Get transparent bank accounts published for this company (only available for VAT-registered subjects). Useful to verify payment details on an invoice match the company.
| 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 readOnlyHint and openWorldHint. The description adds an important non-obvious constraint: availability is limited to VAT-registered subjects, and data is 'published' transparent accounts. This goes beyond the annotations without contradicting them.
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?
Two short sentences, no unnecessary words. The core action is front-loaded, and the use case is stated efficiently.
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 single-parameter, read-only tool with no output schema, the description fully covers purpose, usage context, and a key eligibility constraint. It does not describe the return format, but the tool is simple enough that this is not a major gap.
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 only parameter 'ico' is fully described in the schema (100% coverage), specifying Czech IČO with 7-8 digits. The description does not add further parameter details, but the schema already carries the semantic weight, so baseline 3 applies.
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 uses a specific verb 'Get' and clearly identifies the resource 'transparent bank accounts' scoped to 'this company'. It is clearly distinct from sibling tools like check_vat_payer or get_statutaries, which focus on other data.
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?
Provides a concrete use case ('verify payment details on an invoice match the company') and a key prerequisite ('only available for VAT-registered subjects'). It does not explicitly name alternatives, but the sibling tools address different resources, so the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.