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andyarntsen-alt

brreg-mcp-server

sok_foretak

Search Norwegian enterprises in the Brønnøysund Register. Filter by name, industry code, municipality, organizational form, and more.

Instructions

Søk etter norske foretak i Enhetsregisteret. Kan filtrere på navn, næringskode, kommunenummer, organisasjonsform, og mer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
navnNoNavn eller del av navn på foretaket
pageNoSidenummer (starter på 0)
sizeNoAntall resultater (maks 100)
konkursNoFiltrer på om foretaket er konkurs
naeringskodeNoNACE-næringskode, f.eks. '62.010' for programmeringsvirksomhet
kommunenummerNo4-sifret kommunenummer, f.eks. '0301' for Oslo
fraAntallAnsatteNoMinimum antall ansatte
tilAntallAnsatteNoMaksimum antall ansatte
fraStiftelsesdatoNoFra stiftelsesdato (YYYY-MM-DD)
organisasjonsformNoOrganisasjonsformkode, f.eks. 'AS', 'ENK', 'NUF'
tilStiftelsesdatoNoTil stiftelsesdato (YYYY-MM-DD)
registrertIMvaregisteretNoFiltrer på MVA-registrering
fraRegistreringsdatoEnhetsregisteretNoFra registreringsdato (YYYY-MM-DD)
tilRegistreringsdatoEnhetsregisteretNoTil registreringsdato (YYYY-MM-DD)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions search and filtering, without addressing pagination behavior, return format, authentication requirements, or read-only nature. This is minimal disclosure for a tool with 14 parameters.

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 two short sentences, front-loaded with the primary action, and contains no filler words. Every word contributes to the core purpose and the structure is easy to scan.

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

Completeness3/5

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

For a 14-parameter search tool with no output schema, the description is adequate but incomplete. It gives a general sense of searchability and a few example filters but omits many available filters, pagination details, and return structure. The schema compensates for parameter specifics, so this is not severely lacking, but a more complete overview would help.

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 coverage is 100%, so the schema already documents all 14 parameters. The description reiterates a few filter names (navn, næringskode, etc.) but adds no new meaning beyond the generic 'og mer', so it does not exceed the schema baseline.

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 'Søk' (search) and a clear resource 'norske foretak i Enhetsregisteret', clearly distinguishing it from sibling tools like hent_foretak (retrieve specific) and sok_underenheter (search sub-units). It also lists key filter dimensions, leaving no ambiguity about its function.

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

Usage Guidelines3/5

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

The description implies a search use case but does not explicitly state when to use this tool over alternatives such as sok_underenheter or hent_foretak. No exclusions or alternative tool names are mentioned, so the agent must infer usage from the name and context.

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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