MCP Server
Nordic Financial MCP
io.github.AIDataNordic/nordic-financial-mcp
What this MCP does
Searches Nordic company filings, press releases, macroeconomic data, and electricity prices, and supports company analysis and research.
Capabilities
Tools
7
tools
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'
Read only
Input schema
{'type': 'object', 'required': ['company', 'question'], 'properties': {'model': {'type': 'string', 'default': 'haiku', 'description': "Model: 'haiku' (default, fast, ~$0.07/call) or 'sonnet' (more capable, ~$0.24/call)"}, 'company': {'type': 'string', 'description': "Company name or ticker, e.g. 'Equinor' or 'EQNR'"}, 'question': {'type': 'string', 'description': "Question to answer, e.g. 'How did margins develop 2022-2024?' or 'What are the main risk factors?'"}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'description': 'Generic wrapper for non-object return types.', 'x-fastmcp-wrap-result': True}
company_research
Run multiple targeted searches and return raw results grouped by section.
The caller defines all sections and queries â 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 â 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 â 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' â one entry per requested
section with its name and results (same format as search_filings).
Sections with no results return an empty list.
Read only
Input schema
{'type': 'object', 'required': ['company', 'sections'], 'properties': {'company': {'type': 'string', 'description': "Company name to research, e.g. 'Equinor', 'Norsk Hydro', 'Aker BP'"}, 'sections': {'type': 'array', 'items': {}, 'description': "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."}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'additionalProperties': True}
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) â use search_filings
with country='SE' instead, as Bolagsverket integration is not yet available.
Do not use to discover tickers or ISIN codes â 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.
Read only
Open world
Input schema
{'type': 'object', 'required': ['identifier'], 'properties': {'country': {'type': 'string', 'default': 'NO', 'description': 'Two-letter country code: NO (default), DK, or FI'}, 'identifier': {'type': 'string', 'description': 'Organisation number (NO: 9 digits, DK: 8 digits CVR, FI: business ID with hyphen)'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'additionalProperties': True}
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 â 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 (NO1âNO5) 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),
SE1âSE4, 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.
Read only
Open world
Input schema
{'type': 'object', 'properties': {'zone': {'type': 'string', 'default': 'NO1', 'description': 'Bidding zone: NO1â\x80\x93NO5, SE1â\x80\x93SE4, DK1, DK2, or FI'}, 'include_tomorrow': {'type': 'boolean', 'default': False, 'description': "Also fetch tomorrow's prices if available (published after 13:00 CET)"}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'additionalProperties': True}
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 â 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 â 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.
Read only
Open world
Input schema
{'type': 'object', 'required': ['pdf_url'], 'properties': {'pdf_url': {'type': 'string', 'description': 'Direct HTTPS URL to the PDF file'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'description': 'Generic wrapper for non-object return types.', 'x-fastmcp-wrap-result': True}
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 â 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."
Read only
Input schema
{'type': 'object', 'properties': {'name': {'type': 'string', 'default': 'world', 'description': "Arbitrary label included in the response, e.g. 'healthcheck' or 'agent-1'"}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'description': 'Generic wrapper for non-object return types.', 'x-fastmcp-wrap-result': True}
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 â results
are chunked text excerpts; use parse_pdf_to_text for the full original document.
Do not use for Swedish company registration data â 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 2020âpresent. 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 2020âpresent.
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 â filter by company ticker, e.g. 'SALM', 'EQNR', 'NDA'
fiscal_year: Optional â filter by year, e.g. 2024
report_type: Optional â one of:
'annual_report' â Nordic XBRL/ESEF annual reports
'quarterly_report' â Quarterly/interim reports
'press_release' â Exchange announcements and press releases
'macro_summary' â Quarterly macroeconomic summaries
sector: Optional â filter by sector:
'seafood' â seafood companies
'energy' â energy / oil & gas
'shipping' â shipping companies
country: Optional â 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.
Read only
Input schema
{'type': 'object', 'required': ['query'], 'properties': {'fast': {'type': 'boolean', 'default': False, 'description': 'Skip reranking and return results ranked by hybrid score only. Faster but less precise.'}, 'limit': {'type': 'integer', 'default': 5, 'description': 'Number of results to return (1â\x80\x9320)'}, 'query': {'type': 'string', 'description': "Natural language search query, e.g. 'Equinor dividend 2024' or 'Norwegian housing market Q3'"}, 'sector': {'type': 'string', 'default': '', 'description': "Filter by sector, e.g. 'energy', 'financials', 'salmon'"}, 'source': {'type': 'string', 'default': '', 'description': "Filter by data source, e.g. 'xbrl_esef', 'newsweb', 'mfn_nordics', 'nasdaq_se'"}, 'ticker': {'type': 'string', 'default': '', 'description': "Filter by company ticker, e.g. 'EQNR', 'SALM', 'NDA'"}, 'country': {'type': 'string', 'default': '', 'description': 'Filter by country: NO, SE, DK, or FI'}, 'fiscal_year': {'type': 'integer', 'default': 0, 'description': 'Filter by fiscal year, e.g. 2024. Use 0 for no filter'}, 'report_type': {'type': 'string', 'default': '', 'description': 'Filter by type: annual_report, quarterly_report, press_release, exchange_announcement, macro_summary'}, 'max_chunk_index': {'type': 'integer', 'default': 0, 'description': 'Only return chunks with chunk_index <= this value. Use 3 to target introductory sections of long documents. Use 0 for no filter.'}}, 'additionalProperties': False}
Output schema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}}}, 'description': 'Generic wrapper for non-object return types.', 'x-fastmcp-wrap-result': True}
History
Recent tool changes
Added
analyze_company
Sept. 17, 2026, 12:40 p.m.
Added
company_research
Sept. 17, 2026, 12:40 p.m.
Added
get_current_power_price
Sept. 17, 2026, 12:40 p.m.
Added
parse_pdf_to_text
Sept. 17, 2026, 12:40 p.m.
Added
search_filings
Sept. 17, 2026, 12:40 p.m.
Added
get_company_info
Sept. 17, 2026, 12:40 p.m.
Added
ping
Sept. 17, 2026, 12:40 p.m.
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