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AIDataNordic

Nordic Financial MCP

by AIDataNordic
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Python
Remote

Nordic Financial MCP

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

Connect

Add to your MCP client config:

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

Or with Claude Code:

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

Related MCP server: CompanyIQ MCP Server

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)

# 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

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.

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.

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.

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.

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.

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

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 — 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

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

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

Check MCP server process

ps aux | grep mcp_server.py

Check MCP query activity

# 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

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

Available Tools

6 tools
due_diligence_reportA
Read-only
Inspect

Run multiple targeted searches and return results grouped by section for due diligence.

The agent 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).

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
companyYesCompany name to research, e.g. 'Equinor', 'Norsk Hydro', 'Aker BP'
sectionsYesList 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.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint true and openWorldHint false. The description adds behavioral details: each query runs as hybrid search, ticker filter to avoid false positives, and constraints on sections and limits. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear paragraphs, but it is slightly verbose. Could be more concise while retaining all critical information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and presence of an output schema, the description covers input semantics, constraints, and rationale. It does not need to explain output format due to output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%. The description adds significant value beyond the schema: explains the structure of sections, the importance of ticker, and default limit. This helps the agent use parameters correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run multiple targeted searches and return results grouped by section for due diligence.' It uses specific verbs and nouns, and distinguishes itself from siblings like search_filings and get_company_info.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (due diligence) and provides guidance on how to formulate queries and use tickers. However, it does not explicitly mention when not to use it or name alternative siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_company_infoA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
identifierYesOrganisation number (NO: 9 digits, DK: 8 digits CVR, FI: business ID with hyphen)
countryNoTwo-letter country code: NO (default), DK, or FINO

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true; the description adds context that the data is from an official registry and is authoritative, which aligns with and reinforces the annotations without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences, front-loaded with the main purpose, each sentence providing essential guidance without any fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers scope, exclusions, and use cases adequately for a simple tool with an output schema; no missing details that affect correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage on both parameters, so the description adds minimal new meaning beyond repeating the country codes and identifier format already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Look up') and resource ('company in the official business registry'), and clearly distinguishes from siblings by explicitly stating not to use for Sweden (use search_filings) and not for tickers/ISIN.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use (retrieve authoritative registration data for a known organisation number) and when not to use (for Sweden or tickers/ISIN), providing a specific alternative (search_filings with country='SE').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_current_power_priceA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
zoneNoBidding zone: NO1–NO5, SE1–SE4, DK1, DK2, or FINO1
include_tomorrowNoAlso fetch tomorrow's prices if available (published after 13:00 CET)

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant behavioral context beyond the annotations: it specifies data sources (NordPool, hvakosterstrommen.no, ENTSO-E), the unit (EUR/kWh), and the dynamic behavior of the 'tomorrow' field (returns 'not yet available' before 13:00 CET). The annotations (readOnlyHint, openWorldHint) are consistent with this read and variable-data nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by usage guidelines, timing, and data source details. Each sentence provides unique, essential information without redundancy. Despite five sentences, it remains efficient and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, output schema exists, annotations present), the description covers all necessary aspects: purpose, usage boundaries, timing constraints, data sources, units, and zone-specific behavior. The presence of an output schema relieves the description from explaining return values, making it fully sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with descriptions for both parameters. The description adds value by explaining zone-specific data sources (Norwegian zones use hvakosterstrommen.no, others use ENTSO-E), which contextualizes the 'zone' parameter beyond the schema's simple list. This extra information slightly exceeds the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Fetch today's hourly day-ahead electricity spot prices for a Nordic bidding zone,' which clearly states the verb ('fetch'), resource ('prices'), and scope ('today's hourly day-ahead'). It distinguishes from the sibling 'search_filings' by explicitly stating when not to use this tool and directing to the alternative for historical analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('current and near-term queries') and when not to ('historical analysis'), providing a clear alternative ('use search_filings with report_type='macro_summary''). It also includes timing constraints for tomorrow's prices (published after 13:00 CET) and the resulting behavior when not available.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

parse_pdf_to_textA
Read-only
Inspect

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)".

ParametersJSON Schema
NameRequiredDescriptionDefault
pdf_urlYesDirect HTTPS URL to the PDF file

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds valuable behavioral context: not suitable for scanned PDFs without embedded text, with specific output format ('(no extractable text)') for such pages.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each adding value: action, use case with combination, warning about alternatives, and limitation. No wasted words. Well-structured with front-loaded purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists (context indicates true), the description explains return behavior (page-by-page, placeholder for non-text pages). Covers prerequisites, use cases, and limitations. Complete for a single-parameter tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter (pdf_url) with schema description 'Direct HTTPS URL to the PDF file'. Schema coverage is 100%, so description does not need to add much. Description does not repeat schema details, but it is acceptable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb (download and extract text), resource (PDF from URL), and scope (page by page). Distinguishes from sibling search_filings by stating it's for full text of a specific document.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (after search_filings for full text) and when not to use (if PDF is well-represented in database or is scanned). Names alternative (search_filings) and highlights speed advantage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pingA
Read-only
Inspect

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."

ParametersJSON Schema
NameRequiredDescriptionDefault
nameNoArbitrary label included in the response, e.g. 'healthcheck' or 'agent-1'world

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description adds that the response is a greeting string with the provided name. No contradictions. While it doesn't elaborate on potential non-response behavior, it is sufficient for a simple ping tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loads the purpose, and every sentence adds value. No redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers the tool's purpose, usage context, limitations, and return value. For a simple connectivity check, it is complete and leaves no gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameter 'name' is already well-documented in the schema. The description adds no new semantic information beyond the schema, meeting the baseline expectation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a connectivity check, and the verb 'confirm' combined with 'server process is responding' specifies the exact resource and action. It distinguishes from siblings like search_filings which test data availability.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises using at session start to verify reachability, warns against misuse as database proxy, and directs to search_filings for data health. Provides clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_filingsA
Read-only
Inspect

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".

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query, e.g. 'Equinor dividend 2024' or 'Norwegian housing market Q3'
tickerNoFilter by company ticker, e.g. 'EQNR', 'SALM', 'NDA'
fiscal_yearNoFilter by fiscal year, e.g. 2024. Use 0 for no filter
report_typeNoFilter by type: annual_report, quarterly_report, press_release, exchange_announcement, macro_summary
sectorNoFilter by sector, e.g. 'energy', 'financials', 'salmon'
countryNoFilter by country: NO, SE, DK, or FI
limitNoNumber of results to return (1–20)

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true; description adds that results are chunked text excerpts, not full documents, and notes database size. No contradictions with annotations, but could further detail result structure or pagination.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is longer but well-structured with sections and bullet points. It is informative without verbosity; each part serves a purpose. Could be slightly tighter but the complexity justifies length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given existence of output schema, description covers domain, content types, exclusions, and filtering guidance thoroughly. Provides practical examples and business context for Nordic markets, making it complete for an agent to use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage, so baseline is 3. Description adds value with example queries, enumeration of report_types, and specific macro usage instructions, going beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool searches for company filings, press releases, and macroeconomic summaries in the Nordic financial database. It distinguishes itself from siblings by specifying when to use alternative tools (parse_pdf_to_text for full docs, get_company_info for Swedish registration).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises it as the primary tool for Nordic queries and provides clear exclusions: not for full documents (use parse_pdf_to_text) and not for Swedish company registration (use get_company_info). Also gives specific filter guidance for macro summaries with report_type and country.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updatesv1.0.28
    • Addeddue_diligence_report
    • Changedparse_pdf_to_text1 field changed
      • removedOutput schema / description
        Removed value: -"Generic wrapper for non-object return types."
    • Changedping1 field changed
      • removedOutput schema / description
        Removed value: -"Generic wrapper for non-object return types."
    • Changedsearch_filings1 field changed
      • removedOutput schema / description
        Removed value: -"Generic wrapper for non-object return types."
  2. 1 tool updatev1.0.20
    • Changedping1 field changed
      • changedInput schema / properties / name / description
        Previous value: -"Name to include in the greeting"New value: +"Arbitrary label included in the response, e.g. 'healthcheck' or 'agent-1'"
  3. 5 tool updatesv1.0.7
    • First observedget_company_info
    • First observedget_current_power_price
    • First observedparse_pdf_to_text
    • First observedping
    • First observedsearch_filings
Deploy Server
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TDQS

A4.2/5.0

Scored across 6 tools

Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness3/5

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.

Maintenance

ActivityInactive
ResponsivenessResponsive

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