"IRIS" matching MCP tools:

• formatCitationA

  Scholar SidekickA

  Format scholarly identifiers into a finished citation in a specific style. Use when the user wants a paste-ready citation string for a manuscript, slide, message, footnote, or in-line reference. Style defaults to vancouver if unspecified; ask the user before defaulting if any ambiguity exists (e.g. 'Harvard' and 'Chicago' have multiple variants — confirm which one). Supports five hand-tuned builtins (vancouver, ama, apa, ieee, cse) plus any of 10,000+ CSL style IDs (chicago-author-date, harvard-cite-them-right, modern-language-association, nature, bmj, the-lancet, etc.). Alias and dependent-style resolution apply, so 'harvard' resolves to 'harvard-cite-them-right' and the canonical ID is reported back as styleUsed. Output defaults to text; pass output=html for marked-up HTML or output=json for structured CSL items. Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: one of { text, html, items } depending on the output parameter, followed by a metadata block ({formatter: 'builtin' | 'csl', styleUsed, requestId, warnings?}) appended as a second text content item — surface this to the user when they care about reproducibility. Use resolveIdentifier instead when the user wants raw metadata to inspect or transform; use exportCitation when they want a downloadable bibliography file. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.

  ConnectorNo auth

• exportCitationA

  Scholar SidekickA

  Export scholarly identifiers to a bibliography file format ready to write to disk or paste into a reference manager. Use when the user wants a file (.bib, .ris, .nbib, .xml, .rdf, .csv) for Zotero, Mendeley, EndNote, RefWorks, BibTeX/LaTeX, Pandoc, or Excel. Format parameter is required: bib (BibTeX — LaTeX), ris (RIS — most widely supported by reference managers), csl (CSL JSON — Pandoc/Quarto), endnote-xml, endnote-refer, refworks, medline (NBIB — PubMed round-trips, clinical workflows), zotero-rdf, csv (spreadsheet-friendly), or txt (plain-text bibliography rendered with the optional style parameter — txt is the only format that uses style; the others have their own structured shape and ignore it). Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: { content: string, format: string } where content is the entire bibliography in the requested format as a single string — write it to a file (.bib/.ris/.nbib/etc.) or paste it directly into the target tool. Use formatCitation instead when the user wants in-line citation text (manuscript, slide); use resolveIdentifier when they want raw structured metadata. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.

  ConnectorNo auth

• ris_search_legislationA

  ris-austria-mcp-serverA

  Search Austrian consolidated law and English translations: federal law (scope: federal, the default), one Bundesland (scope: burgenland … wien), municipal law (municipality plus a state scope — selected norms in 6 Bundesländer), or English translations of selected federal laws (language: english, federal only, ~138 documents). One document is one § / Artikel / Anlage; fetch a whole law by filtering law_id. Searches apply the version in force today in Austria by default — set in_force_as_of for another date, include_all_versions: true for full version history, or an entered_force / left_force window for new-law and repeal tracking; the three version filters are mutually exclusive, and the applied date is echoed back in the result. query is full text (boolean UND/ODER/NICHT or AND/OR/NOT, trailing-only * wildcard); title matches title, short title, and abbreviation ("DSG"). For a specific citation like "§ 6 DSG", ris_lookup_citation resolves it deterministically instead. Consolidated text is informational, not legally binding — the authentic gazette artifact lives in ris_search_gazette.

  ConnectorNo auth

• ris_search_case_lawA

  ris-austria-mcp-serverA

  Search Austrian case law (Judikatur) in ONE court or tribunal per call — court is required: vfgh (Constitutional Court), vwgh (Supreme Administrative Court), justiz (ordinary courts incl. OGH, selected decisions), bvwg (federal administrative), lvwg (state administrative), dsk (data protection authority), normenliste (VwGH norm index), dok, pvak, gbk, verg, upts (party transparency), and the historical uvs, asylgh, ubas, umse, bks. Cross-court research is one call per court (calls are cheap); historical bodies are closed windows with successors — codes, windows, and Geschäftszahl examples: ris_list_reference topic courts. Filter by full-text query, cited norm ("DSG §1", "DSGVO Art32" — the highest-value filter), exact case_number (Geschäftszahl), decision date range, decision_type (headnote vs full text), decision_kind, or the court-conditional filters: issuing_body (dsk/dok/pvak/verg), court_name/legal_area/subject_area (justiz), state (lvwg/uvs), party (upts), commission/senate/discrimination_ground (gbk), subject_law (bks), collection_number (vfgh/vwgh/uvs). For a known Geschäftszahl or VfSlg/VwSlg cite, ris_lookup_citation resolves it directly.

  ConnectorNo auth

• ris_get_documentA

  ris-austria-mcp-serverA

  Fetch one RIS document’s full text or its rendition URLs, with explicit binding status and the amtssigniert authentic PDF surfaced wherever it exists. Address the document exactly one of two ways: document_number plus application (both copied verbatim from a ris_search_* or ris_lookup_citation result), or a document_url from a result’s content_urls — or, for a draft’s companion documents (Erläuterungen, Textgegenüberstellung, WFA, cover letter, annexes), a ris_search_drafts record’s materials[].url, which is the only route to them. format: markdown (default — the HTML rendition converted to markdown), html (raw HTML rendition), xml (the RIS Nutzdaten XML), or urls_only (no fetch — every rendition URL, including the Authentisch PDF). Format availability varies by application and the tool degrades explicitly, never silently: consolidated law, gazettes, case law, drafts, and most sectoral collections carry full text; district and municipal promulgations and court rules (Bvb, GrA, KmGer) publish only the signed authentic PDF; party-transparency decisions and council minutes (Upts, Mrp) are PDF-only; the 1848–1940 imperial gazettes (BgblAlt) are metadata-only — for these a text-format request returns a format_unavailable notice with the usable URL, not an error. Every result carries binding_status; only authentic (amtssigniert) publications are legally binding. This tool returns content, not fresh metadata — the metadata rides the search/lookup step that produced the document number. When the markdown text overflows the 40,000-byte budget the tool returns an outline (kind: outline) instead of truncating: the document’s §/Artikel/Anlage sections where it carries at least two such headings, otherwise contiguous byte windows named Part 1 of N … Part N of N covering the whole text and listed in document order. Re-call with sections:[…] naming outline entries to retrieve just those; a name matching no entry returns the outline again with a notice rather than the whole document. Windows are cut at line breaks, not at sentence or § boundaries, so one can open mid-sentence — read them in order and pull the neighbour when a passage straddles a cut. Raw html and xml renditions are never sliced: at or under the 40,000-byte budget they return whole; over it the result is kind: link — no text, truncated: true, the full byte_size, and content_urls, whose html or xml entry fetches the whole artifact in one GET. Every HTML rendition opens with a 40–70 KB stylesheet, so html practically always returns kind: link — read with markdown, parse with xml, and fetch content_urls.html for the authentic markup. Markdown drops the screen-reader expansions RIS ships alongside each abbreviated citation, keeping the visible citation form; raw html/xml renditions are returned exactly as published.

  ConnectorNo auth

• formatCitationA

  Scholar SidekickA

  Format scholarly identifiers into a finished citation in a specific style. Use when the user wants a paste-ready citation string for a manuscript, slide, message, footnote, or in-line reference. Style defaults to vancouver if unspecified; ask the user before defaulting if any ambiguity exists (e.g. 'Harvard' and 'Chicago' have multiple variants — confirm which one). Supports five hand-tuned builtins (vancouver, ama, apa, ieee, cse) plus any of 10,000+ CSL style IDs (chicago-author-date, harvard-cite-them-right, modern-language-association, nature, bmj, the-lancet, etc.). Alias and dependent-style resolution apply, so 'harvard' resolves to 'harvard-cite-them-right' and the canonical ID is reported back as styleUsed. Output defaults to text; pass output=html for marked-up HTML or output=json for structured CSL items. Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: one of { text, html, items } depending on the output parameter, followed by a metadata block ({formatter: 'builtin' | 'csl', styleUsed, requestId, warnings?}) appended as a second text content item — surface this to the user when they care about reproducibility. Use resolveIdentifier instead when the user wants raw metadata to inspect or transform; use exportCitation when they want a downloadable bibliography file. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.

  ConnectorNo auth

• exportCitationA

  Scholar SidekickA

  Export scholarly identifiers to a bibliography file format ready to write to disk or paste into a reference manager. Use when the user wants a file (.bib, .ris, .nbib, .xml, .rdf, .csv) for Zotero, Mendeley, EndNote, RefWorks, BibTeX/LaTeX, Pandoc, or Excel. Format parameter is required: bib (BibTeX — LaTeX), ris (RIS — most widely supported by reference managers), csl (CSL JSON — Pandoc/Quarto), endnote-xml, endnote-refer, refworks, medline (NBIB — PubMed round-trips, clinical workflows), zotero-rdf, csv (spreadsheet-friendly), or txt (plain-text bibliography rendered with the optional style parameter — txt is the only format that uses style; the others have their own structured shape and ignore it). Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: { content: string, format: string } where content is the entire bibliography in the requested format as a single string — write it to a file (.bib/.ris/.nbib/etc.) or paste it directly into the target tool. Use formatCitation instead when the user wants in-line citation text (manuscript, slide); use resolveIdentifier when they want raw structured metadata. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.

  ConnectorNo auth

• zone_compareA

  TrustyDataA

  Compare de 2 à 10 zones de chalandise en un seul appel et mesure leur cannibalisation. Pour chaque zone, rend exactement ce que `zone_stats` rend (`population`, `menages`, `niveau_de_vie_moyen`, `ages`, `sexe`, `menages_profil`, `logements`, `csp`, `potentiel_depense`, `immobilier`, `activites`, `carreaux`, `source`, `attribution` — voir cet outil pour le sens de chaque bloc), plus `id` et `exclusif` : la population, les ménages et le potentiel que la zone est SEULE à couvrir, calculés comme « la zone moins l'union de toutes les autres » — jamais « total moins les paires », qui à trois zones et plus retrancherait deux fois un triple recouvrement ; `exclusif.part_population` en pour cent de la population de la zone. Entre zones : `chevauchements`, une entrée par paire qui se recouvre (population, ménages et potentiel comptés deux fois ; une paire disjointe n'apparaît pas), et `classement`, tous les identifiants triés sur `rank_by` décroissant : `population` (défaut), `exclusive_population`, `theoretical_potential` ou `exclusive_potential` — ces deux derniers exigent `coefficient`. Chaque élément de `zones` se décrit comme sur `zone_stats` : un centre (`address`, un nom de commune suffit, OU `lat`+`lon` WGS84, jamais les deux) et UNE portée, soit `minutes` (1 à 30) avec `mode` `car` (défaut), `walking` ou `cycling` et, en voiture, `traffic` `free` (défaut) ou `peak` (heure de pointe du matin, vitesses modélisées, pas de trafic temps réel — refusé à pied et à vélo, et pouvant répondre « indisponible » si la plateforme ne l'a pas encore ouvert), soit `radius_km` (1 à 50) à vol d'oiseau ; plus un `id` optionnel (1 à 40 caractères, lettres, chiffres, `_`, `-`, unique dans l'appel) repris dans la réponse, le classement et les erreurs — sans `id`, la position à partir de 1. `reference` : `france` (défaut) ou `department` — UN réglage pour DEUX blocs : l'`indice` de chaque poste CSP et celui de chaque poste d'`activites` sont alors rapportés au département qui pèse le plus dans la zone (`csp.reference.code`, `activites.reference.code`, et leurs libellés respectifs `csp.reference.libelle` / `activites.reference.libelle`, `null` en référence France). `coefficient` (0 exclu à 1) : la part du revenu disponible que le secteur capte, appliquée à toutes les zones, à leurs exclusifs et aux paires ; il déclenche `potentiel_theorique` = revenu disponible estimé × coefficient — une estimation, un potentiel théorique, pas une prévision de chiffre d'affaires ; sans coefficient il vaut `null`, une valeur qui ne s'invente pas. Le guide « Zone de chalandise » donne des repères par fonction de consommation (restauration ≈ 0,048). La CSP est elle aussi une estimation : aucun indice n'est exploitable sur une zone dont `csp.fiabilite.indices_fiables` est faux. Comme sur `zone_stats`, un poste dont la part est inférieure à `csp.fiabilite.part_min` (0,5 %) rend `indice: null` — un refus de servir, pas une donnée manquante ; `effectif` et `part` restent valides — ne jamais reconstituer l'indice à partir de leur rapport avec `reference_part` non plus. `reference_part` donne la part du même poste sur la référence : citer les deux parts côte à côte plutôt qu'un écart — un indice de 82 est un écart de 18 %, pas de 18 points. Le bloc `activites` est rendu pour chaque zone, identique à celui de `zone_stats` : c'est un PLANCHER d'établissements actifs et géolocalisés — ne jamais le présenter comme exhaustif, ni appeler ces établissements des concurrents ; d'une zone à l'autre, comparer les `indice` plutôt que les effectifs, le sous-compte s'y annulant en grande partie. `exclusif` et `chevauchements` ne portent PAS d'établissements : population, ménages et potentiel seulement. Une zone inatteignable (isochrone sans contour) rend des zéros, n'entre dans aucune paire et se classe dernière ; plafond de surface 10 000 km² par zone. Plan minimum : Growth ; un appel compté quel que soit le nombre de zones ; dix isochrones prennent une à deux secondes. `attribution` accompagne tout chiffre cité (INSEE Filosofi et Recensement de la population ; IGN Contours IRIS ; SIRENE (INSEE) pour les établissements du bloc `activites` ; DGFiP DVF pour les prix immobiliers ; OpenStreetMap ODbL dès qu'une zone est une isochrone).

  ConnectorNo auth

• polymarket_arbitrageA

  Who IrisA

  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.

  ConnectorNo auth

• resolution_auditA

  Who IrisA

  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.

  ConnectorNo auth

• populationA

  france-data-mcpA

  Population d'une COMMUNE (code INSEE 5 car.), d'un DÉPARTEMENT (2-3 car.) OU d'un IRIS infracommunal (9 car.) — granularité auto-détectée par la longueur du `code`. Retourne un `LookupResult` discriminé par `found`. - IRIS (9 car., ex `751103701` = commune `75110` + IRIS `3701`) : population totale du quartier au Recensement 2022 (champ `population`, comptes bruts), + `libelle`, `code_commune`, `type_iris` (H/A/D/Z). Source : INSEE RP 2022 (table ingérée, géo 01/01/2024). Maille la plus fine (quartier) pour les villes ; en zone peu dense la commune = 1 IRIS (`type_iris` Z, code `COM+0000`). Pour le profil démographique détaillé d'un îlot ou d'un bassin (âge, CSP, familles, revenu), utiliser `profil_iris`. - Commune (5 car., ex `75056` Paris, `13055` Marseille, `2A004` Ajaccio) : PMUN/PCAP/PTOT. Source INSEE Melodi (DS_POPULATIONS_REFERENCE). PMUN = base légale DREES. Commune fusionnée → `found: false` + orientation `autocomplete_commune`. INSEE n'expose PAS les arrondissements PLM (75101-75120, 13201-13216, 69381-69389) → passer la commune-mère ou le département. - Département (2-3 car., ex `75`, `59`, `2A`, `971`) : Mayotte (`976`) ABSENTE de Melodi → `lookupNotFound`. Alias acceptés : `code_insee`/`codeInsee`/`insee`, `code_dept`/`dept`/`departement`/`code_departement`, `code_iris`/`iris` → `code`.

  ConnectorNo auth

• panorama_sante_territoireA

  france-data-mcpA

  Panorama santé d'une commune française en 1 appel (V0.9). Agrège en parallèle : population (INSEE Melodi), densités médecins + infirmiers + pharmaciens avec comparaison nationale (méthodo DREES), nombre d'établissements FINESS par famille (default ["labo","pharmacie","ehpad","mco","msp_cpts"]), et un bloc DEMANDE (V0.22.0 — profil démographique de la commune agrégé depuis ses IRIS : âge, CSP, familles, revenu pondéré, à CROISER avec l'OFFRE ci-dessus pour l'aide à l'implantation ; `demande: null` si commune hors couverture IRIS (DOM non ingéré) — pour le détail au quartier ou un bassin par rayon, utiliser `profil_iris`). Remplace 7-10 appels MCP individuels par 1 seul. Ne renvoie AUCUNE interprétation métier (pas de qualification automatique 'désert médical') — le caller LLM applique sa grille. V0.19.0 : accepte `nom_commune` (string) comme alternative à `code_insee`. `departement` (V0.19) = hint resolver UNIQUEMENT (panorama ne calcule pas par dept ; un `departement` seul lève une erreur explicite). **Granularité mixte** : les densités professionnels et la population sont calculées au niveau **commune** ; le décompte FINESS est agrégé au niveau **département** dérivé du code INSEE (limitation V0.9 — pas de RPC count_finess_by_commune encore). Le champ `niveauEtablissements` du résultat indique `"departement"` (succès), `"indisponible"` (dept indérivable, ex code DOM tronqué) — utiliser cette information pour ne pas confondre ratios commune et dept. Paris/Marseille/Lyon NON supporté : le panorama par commune dépend de la densité par commune, indisponible pour ces villes (INSEE n'expose la population qu'à la commune entière, les praticiens RPPS aux arrondissements). Un code PLM (commune-mère 75056 ou arrondissement) lève une RangeError. Pour ces villes, interroger les tools individuels au niveau `code_dept` (75/69/13). Alias acceptés : `codeInsee`/`insee`/`code` → `code_insee`. Sources : RPPS / Annuaire Santé ANS (mensuel), FINESS DREES (bimensuel), INSEE Melodi (PMUN 2023).

  ConnectorNo auth

• resolveIdentifierA

  Scholar SidekickA

  Resolve scholarly identifiers to structured CSL JSON metadata (title, authors, journal, year, identifiers). Use when the user wants raw bibliographic data to inspect, transform, or feed into another tool — not a formatted citation. Common single-shot conversions: PMID → PMCID, arXiv → DOI, ISBN → CSL JSON, WHO IRIS URL → structured metadata. Accepts DOI, PMID, PMCID, ISBN, arXiv ID, ISSN, NASA ADS bibcode, or WHO IRIS URL, with or without prefixes (PMID:, arXiv:, ISBN hyphens, https://doi.org/...). Pass a single identifier or a comma/newline-separated batch — one round trip per call. Returns: a JSON array of CSL items, each with id, type, title, author[], issued.date-parts, container-title, DOI/PMID/PMCID/ISBN/ISSN/URL when available. Use formatCitation instead when the user wants a finished citation string in a specific style; use exportCitation when they want a downloadable bibliography file. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier; the underlying REST API caches repeated identical requests and surfaces cache state in the x-scholar-cache response header.

  ConnectorNo auth

• ris_lookup_citationA

  ris-austria-mcp-serverA

  Resolve a single Austrian legal citation to its canonical RIS document deterministically — no keyword search. Four routes are auto-detected from the citation shape (or forced with kind): a norm citation — section-first ("§ 6 DSG", "Art 10 B-VG"), abbreviation-first ("DSG §1", "DSGVO Art32", the shape ris_search_case_law returns in norms_cited), or a bare abbreviation like "ABGB" — resolves through consolidated federal law, or a Bundesland with a state hint, as in force today or on in_force_as_of; a gazette citation ("BGBl. I Nr. 165/1999", pre-2004 "BGBl. Nr. 194/1961", imperial "RGBl. Nr. 189/1902", or "LGBl. Nr. 61/2026" with a state hint) routes to the right federal era tier by year, or to a state Landesgesetzblatt — falling back to that Bundesland’s pre-e-Recht series when the citation predates its switch; a case number ("Ro 2026/03/0016", "G 287/2022", "14Os49/26a", "2025-0.934.677", "W256 …") is matched to its court — pass court to skip detection, and ambiguous formats probe up to two courts; a collection number ("VfSlg 19.632/2012", "VwSlg 18.000 A/2010") resolves through the VfGH/VwGH collection — a VwSlg cite given without its part letter can name one decision in each of the two VwGH series, and comes back as ambiguous with both cites named rather than resolved to one of them. Returns the single best-matching document in the same shape as the corresponding search tool, with alternatives_count when more than one matched. A citation that cannot be classified or resolved returns found: false with next-step guidance — it never throws for a miss; only an upstream RIS outage is an error. For keyword rather than citation lookup, use ris_search_legislation or ris_search_case_law.

  ConnectorNo auth

• resolveIdentifierA

  Scholar SidekickA

  Resolve scholarly identifiers to structured CSL JSON metadata (title, authors, journal, year, identifiers). Use when the user wants raw bibliographic data to inspect, transform, or feed into another tool — not a formatted citation. Common single-shot conversions: PMID → PMCID, arXiv → DOI, ISBN → CSL JSON, WHO IRIS URL → structured metadata. Accepts DOI, PMID, PMCID, ISBN, arXiv ID, ISSN, NASA ADS bibcode, or WHO IRIS URL, with or without prefixes (PMID:, arXiv:, ISBN hyphens, https://doi.org/...). Pass a single identifier or a comma/newline-separated batch — one round trip per call. Returns: a JSON array of CSL items, each with id, type, title, author[], issued.date-parts, container-title, DOI/PMID/PMCID/ISBN/ISSN/URL when available. Use formatCitation instead when the user wants a finished citation string in a specific style; use exportCitation when they want a downloadable bibliography file. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier; the underlying REST API caches repeated identical requests and surfaces cache state in the x-scholar-cache response header.

  ConnectorNo auth

• polymarket_fill_riskA

  Who IrisA

  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.

  ConnectorNo auth

• kalshi_weather_edgeA

  Who IrisA

  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").

  ConnectorNo auth

• release_calendar_marketsA

  Who IrisA

  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 error:"no_releases_in_window" with a widen-the-window hint rather than an empty array — econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

  ConnectorNo auth

• pipeworx_feedbackA

  Who IrisA

  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

• profil_irisA

  france-data-mcpA

  Profil démographique au grain QUARTIER (IRIS) — la « demande » d'un territoire (âge, CSP, familles, revenu), à croiser avec l'offre de soins pour l'aide à l'implantation. Source : INSEE RP 2022 + FILOSOFI 2021 (tables ingérées, géo 01/01/2024). Retourne un `LookupResult` discriminé par `found`. Entrée : EXACTEMENT un de `point` (`lat`+`lon`) OU `code_iris` (9 car.). `rayon_km` optionnel (0 < r ≤ 10) → DEUX modes : - SANS `rayon_km` → profil de l'ÎLOT seul (~2000 hab) sous le point / du code. `mode: "ilot"`, `revenu_median` = médiane réelle de l'îlot. - AVEC `rayon_km` → AGRÉGAT du BASSIN = îlots dont le CENTROÏDE est dans le disque (chaque îlot compté 1 fois). `mode: "bassin"`, `population_bassin`, `nb_iris_agreges`, et `revenu_median_pondere` = PROXY (moyenne pondérée population des médianes des îlots couverts — PAS une vraie médiane de bassin) + `couverture` {`revenu_pct_population`, `iris_revenu_manquants`} car FILOSOFI ne couvre que les communes ≥5000 hab. Les parts `age` (part_65_plus/75_plus) et `csp` (cadres, prof_interm, employés, ouvriers, agriculteurs, artisans_comm, retraités, autres) sont des ratios sur comptes bruts (Σ/Σ). Pour une simple population de commune/dept, utiliser `population`. `not_found` motivé si code absent ou point hors métropole / en mer.

  ConnectorNo auth

• data_freshnessA

  france-data-mcpA

  Retourne la fraîcheur des dumps de données ingérés côté serveur : FINESS / ANS (flux quotidien, ingéré le 1er et le 15 du mois), Annuaire Santé Ameli (hebdomadaire), RPPS / Annuaire Santé ANS (mensuel), Centres de Santé CNAM (hebdomadaire), IRIS INSEE (annuel), permis de construire Sit@del / SDES (mensuel, cron le 10). Pour chaque source : `last_data_change_at` + `data_age_days` (dernier run ayant RÉELLEMENT changé la donnée servie, et son âge en jours — C'EST LE CHAMP À LIRE), `last_success_at` + `staleness_days` (dernier run réussi, y compris un run court-circuité « fichier amont identique » — ne mesure PAS l'âge de la donnée), `last_success_row_count`, `last_attempt_at`, `last_attempt_status`, `cadence_hint` (cadence attendue). Usage typique : avant un audit territorial ou une analyse temporelle, le caller appelle ce tool pour savoir si les données sont à jour. Juger sur `data_age_days`, JAMAIS sur `staleness_days` seul : en 2026 la source FINESS s'est tarie 4 mois pendant que `staleness_days` restait à quelques jours (runs « fichier identique » comptés comme succès). Règle d'alerte : `data_age_days > expected_max_age_days` (seuil par source, exposé dans chaque ligne — ne pas le recopier) ; `data_age_days: null` = jamais ingéré. Les sources LIVE (DINUM Recherche Entreprises, INSEE SIRENE V3.11, ANS FHIR live) ne sont PAS listées ici puisqu'elles n'ont pas de cycle d'ingestion — leur fraîcheur est celle des API amont (live, ~secondes). Cache serveur : 5 minutes. Coût : 1 SELECT sur `ingest_log` au pire (sinon hit cache).

  ConnectorNo auth