Nordic Financial MCP README | AllMCPs

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:

config.json

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

Or with Claude Code:

Terminal

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

server.ts

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