io.github.AIDataNordic/nordic-financial-mcp

Official7 tools

by AIDataNordic · Python

Nordic Financial MCP

Semantic search over Nordic filings, press releases, macro data and electricity prices.

Semantic search over Nordic financial filings, press releases, and macro data.

Topics

rag-pipelinevector-database

Captured live from the server via tools/list.

ping

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."

Parameters (1)

namestring
Arbitrary label included in the response, e.g. 'healthcheck' or 'agent-1'

get_company_info

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.

Parameters (2)

identifierstringrequired
Organisation number (NO: 9 digits, DK: 8 digits CVR, FI: business ID with hyphen)
countrystring
Two-letter country code: NO (default), DK, or FI

search_filings

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.

Parameters (10)

querystringrequired
Natural language search query, e.g. 'Equinor dividend 2024' or 'Norwegian housing market Q3'
tickerstring
Filter by company ticker, e.g. 'EQNR', 'SALM', 'NDA'
fiscal_yearinteger
Filter by fiscal year, e.g. 2024. Use 0 for no filter
report_typestring
Filter by type: annual_report, quarterly_report, press_release, exchange_announcement, macro_summary
sectorstring
Filter by sector, e.g. 'energy', 'financials', 'salmon'
countrystring
Filter by country: NO, SE, DK, or FI
sourcestring
Filter by data source, e.g. 'xbrl_esef', 'newsweb', 'mfn_nordics', 'nasdaq_se'
max_chunk_indexinteger
Only return chunks with chunk_index <= this value. Use 3 to target introductory sections of long documents. Use 0 for no filter.
limitinteger
Number of results to return (1–20)
fastboolean
Skip reranking and return results ranked by hybrid score only. Faster but less precise.

parse_pdf_to_text

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.

Parameters (1)

pdf_urlstringrequired
Direct HTTPS URL to the PDF file

get_current_power_price

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.

Parameters (2)

zonestring
Bidding zone: NO1–NO5, SE1–SE4, DK1, DK2, or FI
include_tomorrowboolean
Also fetch tomorrow's prices if available (published after 13:00 CET)

company_research

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.

Parameters (2)

companystringrequired
Company name to research, e.g. 'Equinor', 'Norsk Hydro', 'Aker BP'
sectionsarrayrequired
List of section dicts. Each must have 'name' (str) and 'query' (str). Optional: 'ticker' (str, filters results to that company), 'limit' (int, default 5, max 10). Maximum 8 sections.

analyze_company

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 model: 'haiku' (default) or 'sonnet'

Parameters (3)

companystringrequired
Company name or ticker, e.g. 'Equinor' or 'EQNR'
questionstringrequired
Question to answer, e.g. 'How did margins develop 2022-2024?' or 'What are the main risk factors?'
modelstring
Model: 'haiku' (default, fast, ~$0.07/call) or 'sonnet' (more capable, ~$0.24/call)

README.md16 KBView on GitHub

Nordic Financial MCP

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

Connect

Add to your MCP client config:

json

{
  "mcpServers": {
    "nordic-financial": {
      "type": "streamable-http",
      "url": "https://mcp.aidatanorge.no/mcp"
    }
  }
}

Or with Claude Code:

bash

claude mcp add --transport http nordic-financial https://mcp.aidatanorge.no/mcp

Quick Test

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".

For MCP Client Developers

This server follows the StreamableHTTP MCP transport. A complete handshake is required before calling tools.

Full Handshake Example (Copy-Paste Ready)

bash

# 1. Create session and capture session ID
SESSION_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 session
curl -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 notification
curl -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 tools
curl -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 search
curl -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}
    }
  }'

Common Issues & Solutions

Error	Cause	Solution
`406 Not Acceptable`	Missing `text/event-stream` in Accept header	Send: `Accept: application/json, text/event-stream`
`400 Bad Request: Missing session ID`	No session established	First `GET /mcp`, use returned `mcp-session-id` header
`-32602 Invalid request parameters`	Missing `initialize` before `tools/list`	Complete steps 1-3 in order

Python Example with MCP SDK

python

from mcp import ClientSession
from 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)

Why This Matters

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.

What This Is

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.

MCP Tools

`search_filings`

Semantic search over Nordic company filings, press releases and macroeconomic summaries.

python

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.

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_info`

Look up a company in the official business registry.

python

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`

Download a PDF from a URL and extract all text, page by page.

python

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`

Real-time day-ahead electricity spot prices for all Nordic bidding zones.

python

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`

Run 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.

python

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.

For a fully orchestrated due diligence report where AI plans the sections and synthesizes the narrative, use Alfred MCP instead.

`ping`

python

ping(name="world")
# Returns: "Hello world! Nordic MCP server is running."

Data Coverage

Source	Geography	Content	Volume
XBRL ESEF (filings.xbrl.org)	NO/SE/DK/FI/IS	Annual reports, regulated markets, 2020–present	~89k vectors
MFN Nordics	SE/NO/DK/FI	Annual & quarterly reports, First North companies	~116k vectors
Oslo Børs Newsweb	NO	Exchange announcements, 2020–present	~52k vectors
Nasdaq Copenhagen	DK	Exchange announcements, 2020–present	~8k vectors
Nasdaq Helsinki	FI	Exchange announcements, 2020–present	~5k vectors
Nasdaq Stockholm	SE	Exchange announcements, 2020–present	in progress
Cision	SE/NO/DK/FI	Press releases	~20k vectors
GlobeNewswire	NO/SE/DK/FI	Press releases, updated hourly Mon–Fri	~500 vectors
ENTSO-E	NO/SE/DK/FI	Day-ahead electricity prices, all bidding zones	~24k vectors
Commodity & freight	Global	Oil, gas, metals, shipping rates (BDRY/FRO/ZIM proxies)	25 quarters
Macro Norway	Norway	GDP, CPI, rates, housing, salmon, power	24 quarters
Macro Nordics	SE/DK/FI	Rates, housing, credit, power	72 quarters

Total: 1,000,000+ vectors · Updated nightly

Architecture

code

Data Sources                 Pipeline                  Serving
─────────────────            ─────────────────         ─────────────────
XBRL ESEF               →    Python ingest scripts  →  Qdrant
MFN Nordics             →    + Playwright scraping  →  Vector Database
Oslo Børs Newsweb       →    + PDF extraction        →  (1,000,000+ vectors)
Nasdaq Copenhagen       →    + Chunking              →        ↓
Cision / GlobeNewswire  →
SSB / Norges Bank       →    + Chunking              →        ↓
SSB / Norges Bank       →    + Dense embeddings      →  MCP Server
SCB / DST / stat.fi     →
                        →      (e5-large-v2, 1024d)  →  (FastMCP 3.2)
                        →    + Sparse BM25            →        ↓
                        →    + RRF fusion             →  AI Agents / LLMs

Technical Stack

Data ingestion

Python with Playwright for JavaScript-rendered IR pages and MFN feed
PyMuPDF (fitz) for PDF text extraction
Paragraph-aware chunking (512-token chunks, 100-token overlap)
Dense embeddings: intfloat/e5-large-v2 (1024d)
Sparse embeddings: Qdrant/bm25 via fastembed

Storage & search

Qdrant vector database (self-hosted)
Hybrid dense+sparse retrieval with Reciprocal Rank Fusion (RRF)
Cross-encoder reranking (mmarco-mMiniLMv2-L12-H384-v1)

Serving

FastMCP 3.2 over HTTP (/mcp endpoint)
Cloudflare Tunnel — rate limited to 60 req/min per IP
Compatible with Claude, LangChain, and any MCP-capable agent

Infrastructure

Ubuntu Server 24 LTS, self-hosted
16 GB RAM
Automated cron jobs for continuous ingestion
Bitcoin full node (LND) for Lightning Network payments
DigiByte full node with DigiRail and DigiDollar Oracle node

Agent Payment 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.

Ingest Pipeline Design

Each data source has a dedicated ingest script with:

Idempotent processing via MD5-based point IDs (upsert-safe)
processed.txt log to avoid redundant re-fetching
nohup + cron scheduling for unattended overnight runs
Structured payload per chunk: source, country, ticker, company_name, report_type, published_date, chunk_index, total_chunks

Chunking 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.

Cron Schedule

Time	Job
03:17 Sundays	XBRL annual reports
06:00 Mon–Fri	yfinance — stock prices and FX rates
06:15 daily	MFN Nordics — quarterly reports and press releases
06:30 Mon–Fri	ENTSO-E — energy data
07:00 daily	Oslo Børs Newsweb — exchange announcements
08:00–18:00 hourly Mon–Fri	GlobeNewswire — press releases (NO/SE/DK/FI)
09:00 daily	Query analysis report (email)

Monitoring & Activity

Check server health

bash

# Qdrant responding?
curl http://localhost:6333

# Vector count
curl http://localhost:6333/collections/nordic_company_data | python3 -m json.tool

Check MCP server process

bash

ps aux | grep mcp_server.py

Check MCP query activity

bash

# Tail live log
tail -f ~/logs/mcp_server.log

# Run full query analysis
cd ~/norsk-mcp-server && venv/bin/python3 analyze_queries.py

Check Cloudflare tunnel

bash

journalctl -u cloudflared --since "1 hour ago" | tail -50

Skills Demonstrated

RAG system design — end-to-end pipeline from raw data to semantic search
Hybrid retrieval — dense+sparse embeddings with RRF fusion and cross-encoder reranking
Web scraping at scale — Playwright, RSS feeds, REST APIs, PDF extraction
Vector database operations — Qdrant, embedding models, reranking
MCP server development — FastMCP, tool design for LLM agents
Agent payment protocols — x402, L402, DigiRail
Linux server administration — process management, cron, systemd
Blockchain infrastructure — Bitcoin full node + LND, DigiByte full node + oracle
Python engineering — async pipelines, error handling, idempotent design
Financial data domain knowledge — Nordic exchanges, regulatory filings, macro data

Status (May 2026)

nordic_company_data: 1,000,000+ vectors — XBRL, MFN, Newsweb, Cision, GlobeNewswire, ENTSO-E, commodity/freight, macro
MCP server: live at https://mcp.aidatanorge.no/mcp
Published: Smithery · MCP Registry · Glama · mcp.so
x402 pay-per-call: implemented, currently paused — will be integrated into main server
L402 / DigiRail: infrastructure in place, monetization layer in development
Live demo: https://mcp.aidatanorge.no/demo

Install

claude_desktop_config.json

{
  "mcpServers": {
    "nordic-financial-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.aidatanorge.no/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.