Check whether a Czech company is a registered VAT payer (plátce DPH). If yes, returns DIČ, financial office, and any transparent bank accounts (payment details).
| Name | Required | Description | Default |
|---|---|---|---|
| ico | Yes | Czech IČO (7-8 digits). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only and open-world, so the safety profile is covered. The description adds conditional behavior ('If yes') and specifies the returned fields (DIČ, financial office, transparent bank accounts), which is valuable beyond annotations. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that immediately states the action and output. It is concise and well-structured, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with one parameter, the description covers the core behavior and outputs, complemented by schema and annotations. It is complete enough for an agent to understand the tool's purpose and expected results, though it does not explicitly state what happens when the company is not a VAT payer (e.g., return type), which would make it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'ico' is fully documented in the schema with its description 'Czech IČO (7-8 digits)' (100% coverage). The tool description does not add further parameter details, so it relies on the schema; baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the exact purpose: checking VAT payer status for a Czech company, with a specific verb ('Check') and resource ('Czech company'). It distinguishes itself from siblings like validate_dic (which validates DIČ format) and lookup_by_ico (general lookup) by focusing on VAT payer status and the specific output fields (DIČ, financial office, bank accounts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use it: when you need to determine VAT payer registration and associated details. However, it does not explicitly mention alternatives or exclusion scenarios, such as when to prefer validate_dic for DIČ validation, which is a minor gap. The context is clear enough for an agent to select it appropriately.
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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Each tool targets a distinct aspect of Czech company data: VAT status, bank accounts, historical changes, statutory bodies, basic lookup by IČO, address search, NACE search, combined search, DIČ validation, and monitoring. No significant overlap.
All tools use consistent snake_case and follow a verb_noun pattern (e.g., check_vat_payer, get_statutaries, search_companies). No mixing of conventions.
10 tools is appropriate for the domain of Czech company information, covering lookups, searches, VAT checks, history, and monitoring. Not excessive nor insufficient.
Core operations for the ARES domain are covered: lookup, search, VAT and DIČ checks, history, and statutory data. The watch_entity tool is a stub, and there might be missing advanced queries, but the set is largely complete for intended use.