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regdata_germany_handelsregister

Search the German Handelsregister by company name or register number to extract officers, capital, and legal form as structured JSON for KYC and due diligence.

Instructions

Germany Handelsregister Scraper. Search the German Commercial Register (Handelsregister) by company name or register number. Extract company data, officers / managing directors (Geschäftsführer) with dates of birth, capital, legal form, seat, business purpose. No official API - structured JSON for KYC, KYB & due diligence. Advanced fields beyond this schema are also accepted (regdata_describe lists them).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxItemsNoOptional cap on billed dataset items returned.
exactMatchNoMatch the exact company name (genaue Firmenbezeichnung) instead of keyword containment. Reduces noise for precise lookups.
maxResultsNoMaximum number of matching companies to return structured data for (from the first results page). 1-30.
searchQueryNoCompany name or keyword(s) to search for (Schlagwörter). E.g. "Zalando SE" or "BioNTech".
registerNumberNoOptional commercial register number to narrow the search, e.g. "215511" (HRB). Must be combined with a company name/keyword - the portal does not allow number-only searches.
Behavior2/5

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

With no annotations provided, the description carries full burden. It mentions 'No official API' and that advanced fields are accepted via regdata_describe, but lacks details on rate limits, pagination, data freshness, or error behavior.

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 relatively concise and front-loaded with purpose. It efficiently covers key points without excessive verbosity, though it could be slightly tighter.

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?

No output schema; description partially covers return values by listing extracted fields. However, it lacks details on pagination, error handling, and usage limits, making it adequate but not comprehensive.

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 parameters are well-documented in the schema. The description adds context about advanced fields being accepted, but does not elaborate on parameter usage beyond what the schema provides.

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

Purpose4/5

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

The description clearly states the tool searches the German Commercial Register and extracts specific data like officers and capital. However, it does not explicitly differentiate from sibling tools like regdata_austria_ediktsdatei, relying on the tool name for country distinction.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. No when-not scenarios or prerequisites mentioned. The description only states what the tool does, not when it's appropriate.

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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