTLICH wiederzugebende Lizenzverweis (DZT-`copyrightNotice`, § 3 der DZT-Nutzerbedingungen; bei OSM-Treffern die ODbL-Namensnennung), `holder` = der zu nennende Urheber (das `author`- bzw. `copyrightHolder`-Feld des Datensatzes). Gibt er nichts an, ist das Feld **abwesend** — nie eine geratene Vorgabe-Lizenz. Nennst du einen Treffer mit `license.holder`, nenne den Urheber mit. **`hub_detail_url` (additiv, PRO Treffer, nur bei `source: 'niedersachsen-hub'`)**: die Entitätsseite dieses Treffers beim Niedersachsen-Hub, dem Nutzer nennbar — NICHT die Betreiber-Website (`uri`). Jeder Treffer, dessen Art beim Hub eine Eintragsseite hat, trägt sie: POI (`p_…`), Tour (`t_…`), Veranstaltung (`e_…`), Unterkunft (`h_…`), Gastronomie (`g_…`), Gebiet (`r_…`). Ein Medien-Datensatz (`m_…`) hat keine und trägt keine; fehlt sie, keine konstruieren. Mit `family_only` trägt jeder Treffer zusätzlich `family`, `suitability` ({family, age_bands, pushchair, seal (Kinderferienland-Siegel), indoor_bad_weather}) und, wo die Quelle sie führt, `price_child`/`price_family` — alle feature-belegt: „kinderfreundlich" ist belegt, nicht geraten.

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• getNormA

  nu:legal Deutsches RechtA

  The text of one provision, by default as clean Markdown — about a tenth the size of the reader page for the same provision, with no navigation, no scripts and no boilerplate. `law` is the abbreviation as a citation writes it ('BGB', 'DSGVO', 'BDSG 2018', 'RVG'); `ref` is the bare number, with any letter suffix and no § or Art. ('622', '823', '3a', '83'). Aliases resolve, and CASE IS READ: 'LwG' is the federal Landwirtschaftsgesetz while 'LWG' is a Land statute (Bayern's Landeswahlgesetz, NRW's Landeswassergesetz), so write the abbreviation the way your citation writes it. A spelling that matches no law exactly still resolves case-insensitively, and a miss lists the other laws the abbreviation names under `other_laws`, each with a `law_key` you can call again with. POINT IN TIME: `as_of=YYYY-MM-DD` returns the version stored for that date. Read `version_coverage` on every answer — the version archive begins 2019-06-10, and a date before that answers `outside_coverage` with the law's amendment register attached. That is a limit of our archive and says nothing about whether the provision existed. Every answer carries `first_observed`, `valid_to`, `date_precision` and `amendment_note`. `first_observed` is the day we first saw the text, NOT the legal Inkrafttreten — do not compute a deadline from it without reading `date_precision` (day / week / launch; 'launch' means the date is a floor). TRUST: `fundstelle` is the gazette citation of the authentic text — the citation a court accepts. `authoritative_source` names what our copy is (a consolidated, non-official reading version) and where the binding text lives. Quote the provision from `markdown`; the reader page at `url` carries per-Absatz anchors (#abs-N) if you want to deep-link a single Absatz.

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• entity_profileA

  Ban FrA

  "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

  Ban FrA

  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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• release_calendar_marketsA

  Ban FrA

  JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns the SAME shape as a populated one — release_count:0, releases:[] — plus empty_reason:"no_releases_in_window" and a `hint` telling you to widen, so you never branch on the response shape and never have to guess whether zero means "nothing is scheduled" or "the lookup failed". Econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

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• zahlung_erfassenA

  Rechnungslotse: deutsche E-RechnungA

  Verbucht einen Geldeingang auf eine Rechnung. Ohne Betrag gilt der offene Rest, ohne Datum der heutige Tag. Deckt der Betrag den Rest, wird die Rechnung als bezahlt geführt. Eine Überzahlung wird abgewiesen – dafür gehört eine Gutschrift erstellt.

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• listNormVersionsA

  nu:legal Deutsches RechtA

  Every stored version of one provision, newest first, so you can find out which dates `getNorm(as_of=…)` can actually answer before you ask. Each entry carries `first_observed` (the day the text was first seen here — NOT the Inkrafttreten), `valid_to`, `date_precision` and the law-level `amendment_note`. `at_archive_floor: true` marks the version that was current when mirroring began: its date is a floor, not an amendment, and earlier amendments exist that are named in the law's Änderungsverlauf (linked as `amendment_history_url`) but whose text is not held. There is no diff tool: fetch two versions with `getNorm(as_of=…)` and diff them yourself — a diff we computed would hide which side of it came from a floor date.

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• resolve_entityA

  Ban FrA

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

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• company_factsA

  Ban FrA

  TYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the period matters: those answer "the most recent figures" and will happily hand back FY2025 when you asked about FY2019, and neither separates a discrete quarter from a year-to-date figure. This one refuses instead — it NEVER substitutes the latest period for the period requested, NEVER returns 0 for missing data, NEVER lets a 9-month YTD number answer a quarterly question, and NEVER converts a currency. Every answer carries the exact us-gaap concept it came from, what that concept MEASURES (NetIncomeLoss excludes non-controlling interests, ProfitLoss includes them — not synonyms), the accession number and a link to the filing on sec.gov, the restatement trail of any superseded figures, and a contract + derivation version to pin against. Fiscal periods are the FILER'S OWN, anchored on their fiscal-year end, so Walmart's year ending 2026-01-31 is FY2026 and Apple's ending 2025-09-27 is FY2025. Attributes in v1: revenue, net_income, cash. Every non-answer is a named status — `unavailable` (the filer did not report it for that period; the periods that DO exist are listed, without values), `unsupported` (outside what v1 covers — a non-us-gaap filer, an unknown attribute, a non-USD unit), `ambiguous` (the company name matched two filers equally well; both are named), `conflicting` (two filings the same day disagree; both are returned and neither is picked), `partial` (a value with no accession behind it). Source: SEC EDGAR XBRL companyconcept, one publisher read once — see `corroboration`. Same response is served at POST https://gateway.pipeworx.io/v1/facts for non-MCP callers.

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