io.github.AIDataNordic由 GitHub 帳號 AIDataNordic 發布v1.1.0更新於 Oct 24, 2026
Semantic search over Nordic filings, press releases, macro data and electricity prices.
遠端 MCP 伺服器，可對北歐財務申報文件、新聞稿、總體經濟資料與電價進行語意搜尋，並提供公司登記資訊查詢與 PDF 文字擷取。
Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。
把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。
閱讀 https://sourceweft.com/mcp/io.github.AIDataNordic%2Fnordic-financial-mcp/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。把它新增到你客戶端的 mcpServers 設定中。
{
"mcpServers": {
"nordic-financial": {
"type": "http",
"url": "https://mcp.aidatanorge.no/mcp"
}
}
}[smithery badge] [nordic_financial_mcp MCP server]
A production-grade semantic search server for Nordic financial markets — built for autonomous AI agents. 1,000,000+ vectors across exchange filings, company reports, commodity prices, freight rates, energy data and press releases.
Search: Natural language queries over annual reports, quarterly reports, exchange announcements and macroeconomic summaries — filtered by company, ticker, country, sector or year. Two-stage hybrid retrieval (dense + sparse BM25, fused via RRF) with cross-encoder reranking for high-precision results.
Live endpoint: https://mcp.aidatanorge.no/mcp
Transport: streamable-http
Registry: Smithery · MCP Registry · Glama · mcp.so
Add to your MCP client config:
Or with Claude Code:
Try the live demo in your browser:
👉 https://mcp.aidatanorge.no/demo
No installation, no configuration. Just search for "Equinor dividend", "Swedish policy rate", or "salmon price Q3".
This server follows the StreamableHTTP MCP transport. A complete handshake is required before calling tools.
This handshake is automatic in MCP-compliant clients like Claude Desktop, LangChain, and the MCP Python SDK. If you're building a custom client, following the sequence above ensures compatibility.
The /demo endpoint shows how a browser can perform the same handshake using JavaScript fetch() — view source for a working implementation.
AIDataNorge is a full-stack data pipeline and semantic search system that ingests, processes, and indexes financial data from Nordic markets into a vector database optimized for AI agent queries. It exposes data through a Model Context Protocol (MCP) server, making it natively compatible with Claude, LangChain, and other LLM-based agents.
The system is designed with autonomous machine-to-machine consumption in mind, including support for emerging agent payment protocols. The database is updated nightly.
search_filingsSemantic search over Nordic company filings, press releases and macroeconomic summaries.
Search pipeline: Dense embedding (intfloat/e5-large-v2, 1024d) + sparse BM25, fused via Reciprocal Rank Fusion (RRF), reranked by mmarco-mMiniLMv2-L12-H384-v1. Natural language queries in any language are supported.
get_company_infoLook up a company in the official business registry.
parse_pdf_to_textDownload a PDF from a URL and extract all text, page by page.
get_current_power_priceReal-time day-ahead electricity spot prices for all Nordic bidding zones.
company_researchRun multiple targeted searches in a single call and get raw results grouped by section. The caller defines all sections and queries and is responsible for synthesizing the output.
For a fully orchestrated due diligence report where AI plans the sections and synthesizes the narrative, use Alfred MCP instead.
pingTotal: 1,000,000+ vectors · Updated nightly
Data ingestion
intfloat/e5-large-v2 (1024d)Qdrant/bm25 via fastembedStorage & search
mmarco-mMiniLMv2-L12-H384-v1)Serving
/mcp endpoint)Infrastructure
The system is built with autonomous agent monetization in mind, supporting three complementary payment protocols:
x402 Micropayments
A pay-per-call variant of the server (mcp_server_x402.py) is implemented using the x402 protocol — the HTTP 402 payment standard for autonomous agents. Agents receive a payment requirement response, pay in USDC on Base, and retry automatically. Currently paused — x402 functionality will be integrated directly into the main server (mcp_server.py) in a future release.
Lightning Network (L402)
Running a full Bitcoin node with LND enables L402 — the HTTP payment protocol for autonomous agents. Agents can discover the API, receive a Lightning invoice, pay in millisatoshis, and get access — all without human intervention. Infrastructure in place, monetization layer in development.
DigiRail / DigiDollar
Also running a DigiByte full node with DigiRail (an agent payment protocol similar to L402) and a DigiDollar Oracle node. DigiDollar is the world's first UTXO-native decentralized stablecoin, implemented directly in DigiByte Core v9.26. The oracle node contributes to the decentralized price feed that maintains DigiDollar's USD peg — 15 of 30 randomly selected oracle nodes must reach consensus every ~25 minutes using Schnorr signatures.
This multi-protocol payment infrastructure (x402/Base + Bitcoin/Lightning + DigiByte/DigiRail) positions AIDataNorge to serve agents operating across different payment ecosystems.
Each data source has a dedicated ingest script with:
processed.txt log to avoid redundant re-fetchingnohup + cron scheduling for unattended overnight runssource, country, ticker, company_name, report_type, published_date, chunk_index, total_chunksChunking strategy: paragraphs are accumulated until reaching the 512-token model window. Chunks never split mid-sentence. 100-token overlap ensures context continuity across chunk boundaries.
nordic_company_data: 1,000,000+ vectors — XBRL, MFN, Newsweb, Cision, GlobeNewswire, ENTSO-E, commodity/freight, macrohttps://mcp.aidatanorge.no/mcphttps://mcp.aidatanorge.no/demo來源：README.md，提交 60b1e04
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."
nameLook 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.
countryidentifier必填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' fisc
fastlimitquery必填sectorsourcetickercountryfiscal_yearreport_typemax_chunk_indexDownload 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.
pdf_url必填Fetch today's hourly day-ahead electricity spot prices for a Nordic bidding zone. Use this for current and near-term (today/tomorrow) price queries. Do not use for historical price analysis — use search_filings with report_type='macro_summary' and a date reference in the query for that purpose. Tomorrow's prices are published by NordPool around 13:00 CET; requests before that time will return "not yet available" for the tomorrow field. All zones return prices in EUR/kWh (NordPool day-ahead, native currency). Norwegian zones (NO1–NO5) use hvakosterstrommen.no; all other zones use ENTSO-E. Args: zone: Bidding zone code. Options: NO1 (East/Oslo), NO2 (Southwest), NO3 (Central/Trondheim), NO4 (North), NO5 (West/Bergen), SE1–SE4, DK1, DK2, FI. include_tomorrow: Set to True to also fetch tomorrow's hourly prices if already published (default False). Returns: Dict containing zone, date, current_hour_utc, current price, and a 'today' summary with min/max/avg and the full hourly list. Includes a 'tomorrow' key if include_tomorrow=True. Returns {'error': '<message>'} if price data is unavailable for the requested zone or date.
zoneinclude_tomorrowRun 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.
company必填sections必填AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company
company必填question必填已滿足適用檢查項的 100%
該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。
{ "mcpServers": { "nordic-financial": { "type": "streamable-http", "url": "https://mcp.aidatanorge.no/mcp" } }}claude mcp add --transport http nordic-financial https://mcp.aidatanorge.no/mcp# 1. Create session and capture session IDSESSION_ID=$(curl -X GET https://mcp.aidatanorge.no/mcp \ -H "Accept: application/json, text/event-stream" \ -s -i | grep -i "mcp-session-id" | awk '{print $2}' | tr -d '\r')
echo "Session ID: $SESSION_ID"
# 2. Initialize sessioncurl -X POST https://mcp.aidatanorge.no/mcp \ -H "Content-Type: application/json" \ -H "Accept: application/json, text/event-stream" \ -H "mcp-session-id: $SESSION_ID" \ -d '{ "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "example-client", "version": "1.0"} } }'
# 3. Send initialized notificationcurl -X POST https://mcp.aidatanorge.no/mcp \ -H "Content-Type: application/json" \ -H "Accept: application/json, text/event-stream" \ -H "mcp-session-id: $SESSION_ID" \ -d '{"jsonrpc": "2.0", "method": "notifications/initialized"}'
# 4. List available toolscurl -X POST https://mcp.aidatanorge.no/mcp \ -H "Content-Type: application/json" \ -H "Accept: application/json, text/event-stream" \ -H "mcp-session-id: $SESSION_ID" \ -d '{"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}'
# 5. Perform a searchcurl -X POST https://mcp.aidatanorge.no/mcp \ -H "Content-Type: application/json" \ -H "Accept: application/json, text/event-stream" \ -H "mcp-session-id: $SESSION_ID" \ -d '{ "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "search_filings", "arguments": {"query": "Equinor dividend", "limit": 3} } }'from mcp import ClientSessionfrom mcp.client.streamable_http import streamablehttp_client
async with streamablehttp_client("https://mcp.aidatanorge.no/mcp") as transport: async with ClientSession(*transport) as session: await session.initialize() tools = await session.list_tools() result = await session.call_tool( "search_filings", {"query": "Norwegian housing market Q3 2024", "country": "NO"} ) print(result.content[0].text)search_filings( query="Nordea net interest margin outlook 2025", report_type="quarterly_report", # annual_report | quarterly_report | press_release | macro_summary country="SE", # NO | SE | DK | FI ticker="NDA", # optional — filter by company ticker fiscal_year=2025, # optional — filter by year sector="energy", # optional — seafood | energy | shipping limit=10 # default 5, max 20)# Returns semantically ranked text chunks with rerank_score, hybrid_score, vector_score,# company, ticker, country, fiscal_year, report_type, filing_date and full text.get_company_info( identifier="923609016", # org/CVR/business ID country="NO" # NO (Brønnøysund) | DK (CVR) | FI (PRH))# Returns company name, status and registered address.parse_pdf_to_text( pdf_url="https://example.com/annual_report_2024.pdf")# Returns extracted text with page separators.# Useful for reading report attachments not indexed in the main database.get_current_power_price( zone="NO1", # NO1–NO5, SE1–SE4, DK1, DK2, FI include_tomorrow=False # fetch tomorrow's prices if available (published ~13:00 CET))# Returns EUR/kWh — current hour price + full hourly breakdown + daily min/max/avg.# Norwegian zones sourced from hvakosterstrommen.no, others directly from ENTSO-E.# Handles both PT60M (hourly) and PT15M (15-min) resolutions.company_research( company="Equinor", sections=[ {"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: {company, generated_at, sections} — one entry per section with ranked text chunks.# All sections are searched in parallel. Up to 8 sections, max 10 results each.# Use ticker on company-specific sections to avoid false positives from documents# that merely mention the company as a customer or competitor.ping(name="world")# Returns: "Hello world! Nordic MCP server is running."Data Sources Pipeline Serving───────────────── ───────────────── ─────────────────XBRL ESEF → Python ingest scripts → QdrantMFN Nordics → + Playwright scraping → Vector DatabaseOslo Børs Newsweb → + PDF extraction → (1,000,000+ vectors)Nasdaq Copenhagen → + Chunking → ↓Cision / GlobeNewswire →SSB / Norges Bank → + Chunking → ↓SSB / Norges Bank → + Dense embeddings → MCP ServerSCB / DST / stat.fi → → (e5-large-v2, 1024d) → (FastMCP 3.2) → + Sparse BM25 → ↓ → + RRF fusion → AI Agents / LLMs# Qdrant responding?curl http://localhost:6333
# Vector countcurl http://localhost:6333/collections/nordic_company_data | python3 -m json.toolps aux | grep mcp_server.py# Tail live logtail -f ~/logs/mcp_server.log
# Run full query analysiscd ~/norsk-mcp-server && venv/bin/python3 analyze_queries.pyjournalctl -u cloudflared --since "1 hour ago" | tail -50