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Nordic Financial MCP

by io.github.AIDataNordic

company_research

Read-only

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.

Input Schema

TableJSON 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

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior4/5

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

Discloses that results are raw chunks, queries are independent, and synthesis is caller's responsibility. Annotations already indicate readOnlyHint=true, but description adds important behavioral context like hybrid search and false positive risks without ticker.

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?

Well-structured with clear sections, but slightly long. Front-loads purpose and usage. Minor redundancy (ticker explanation appears twice) prevents perfect score.

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 complexity and presence of output schema, description covers all necessary behavioral aspects: return format, section behavior, limits, error handling implied. No gaps identified.

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%, but description greatly extends meaning: company is metadata-only, sections includes examples, optional ticker/limit fields, and clarifications. Adds value beyond 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 clearly states the tool's function: 'Run multiple targeted searches and return raw results grouped by section.' It distinguishes from siblings like search_filings and analyze_company by focusing on multi-section, raw output.

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?

Detailed guidance on when to use (multiple targeted searches, raw synthesis) and when not (use Alfred MCP for orchestrated reports). Provides explicit alternatives and instructions for query formulation, ticker usage, and section structuring.

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

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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose: analyze_company for synthesized answers, company_research for raw grouped search results, search_filings for direct database queries, get_company_info for registry data, get_current_power_price for electricity prices, parse_pdf_to_text for full document extraction, and ping for connectivity. Descriptions explicitly differentiate them, preventing confusion.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: analyze_company, company_research, get_company_info, get_current_power_price, parse_pdf_to_text, search_filings, and ping (a single word but acceptable). No mixing of conventions or inconsistent styles.

Tool Count5/5

Seven tools is well-scoped for a Nordic financial data server. Each tool serves a necessary function without redundancy or bloat. The count covers core data retrieval, analysis, registry lookup, niche data (power prices), and utility tools (PDF parsing, health check), fitting the server's purpose.

Completeness4/5

The tool set covers the main workflows: searching filings, analyzing companies, retrieving registry data, fetching power prices, and extracting full documents. A minor gap is the lack of a direct Swedish registry lookup (get_company_info excludes Sweden, and search_filings doesn't provide registration data). Otherwise, the surface is well-rounded for Nordic financial queries.

Resources