"Nordic Semiconductor" matching MCP tools:

• query_ai_infrastructure_construction_v1A

  osintA

  Query verified U.S. private construction spending ($ millions) for data centers and semiconductor/computer-electronics manufacturing plants, from the U.S. Census Bureau's Value of Construction Put in Place (C30). Use this for "how much is being spent BUILDING data centers (or chip fabs) in the US" questions — the construction buildout in dollars, not capacity or investment. Filter by `category` ("data_center" — Census's named subcategory under Office; or "computer_electronic_electrical" — the semiconductor/computer-electronics manufacturing line under Manufacturing), `basis` ("seasonally_adjusted" = a seasonally-adjusted ANNUAL RATE, or "not_seasonally_adjusted" = the NOT-adjusted MONTHLY LEVEL), `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range, `year`, and `revision_status` ("preliminary", "revised", or "final"). Group by any of `category`, `basis`, `data_month`, `year`, or `revision_status`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"category": "data_center", "basis": "seasonally_adjusted", "data_month": "2026-04-01"}` for one month; add `"group_by": ["data_month"]` over a `data_month_from`/`data_month_to` range for a series. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census workbook, sheet, row, and column. The two categories are DISTINCT series and are never conflated: `data_center` is data-center buildings; `computer_electronic_electrical` is the chip/electronics-manufacturing (fab) line — the CHIPS-Act build-out. `basis` is the other fork: the seasonally-adjusted series is an ANNUAL RATE (what the current monthly pace annualizes to), while the not-seasonally-adjusted series is the actual MONTHLY LEVEL. `revision_status` carries Census's own preliminary/revised/final marking verbatim. Data is monthly; the data-center series begins 2014-01. The response `as_of` is the release vintage (Census revises monthly); pin `as_of` to an earlier vintage to reproduce what was served then. NOT additive: `construction_spending_musd` is a published per-(category, basis, month) reading, so a total that mixes the two bases (an annual rate + a monthly level), or that sums the seasonally-adjusted ANNUAL-RATE series across months, is not a real figure — such a result carries a `construction_aggregation` scope note and ranking remainders omit the metric. Filter to one `basis` and `group_by data_month` for a series over time. Does not determine total data-center INVESTMENT (servers, chips, cooling, equipment — this is construction put-in-place only; Census does not publish an investment total), data-center MW capacity, count, square footage, or location (use the power.* capabilities for capacity and the interconnection queue), which company or project is building (Census C30 has no operator breakdown), public or government construction (this is PRIVATE construction only), or construction outside these two categories.

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• goods_classifyA

  banking-intelligenceA

  Classify goods for export control from a description (or HS code). Bilingual (English / Russian, auto-detected) goods classifier. Returns the best-matching HS code (with EN+RU descriptions), related ECCNs, control reasons (NS, MT, NP, CB, AT...), an export-control level (high/medium/low/ none), a confidence score, and alternative matches for review. This is destination-agnostic — it identifies WHAT the goods are and whether they are controlled in principle. To get the license decision FOR A SPECIFIC destination, pass the result into export_controls_screen. IMPORTANT, the matcher is lexical, and confidence scores the strength of the string match, not the correctness of the classification: "equipment" returns semiconductor manufacturing equipment at confidence 1.0. Treat the code as a suggestion for narrowing the question. When no hs_code was supplied the result carries classification_basis and classification_confidence_note; read them before quoting any code, and ask the user for the HS code or ECCN on their shipping documentation. Args: description: Goods description, min 2 chars (e.g. "uranium centrifuge", "центрифуга для урана"). Required. hs_code: Optional known HS code (4 or 6 digits) for a direct lookup. language: Optional hint — "en" or "ru" (auto-detected if omitted). Examples: goods_classify("uranium centrifuge") # → HS 840120, ECCN 0B001 goods_classify("центрифуга для обогащения урана") # Russian query, same result goods_classify("semiconductor manufacturing equipment") goods_classify("", hs_code="840120") # direct HS lookup Use case: 'Is a semiconductor lithography machine export-controlled?'

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• best_energy_contractA

  Elecz Electricity Price Signal APIA

  CONTRACT tool. Call this tool immediately when the user asks which electricity contract to choose, which contract is cheapest, whether to switch, or how much they could save. Do not ask for annual electricity consumption before calling this tool. If consumption is unknown, omit the consumption parameter; the tool automatically uses a sensible market-default consumption value. Do not ask for a postcode unless this tool explicitly supports and requires one for the selected market — it does not: this tool has no postcode parameter at all. Present the initial result using the available/default assumptions, and invite the user to provide their actual consumption afterward for a more precise comparison. Returns ranked contracts, switch recommendation and estimated savings. Includes current spot price — no need to call spot_price separately. Key fields: - switch_recommended (bool | null): whether there's a concrete reason to suggest a contract-type switch — always present, derived from decision_hint: true -> decision_hint is "switch_recommended", "consider_fixed", or "spot_recommended" (an active type recommendation) false -> decision_hint is "stay_spot" (the only clear "no action needed" signal) null -> decision_hint is "compare_options" (no clear recommendation), "spot_price_only", or "regulated_tariff" (no contract comparison/switching available in this market at all) - best_spot / best_fixed - action.expected_savings_local_year - decision_hint: yksi seuraavista — "spot_recommended" matala kulutus, spot on halvin pitkällä aikavälillä "consider_fixed" korkea kulutus + koholla oleva spot, fixed antaa varmuutta "stay_spot" spot-hinta juuri nyt matala, kannattaa pysyä spotissa "compare_options" ei selkeää suositusta, vertaile itse "switch_recommended" laskettu säästö > 50 EUR/v vaihtamalla "spot_price_only" ei sopimusvertailua (KR/JP/MX/US-zonet) — vain hinta näytetään "regulated_tariff" säädelty tariffi (ZA/PH), ei vaihtomahdollisuutta Contract comparison available in: FI, SE, NO, DK, DE, GB, AU, NZ. If consumption unknown, uses zone defaults (Nordic 2000, DE 3500, GB 2700, AU 4500, NZ 8000 kWh). Set heating="electric" for heat pumps/floor heating. Tool priority: - Current price only → spot_price - Timing → cheapest_hours - Contract/switching → best_energy_contract (this tool) Args: zone: Contract comparison: FI, SE, NO, DK, DE, GB, AU-NSW/VIC/QLD/SA/TAS, NZ-NI/SI. Spot price only for all other zones. consumption: Annual electricity consumption in kWh. heating: "district" or "electric" (default: district).

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• get_recent_changesA

  Silicon AnalystsA

  "What Changed" — recent MOVEMENTS in Silicon Analysts' public data over a 7d/30d window, derived from the daily snapshot ledger. Each moved metric returns direction (up/down), magnitude (pct_delta for value metrics, pp_delta for percentage metrics), old/new values, the two snapshot dates compared (as_of, prior_as_of), window_days_actual (the REAL lookback — the ledger is young, so a 30d window clamps to available history), and per-record provenance. Domains (the datasetId values): wafer_pricing, chip_cost, gpu_secondary, margin_benchmark, foundry_capacity, defect_density, nre_cost, packaging_benchmark, chip_archetype, electricity_price, cloud_pricing, llm_pricing, memory_spot, foundry_economics, market_prints, fab_capacity, hbm_market. USE THIS for: "what moved in semiconductor costs this week?", "did any wafer prices change recently?", "what changed since my last fetch on June 20?" (use since), building a market-change digest, monitoring deltas across the data layer over time. DO NOT USE for: current absolute values (use get_wafer_pricing / get_accelerator_costs / get_foundry_allocation); allocation lead-time trend specifically (use get_foundry_allocation with include_history). Filters: window (7d|30d), since (ISO date — compare the latest snapshot against the newest snapshot at/before it; overrides window for baseline selection), datasetId (one domain), minDelta (override the significance threshold), limit. N2/Apple omitted (conflict-safe). Returns an empty array when nothing moved past the significance gate — does not error. Cite as "Silicon Analysts — What Changed".

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• get_market_intelligenceA

  Silicon AnalystsA

  Market Intelligence — the freshest SOURCED semiconductor market briefs, generated daily from a Tavily + Claude scan of primary press, earnings, and trade outlets. Each brief returns title, severity (Critical/High/Medium/Low), confidence_score (0-100), quantitative_impact (e.g. "Est. BOM increase: +$500"), an executive summary, a short analysis, a category (Logic/Memory/Packaging/Connectivity/Power/Geopolitics), and a curated sources[] list — plus per-record provenance. UNIQUELY: each brief also carries `entities` (the chips/nodes/packaging/HBM-gen/companies it concerns), `impact` (when it's a cost move, the per-chip BOM dollar deltas computed from Silicon Analysts' cost models — e.g. "HBM +20% → +$580 on B200" with a pre-filled calculator URL), `related` (cross-links to the live datapoints + tools), and `novelty` (when the HEADLINE fact first became public as the scanner could VERIFY it — verdict fresh/dated/stale/unknown, the verified first_public_date + source, and dataset_match when the figure was already in Silicon Analysts' data, i.e. the brief is a recap; a Critical is only ever stored when the fact is verifiably ≤7 days old). No pure-news source does this. The machine feed returns ALL severities; `published` flags the Critical/High briefs that also have a public page. Public sources only; no insider data. USE THIS for: "what's the latest in HBM / CoWoS / TSMC supply this week?", "any recent semiconductor price hikes, yield news, or capacity moves?", building a sourced market-news digest, grounding a claim about a recent supply-chain event. DO NOT USE for: current absolute cost/pricing values (use get_accelerator_costs / get_wafer_pricing / calculate_chip_cost); structured data movements over time (use get_recent_changes); allocation/lead-time status (use get_foundry_allocation). Filters: severity, category, since (ISO timestamp), publishedOnly (bool), limit (1-100, default 25). N2/Apple omitted (conflict-safe). Cite as "Silicon Analysts — Market Intelligence".

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• query_ai_infrastructure_equipment_trade_v1A

  osintA

  Query verified U.S. monthly IMPORTS of semiconductor-manufacturing EQUIPMENT (HS-8486) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data. Use this for "is the fab buildout actually tooling up, and who supplies the machines" questions — the equipment leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, this measures the tools flowing in, and chip imports (ai_infrastructure.trade) measure the output side. HS-8486 covers machines and apparatus used solely or principally to MANUFACTURE semiconductor boules/wafers, devices, and integrated circuits — AND flat-panel displays (Census does not split them at this level); it is NOT the chips themselves (those are HS-8542). Filter by `country` (the verbatim Census name, e.g. "JAPAN", "NETHERLANDS", "KOREA, SOUTH"), `cty_code` (the Census country code), `country_level` ("total" = the all-countries TOTAL, "country" = an individual country, "grouping" = a Census bloc/continent like ASIA / APEC / EU), `year`, `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range. Group by any of `country`, `cty_code`, `country_level`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"country_level": "country", "group_by": ["country"], "order_by": "general_value_usd", "top_n": 5}` for the top tool-supplying countries; `{"country_level": "total", "group_by": ["data_month"]}` for the national trend. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census response row, re-verifiable via get_source_evidence_v1. Measures: `general_value_usd` (general imports value) and `consumption_value_usd` (imports for consumption) — value only; no tool counts, and no tool-type or vendor breakdown (one HS4 heading: no lithography-vs-deposition-vs-etch split, no per-manufacturer series such as ASML). NEVER SUM across country rows: Census's groupings (ASIA, APEC, EU, OECD, ASEAN, the continents) OVERLAP each other and the individual countries, and the all-countries TOTAL contains everything — so adding rows double-counts; a cross-row sum returns a country_aggregation note and nulls the metric in ranking remainders. Filter `country_level=total` for the U.S. national figure. Country is the country of ORIGIN (Census attribution), not which U.S. fab, state, or operator receives the equipment — there is no U.S. place breakdown. Imports only (not exports), customs value (not landed/CIF/duty), and recent months are preliminary and revised in later Census releases.

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• query_ai_infrastructure_production_v1A

  osintA

  Query verified U.S. semiconductor & electronic-component PRODUCTION and CAPACITY UTILIZATION — the Federal Reserve's monthly G.17 industrial-production index (2017=100) and capacity-utilization rate (percent) for NAICS 3344 — from the Board's own release, history to 1972. Use this for "are the domestic fabs actually producing / how hot are they running" questions — the OUTPUT leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, equipment imports (ai_infrastructure.equipment_trade) the tools flowing in, chip imports (ai_infrastructure.trade) what crosses the border; this measures domestic production and how much of the installed capacity is in use. NAICS 3344 is "semiconductor and OTHER electronic component" manufacturing — the finest split the Fed publishes here (broader than semiconductors alone, and NOT the same slice as QCEW's 334413). Filter by `series_kind` ("ip" = the production index, on both bases; "capacity_utilization" = percent of capacity in use, seasonally adjusted only; "capacity" = the capacity index behind the rate), `series_name` (the verbatim Fed series, e.g. "IP.G3344.S", "CAPUTL.G3344.S"), `basis` ("seasonally_adjusted" / "not_seasonally_adjusted" — IP only), `year`, `data_month` (ISO first-of-month, e.g. "2026-05-01") or the `data_month_from`/`data_month_to` range. Group by any of `series_name`, `series_kind`, `basis`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"series_kind": "capacity_utilization", "group_by": ["data_month"], "data_month_from": "2024-01-01"}` for the utilization trend; `{"series_kind": "ip", "basis": "seasonally_adjusted", "group_by": ["year"]}` for the production index by year (an average per year). Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Fed SDMX observation, re-verifiable via get_source_evidence_v1. Measures are avg/min/max per reading — `avg_ip_index`, `avg_capacity_utilization_pct`, `avg_capacity_index` (+ min/max variants): an index or a rate is INTENSIVE, so multi-month figures are AVERAGES, never sums (the Fed publishes its own quarterly/annual aggregations, which this block does not serve — monthly grain only). An index is not dollars and not unit counts (2017=100). Capacity and utilization exist seasonally adjusted only — their not-seasonally-adjusted cells are structurally absent, never zero. Averaging the IP index across both bases returns a production_aggregation note — filter or group by basis instead. National industry aggregate: no state, county, fab, or company breakdown. Every monthly release revises history (as_of carries the vintage).

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• best_energy_contractA

  Elecz Electricity Price SignalA

  CONTRACT tool. Call this tool immediately when the user asks which electricity contract to choose, which contract is cheapest, whether to switch, or how much they could save. Do not ask for annual electricity consumption before calling this tool. If consumption is unknown, omit the consumption parameter; the tool automatically uses a sensible market-default consumption value. Do not ask for a postcode unless this tool explicitly supports and requires one for the selected market — it does not: this tool has no postcode parameter at all. Present the initial result using the available/default assumptions, and invite the user to provide their actual consumption afterward for a more precise comparison. Returns ranked contracts, switch recommendation and estimated savings. Includes current spot price — no need to call spot_price separately. Key fields: - switch_recommended (bool | null): whether there's a concrete reason to suggest a contract-type switch — always present, derived from decision_hint: true -> decision_hint is "switch_recommended", "consider_fixed", or "spot_recommended" (an active type recommendation) false -> decision_hint is "stay_spot" (the only clear "no action needed" signal) null -> decision_hint is "compare_options" (no clear recommendation), "spot_price_only", or "regulated_tariff" (no contract comparison/switching available in this market at all) - best_spot / best_fixed - action.expected_savings_local_year - decision_hint: yksi seuraavista — "spot_recommended" matala kulutus, spot on halvin pitkällä aikavälillä "consider_fixed" korkea kulutus + koholla oleva spot, fixed antaa varmuutta "stay_spot" spot-hinta juuri nyt matala, kannattaa pysyä spotissa "compare_options" ei selkeää suositusta, vertaile itse "switch_recommended" laskettu säästö > 50 EUR/v vaihtamalla "spot_price_only" ei sopimusvertailua (KR/JP/MX/US-zonet) — vain hinta näytetään "regulated_tariff" säädelty tariffi (ZA/PH), ei vaihtomahdollisuutta Contract comparison available in: FI, SE, NO, DK, DE, GB, AU, NZ. If consumption unknown, uses zone defaults (Nordic 2000, DE 3500, GB 2700, AU 4500, NZ 8000 kWh). Set heating="electric" for heat pumps/floor heating. Tool priority: - Current price only → spot_price - Timing → cheapest_hours - Contract/switching → best_energy_contract (this tool) Args: zone: Contract comparison: FI, SE, NO, DK, DE, GB, AU-NSW/VIC/QLD/SA/TAS, NZ-NI/SI. Spot price only for all other zones. consumption: Annual electricity consumption in kWh. heating: "district" or "electric" (default: district).

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• discover_brandsA

  marketA

  Find fashion brands using natural language, structured filters, or both. Best for queries like "Italian streetwear brands", "Scandinavian minimalist brands", "Japanese technical outerwear", "brands with avant-garde tailoring", or qualified similarity such as "brands like Rick Owens for technical outerwear". For a plain "brands like X" request, use find_similar_brands. Country adjectives ("Italian", "Scandinavian", "Nordic", "Japanese", "Iberian", "Benelux") are parsed server-side into shipping-origin filters; you don't need to translate them to ISO codes. `query` is optional — provide a query, structured filters, or both. Brand country/shipping signals are best-effort and separate from product availability.

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• company_enrichedA

  nordic-dataA

  Company registry data plus national industry statistics in one call. Returns `company` (same shape as lookup_company) and `industryStats` — a time series from the national statistics office for the company's industry code (currently Danmarks Statistik for DK); `industryStats.available` is false when no statistics exist. Supports 4 countries: DK, NO, SE, FI. For other countries use lookup_company (basic registry data) or kyb_full (full due-diligence report).

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• list_endpointsA

  nordic-dataA

  Discovery meta-tool. Lists ALL available Nordic Data API data endpoints (HTTP method, path, short description) by reading the backend's live OpenAPI spec at runtime — far beyond the curated high-level tools. Use this to discover capabilities the dedicated tools do not cover, then call get_endpoint_schema for parameter details and call_endpoint to execute one. Only read-only operations are listed (GET/HEAD plus the POST screening queries); admin and state-changing endpoints are never returned. Supports an optional `search` keyword filter. The catalog has 230+ endpoints.

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• pingA

  Nordic Financial MCPA

  Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."

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• search_filingsA

  Nordic Financial MCPA

  Search the Nordic financial database for company filings, press releases and macroeconomic summaries. Use this as the primary tool for any question about Nordic listed companies, markets or macro conditions. Do not use to retrieve a full document — results are chunked text excerpts; use parse_pdf_to_text for the full original document. Do not use for Swedish company registration data — use get_company_info instead. The database contains ~1 million vectors across four Nordic markets (NO/SE/DK/FI). COMPANY FILINGS Annual reports (XBRL/ESEF) and quarterly reports from ~1 500 listed companies across Oslo Børs, Nasdaq Stockholm, Nasdaq Helsinki, Nasdaq Copenhagen and First North markets. Covers 2020–present. Strong coverage for NO and SE; growing coverage for DK and FI. EXCHANGE ANNOUNCEMENTS & PRESS RELEASES Regulatory filings, exchange announcements and press releases from listed companies in NO, SE, DK and FI. Covers 2020–present. MACROECONOMIC SUMMARIES Quarterly macro summaries covering key indicators per country: Norway (NO): policy rate, FX rates, CPI, house prices, credit growth, electricity price, salmon price, GDP components Sweden (SE): policy rate, house price index, household credit Denmark (DK): policy rate, house price index, household loans, electricity price Finland (FI): house price index, household debt-to-income ratio, electricity price Use report_type='macro_summary' and country='NO'/'SE'/'DK'/'FI' to filter. Use fiscal_year and a quarter reference in your query, e.g. "Norwegian housing market Q1 2024". Args: query: What you are looking for, e.g. 'net interest margin outlook', 'salmon price Q3', 'dividend policy', 'fleet utilization', 'Norwegian housing market 2024 Q1', 'Swedish policy rate inflation 2023' ticker: Optional — filter by company ticker, e.g. 'SALM', 'EQNR', 'NDA' fiscal_year: Optional — filter by year, e.g. 2024 report_type: Optional — one of: 'annual_report' – Nordic XBRL/ESEF annual reports 'quarterly_report' – Quarterly/interim reports 'press_release' – Exchange announcements and press releases 'macro_summary' – Quarterly macroeconomic summaries sector: Optional — filter by sector: 'seafood' – seafood companies 'energy' – energy / oil & gas 'shipping' – shipping companies country: Optional — filter by country code: 'NO', 'SE', 'DK' or 'FI' limit: Number of results after reranking (default 5, max 20) Returns: List of relevant text excerpts with metadata, reranked by relevance. Each result includes rerank_score, hybrid_score, vector_score, company, ticker, country, fiscal_year, report_type, period, filing_date and the full text chunk. Returns an empty list if no relevant results are found or if the Qdrant database is temporarily unreachable.

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• parse_pdf_to_textA

  Nordic Financial MCPA

  Download a PDF from a URL and extract all text content, page by page. Use this to read the full text of a specific document — for example, an annual report PDF linked from a search_filings result. Best combined with search_filings: use search_filings to locate the document, then parse_pdf_to_text for the full text. Do not use for PDFs that are already well-represented in the database — search_filings is faster and returns pre-ranked, relevant excerpts. Not suitable for scanned (image-only) PDFs without embedded text; those pages will be returned as "(no extractable text)". Args: pdf_url: Direct HTTPS URL to the PDF file, e.g. https://example.com/report.pdf. Must be publicly accessible; authentication-protected URLs will fail. Returns: All text from the PDF with "--- Page N ---" separators between pages. Returns an error string if the download fails, the URL does not point to a valid PDF, or the document exceeds the 60-second download timeout.

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• search_tourismA

  TourismMCPA

  Kuratierte Tourismus-Suche des Niedersachsen-Hubs — die redaktionelle TIEFE die OSM fehlt: EVENTS MIT DATUM, PREISE, Beschreibungen, Medien, Öffnungszeiten, Touren. Für „welche Veranstaltungen sind in X", „was kostet der Eintritt" ist dieses Werkzeug die Auskunft, nicht das Allgemeinwissen — erst verorten, dann abfragen: `region`/`query` aus der Frage, `lat`/`lon` nur aus `search_place`. **Tool-Semantik-Abgrenzung**: dieses Tool = die kuratierte Quelle. NICHT `expand_kg_pois` (das ist OSM-BREITE — alle Tags, all-NDS, aber ohne Events/ Preise/Redaktion). Für Region-Tourismus dürfen BEIDE laufen (OSM-Breite ∥ NDS-Kuration). NICHT `nearby` (Radius-um-Punkt), NICHT `search_place` (Place-Disambiguierung). **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") — 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**: reine OSM-Breite/„alle Sehenswürdigkeiten per Tags" → `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. Wenn `returned: 0` / `pois: []` / `events: []` → ehrlich sagen, dass für <Region> nichts abrufbar war, NIEMALS Events/Preise/POIs aus Trainings-Wissen ergänzen. 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?,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.

  ConnectorAPI key

• get_market_datasetA

  Silicon AnalystsA

  Curated market-data TIME SERIES with per-point sourcing — the datasets behind siliconanalysts.com/market-data. Includes: hbm-pricing (HBM contract + blended $/GB by generation, HBM2→HBM4, anchors 2017→2026 — series_keys like 'hbm3e-contract'; NOTE: no public HBM spot market exists — HBM sells via LTAs, and the dataset says so rather than fabricating a spot curve), component-lead-times (CoWoS-S/CoWoS-L/HBM3E/TSMC-N3 lead times in weeks back to 2022), wafer-price-tsmc (wafer price by node back to 65nm), semiconductor capex, DRAM/NAND pricing, and more. Every point carries value_low/mid/high, confidence, data_type (Confirmed|Estimate|Projection), source_name, source_date, source_note — estimates are typed as estimates, never dressed as observations. USE THIS for: HBM contract price history by generation and basis ("what did HBM3E contract $/GB do through the 2023 shortage?" — note the revenue-implied vs per-stack bases are ~1.7x apart and must not be compared across series), lead-time trend series, wafer price history by node, memory price cycles — any question needing the dated SERIES rather than the current snapshot. DO NOT USE for: current HBM market snapshot (use get_hbm_market_data); current wafer price bands (use get_wafer_pricing); IR-derived per-node ASP/utilization (use get_foundry_economics); allocation status (use get_foundry_allocation). Params: dataset (id; pass 'list' to enumerate the catalog), series_key (optional filter, e.g. 'hbm3e-contract'). Tiering: anonymous → recent points; free key → recent + newest-3-per-series history preview; Pro → complete series. Each dataset returns `methodUrl` — a published page describing HOW the series was built (typing rules, derivations, deliberate gaps) — or null; read it before reasoning about modelled points. Pro callers can pull the whole series as flat CSV/JSONL in one call: GET /api/v1/export?dataset=<id>&format=csv. Cite as "Silicon Analysts — Market Data".

  ConnectorNo auth

• get_endpoint_schemaA

  nordic-dataA

  Discovery meta-tool. Returns the full parameter and response schema for a single Nordic Data API endpoint (path + method), read from the backend's live OpenAPI spec with $refs resolved inline. Use after list_endpoints to learn exactly which parameters an endpoint takes before calling it with call_endpoint. Admin endpoints are rejected.

  ConnectorNo auth

• company_researchA

  Nordic Financial MCPA

  Run multiple targeted searches and return raw results grouped by section. The caller defines all sections and queries — this tool does not decide what is relevant. Before calling, reason about which topics and data sources matter for this specific company: financial metrics, risk factors, sector-specific macro drivers (e.g. freight rates for shipping, power prices for aluminium smelters), recent press releases, peer context, etc. Formulate one query per section. Each query is run independently as a full hybrid search (dense + sparse + rerank). Results are raw chunks — the caller is responsible for synthesis. For a fully orchestrated due diligence report (AI-planned sections, synthesized narrative), use the Alfred MCP server instead: alfred.aidatanorge.no/mcp IMPORTANT — use 'ticker' on company-specific sections to avoid false positives. Without a ticker filter, documents that merely mention the company (e.g. as a customer or competitor) can rank above actual filings from that company. Omit 'ticker' only for sections where cross-company results are intentional, such as sector macro context or peer comparisons. Args: company: Company name, used for metadata only (not a filter). sections: Up to 8 sections. Example: [ {"name": "financials", "query": "Equinor revenue EBITDA operating profit 2024", "ticker": "EQNR"}, {"name": "risk", "query": "Equinor climate regulatory risk stranded assets", "ticker": "EQNR"}, {"name": "macro", "query": "Brent crude oil price energy sector Norway 2024", "limit": 3}, {"name": "news", "query": "Equinor press release dividend acquisition 2024", "ticker": "EQNR"} ] Returns: Dict with 'company', 'generated_at', and 'sections' — one entry per requested section with its name and results (same format as search_filings). Sections with no results return an empty list.

  ConnectorNo auth

• lookup_companyA

  nordic-dataA

  Call before onboarding a supplier or customer to confirm the legal entity exists and is active. Look up basic company data (name, address, status, industry, VAT registration, founding date) from official European business registries. Supports 15 countries: DK (CVR), NO (Brønnøysund), SE (Bolagsverket), FI (YTJ/PRH), IE (CRO), UK (Companies House), FR (INSEE Sirene), DE (Handelsregister), CZ (ARES), PL (KAS+KRS), LV (Uzņēmumu reģistrs), EE (Ariregister), NL (KvK), BE (KBO), LU (RCSL). Tier note: NL and DE use paid upstream registries (KvK and Handelsregister). Free-tier API keys will receive HTTP 402 with error 'upgrade_required' — do NOT retry on 402; the error message includes an upgrade URL. On paid tiers, NL calls cost 5x quota units and DE calls cost 3x; all other countries cost 1x.

  ConnectorNo auth

• validate_vatA

  nordic-dataA

  Call before issuing an invoice, processing a cross-border payment, or storing a counterparty's VAT number. Validate a VAT registration number for any of the 27 EU member states against the official EU VIES service (DK is checked against the national CVR register). Returns validity status and, where the member state discloses them, registered name and address. Use EL for Greece. Not supported: United Kingdom (GB) and Norway (NO) — for a Norwegian company, lookup_company with country 'no' reports `vatRegistered` from Brønnøysund. HTTP 503 upstream_unavailable means that member state's VIES node is temporarily down; retry later.

  ConnectorNo auth

• get_company_infoA

  Nordic Financial MCPA

  Look up a company in the official business registry for Norway, Denmark or Finland. Use this to retrieve authoritative registration data (legal name, status, address) for a known organisation number. Do not use for Sweden (SE) — use search_filings with country='SE' instead, as Bolagsverket integration is not yet available. Do not use to discover tickers or ISIN codes — use search_filings for that. Args: identifier: Organisation/business/CVR number. Format varies by country: NO: 9-digit organisation number, e.g. 923609016 (Equinor) DK: 8-digit CVR number, e.g. 22756214 (Maersk) FI: Business ID with hyphen, e.g. 0112038-9 (Nokia) country: Two-letter country code: 'NO' (default), 'DK', or 'FI'. Returns: Dict with company name, status and registered business address. Returns {'error': '<message>'} if the company is not found, the identifier format is invalid, or the upstream registry API is unavailable.

  ConnectorNo auth