Handelsregister

List sectors & value lists (AT)

list_sectors

Read-onlyIdempotent

The value lists for filtering - legal forms, size classes - and, with a query word, the matching OENACE sectors with the exact nace_* filter to use. Read-only.

    Parameters:
    - query (optional): a category word (e.g. "Brauerei", "software"); returns ``matches``
      with each sector's level, code, label and the nace_* filter value for search_companies.
      Omitted: the legal-form and size-class value lists with counts.

    Served from the Austrian backend; the industry codes are OENACE == WZ == NACE Rev. 2,
    identical for both countries, so a resolved code also filters the German register. The
    value lists and counts themselves are Austrian (a notice says so). It returns no company
    data - call it once up front to resolve a filter value.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 11, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already cover read-only/idempotent/no-destruction, so the description's 'Read-only' is redundant. It does add real context the annotations cannot: data is served from the Austrian backend, the value lists and counts are Austrian-specific (with an in-response notice), while OENACE/WZ/NACE Rev.2 codes are country-identical. Missing only notes on response size/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?

Front-loaded with purpose and the key scoping caveat, then a clean Parameters block. Most sentences earn their place, though the backend/NACE paragraph is slightly dense and could be tightened.

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?

An output schema exists, so return values need less exposition, yet the description still maps the 'matches' shape and the omitted-query case. Combined with the provenance caveat, an agent has everything needed to call it correctly and interpret results.

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 description coverage is 0%, so the description carries the load. It documents the single optional parameter, gives example category words, and explains the two distinct return shapes depending on whether query is supplied — substantially more than the bare 'query: string|null' schema provides.

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?

States a concrete verb and resource (list sectors and value lists for filtering) and explicitly contrasts itself with search_companies, which consumes the resolved nace_* filter. An agent can distinguish it from every sibling without opening a schema.

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?

Gives explicit when-to-use guidance ('call it once up front to resolve a filter value') and a when-not signal ('It returns no company data'), plus the alternative that does the actual search. The optional-vs-omitted query behavior is spelled out.

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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