Nordic Financial MCP
# Nordic Financial MCP
<!-- mcp-name: io.github.AIDataNordic/nordic-financial-mcp -->
[](https://smithery.ai/servers/kontakt-qy0g/nordic-financial-mcp) [](https://glama.ai/mcp/servers/AIDataNordic/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](https://smithery.ai/servers/kontakt-qy0g/nordic-financial-mcp) · [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.AIDataNordic%2Fnordic-financial-mcp/versions) · [Glama](https://glama.ai/mcp/servers/AIDataNordic/nordic_financial_mcp) · [mcp.so](https://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](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](https://alfred.aidatanorge.no/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
```
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](https://x402.org) — 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`
TDQS
Scored across 6 tools
get_company_info and search_filings have contradictory scoping: get_company_info tells you to use search_filings for Swedish company data, while search_filings tells you to use get_company_info instead. Additionally, due_diligence_report is essentially a multi-query wrapper around the same search functionality, creating a boundary question for agents.
Most tools follow a clear verb_noun pattern (get_company_info, search_filings, parse_pdf_to_text, get_current_power_price). The exceptions are ping, which is a standard health-check convention, and due_diligence_report, which is noun-oriented but still recognizable. Overall readable and mostly predictable.
Six tools is a well-scoped count for the described domain: connectivity, structured registry lookup, broad search, PDF processing, a market-specific data tool, and a multi-step report generator. Each tool earns its place without redundancy or bloat.
The surface covers search, company info, full-text PDF access, power prices, and report generation, but there are notable gaps: no structured Swedish company registry support (despite Nordic coverage), no direct way to list all filings for a given company beyond hit-based search, and no tool for retrieving historical power prices except via macro summaries. These gaps can likely be worked around but may cause agent uncertainty.