Skip to main content
Glama
jkbngb

agentic-firmenbuch

by jkbngb
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

List sectors & legal forms

list_sectors
Read-onlyIdempotent

Discover valid sector, legal-form, and company-size filter values with counts, and map German sector terms to ÖNACE codes before querying Austria's Firmenbuch.

Instructions

Valid filter values for search_companies, with company counts. Read-only.

    Optional `query`: a German category word or phrase ("Brauereien", "Steuerberater",
    "Softwarefirma") -> `matches` with the ÖNACE level, code, official label, an activity
    concept and the search_companies filter to use (oenace_section/division/group). Use this
    instead of guessing a code; a concept-only match means "use filters.query".

    Returns the legal-form (Rechtsform) codes and the size-class (`gkl`: W/K/M/G) values
    present in the served dataset, each with its count. Call this first to discover the real
    `legal_form` / `size_gkl` values to pass to search_companies or get_cohort_summary, instead
    of guessing codes. For region/format coverage instead, use get_coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false. The description adds valuable behavioral context beyond these: it discloses return structure (matches with ONACE level, code, label, activity concept, filter), explains the concept-only match semantics, and clarifies that counts reflect the served dataset. This is substantive transparency beyond annotations.

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?

Reasonably structured with front-loaded purpose and clear sections, but notably lengthy for a single-parameter tool. Every sentence serves a purpose, yet the density and use of parentheses/arrows may reduce scannability. The core value is present without excessive waste.

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?

Despite having only one optional parameter, the description configures how to interpret results (concept-only matches), when to call it (first, before guessing), and where to go for complementary coverage. Given the tool's discovery role and an output schema present, the description provides sufficient context without needing to explain return values in detail.

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 description coverage is 0%, so the description must carry parameter meaning. It does so thoroughly: the optional query parameter accepts 'a German category word or phrase' with concrete examples (Brauereien, Steuerberater, Softwarefirma), and explains the match structure returned. It also documents the enum-like values for size-class (W/K/M/G). This fully compensates for the schema gap.

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 specific purpose -- valid filter values for search_companies with counts -- and names the sibling it serves. Distinguishes itself from get_coverage explicitly for region/format coverage. An agent can identify its role in the tooling ecosystem without reading the 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?

Explicitly instructs to 'call this first to discover real legal_form/size_gkl values' before using search_companies or get_cohort_summary, and directs to get_coverage for region/format coverage instead. Provides clear when-to-use and alternative-routing guidance.

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

Deploy Server

Other Tools