"Spyder IDE" matching MCP tools:

• professionnels_in_radius

  france-data-mcp

  Recherche de professionnels de santé libéraux conventionnés dans un rayon géographique. Précision géo HYBRIDE depuis le géocodage BAN (Chantier C) : ~77 % des PS sont géolocalisés à l'adresse précise (rue/bâtiment, `distance_km` exacte au m près), ~23 % restent au centroïde commune (~3 km, repli pour adresses non géocodables — DROM, Monaco, CEDEX, lieux-dits). Lire `geo_precision` PAR résultat — ne pas présumer une précision uniforme. Codes type_ps Ameli présents en base (3) : '1' médecins, '2' auxiliaires médicaux (fourre-tout : IDE, kinés, sages-femmes, podologues, orthophonistes, orthoptistes, IPA), '5' chirurgiens-dentistes. Pour cibler une profession précise (ex: IDE seuls, kinés seuls, podologues seuls), passer par `specialite_codes` plutôt que `type_ps_codes` qui ratisse plus large. Liste exhaustive des codes spécialité disponibles via le tool `lister_nomenclature(referentiel:'ameli_specialites')`. Multi-sites : par défaut un PS exerçant sur N adresses apparaît N fois — utiliser `dedupe_by_ps=true` pour regrouper par praticien et lister les sites en sous-objet. Distance retournée en km vol d'oiseau (haversine PostGIS) — pour distance routière, croiser avec un service externe (OSRM, ORS). Chaque PS géolocalisé porte `geo_precision` ∈ {`"adresse"`, `"centroide_commune"`} : `"adresse"` = coords BAN précises, `distance_km` exacte, classement individuel fiable ; `"centroide_commune"` = ~3 km, `distance_km` IDENTIQUE pour tous les PS d'une même commune (non discriminante intra-commune — filtre de zone uniquement, pas de classement/choix d'un PS individuel). **Param `precise_only`** (défaut false) : à true, exclut les PS au centroïde commune et ne renvoie que les ~77 % géocodés à l'adresse BAN (`distance_km` exacte) — recommandé pour les rayons courts (<3 km) et le classement intra-commune. PÉRIMÈTRE : libéraux conventionnés UNIQUEMENT. HORS PÉRIMÈTRE : médecins exclusivement hospitaliers/salariés, biologistes médicaux salariés en LBM, anatomopathologistes hospitaliers, médecins du travail, médecine légale. Pour effectifs tous statuts, voir Annuaire Santé ANS (RPPS, esante.gouv.fr) — non couvert par ce serveur. Source : Annuaire santé Ameli (Assurance Maladie), MAJ hebdomadaire. Réutilisation soumise à l'art. L.1461-2 CSP — citer la source et la date de sync.

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• get_series

  Bundesbank De

  Pull observations for a Bundesbank series as SDMX-JSON. flowRef is the dataflow id (e.g. "BBEX3"); key is a dot-separated SDMX dimension filter in key order (e.g. "D.USD.EUR.BB.AC.000" = daily USD/EUR reference rate). Use dataflow_structure to discover the dimensions/codes for a flow. Leave a dimension empty to wildcard it (e.g. "D..EUR.BB.AC.000"). Filter by lastNObservations (most recent N) or a startPeriod/endPeriod date range (YYYY, YYYY-MM, or YYYY-MM-DD).

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• validate_claim

  Datagov Ie

  Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources. Use when an agent needs to check whether something a user said is true ("Is it true that…?", "Was X really…?", "Verify the claim that…", "Validate this statement…"). v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).

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• polymarket_arbitrage

  Bundesbank De

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. TWO MODES: (1) `event` — pass a single Polymarket event slug; 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). (2) `topic` — pass a seed question ("Strait of Hormuz traffic returns to normal"); 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}.

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• remember

  Cso Ie

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• entity_profile

  Cso Ie

  Get everything about a US public company in one call. Use when a user asks "tell me about X", "research Acme", "brief me on Tesla", or you'd otherwise call 10+ pack tools across SEC EDGAR, XBRL, USPTO, news, GLEIF. Returns: cik + company_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 — Run 6 fix landed real FY2025 numbers, not stale FY2022); patents (USPTO PatentsView API was sunset May 2025; pack soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first).

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• recent_changes

  Cso Ie

  What's new with a company in the last N days/months? Use for "what's happening with X", "updates on Y", "news on Apple this month", or change-monitoring. Fans out in parallel to: SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• remember

  Bundesbank De

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• ask_pipeworx

  Datagov Ie

  PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 3,350 tools across 751 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1".

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• pipeworx_feedback

  Ine Es

  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). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. 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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• chieflab_create_next_move_action

  ChiefLab

  P75 — turn a Next Move suggestion into an approval-gated draft action. USE WHEN you've called chieflab_suggest_next_move and the suggestion's kind is not 'wait' or 'noop'. Creates an actionStore entry with status='awaiting_approval', the suggested draft body inline, and an executionMatrix that points at the right next-execution path. The reviewer sees the new card in the Launch Room / IDE chat like any other approval card — same approve / revise / reject flow. Closes the loop: launch → measure → next move → approve → execute → repeat.

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• scan_dependency

  Cso Ie

  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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• entity_profile

  Bundesbank De

  Get everything about a US public company in one call. Use when a user asks "tell me about X", "research Acme", "brief me on Tesla", or you'd otherwise call 10+ pack tools across SEC EDGAR, XBRL, USPTO, news, GLEIF. Returns: cik + company_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 — Run 6 fix landed real FY2025 numbers, not stale FY2022); patents (USPTO PatentsView API was sunset May 2025; pack soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first).

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• pipeworx_feedback

  Bundestag De

  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). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. 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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• bet_research

  Bundestag De

  Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note.

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• query_dataset

  Cso Ie

  Read a FILTERED slice of a CSO table via JSON-RPC. Pass a map of dimension code -> array of category index values to keep (get the dimension codes and category index values from dataset_metadata). Returns JSON-stat 2.0 covering only the selected cells — far smaller than get_dataset.

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• recent_changes

  Datagov Ie

  What's new with a company in the last N days/months? Use for "what's happening with X", "updates on Y", "news on Apple this month", or change-monitoring. Fans out in parallel to: SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• resolve_entity

  Ine Es

  Resolve a user-spoken name to the canonical/official identifiers other tools require as input. Use FIRST when you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

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• scan_dependency

  Bundestag De

  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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• rpps_search_by_name

  france-data-mcp

  Trouve un PS par identité (matching trigram tolérant aux accents/typos). Usage : "Dr Martin à Paris" → `nom: "Martin", departement: "75"`. Nom obligatoire ; `prenom` et `departement` affinent. Tri par `match_score` ∈ [0..1] décroissant (score trigram pg_trgm). Un score <0.5 = homonymie partielle à confirmer côté caller. Sans `departement`, des homonymes exacts ("Pierre Martin") ont TOUS le même score ~1.0 et ne sont pas départagés — toujours filtrer par dept ou prénom sur un nom commun. `truncated: true` = d'autres résultats existent (restreindre, ne pas parcourir). Chaque résultat géolocalisé porte `geo_precision` ∈ {`"adresse"`, `"etablissement_finess"`, `"centroide_commune"`} — lire ce champ pour évaluer la fiabilité des `coords` (précise BAN/FINESS au m près vs centroïde commune ~3 km, non discriminant intra-commune). Catégorie par défaut : Civil (C, ~97 % — libéraux, salariés privés, hospitaliers contractuels). Opt-in : `include_agents_publics: true` ajoute Agents publics (M, ~0,3 % — PH titulaires, ARS, CNAM, Éducation nationale, PMI, militaires SSA) ; `include_etudiants: true` ajoute Étudiants (E, ~2,5 % — internes, externes, élèves IDE/SF). Réf : https://mos.esante.gouv.fr/NOS/TRE_R09-CategorieProfessionnelle/. Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

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