Validate a Czech DIČ (VAT ID). Format check: "CZ" + 8-10 digits. For 8-digit tail (legal entities) also runs MOD11 checksum against the embedded IČO.
| Name | Required | Description | Default |
|---|---|---|---|
| dic | Yes | Czech DIČ — e.g., "CZ26168685". Whitespace and case tolerated. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true, and the description adds valuable behavioral details beyond that: the format check for 'CZ'+8-10 digits and MOD11 checksum for 8-digit tails. This gives the agent a clear picture of what happens during validation with no contradiction to annotations.
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 two sentences, front-loaded with the primary purpose, and includes only relevant technical details. No filler or redundancy.
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?
Given the simple input schema and annotations, the description covers the core behavior well. However, since there is no output schema, the description does not mention what the tool returns (e.g., boolean, validation errors), which is a gap in completeness for an agent.
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 schema fully describes the parameter 'dic' with an example and format tolerances, so schema coverage is 100%. The description reinforces the format but does not add new parameter-level meaning beyond what the schema already provides, earning a baseline 3.
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 clearly states the tool validates a Czech DIČ (VAT ID) with a verb+resource structure and gives specific details about format and checksum. It is immediately understandable, though it does not explicitly differentiate from the sibling tool check_vat_payer, which also relates to VAT validation.
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?
No guidance is provided on when to use this tool versus alternatives. The description implies usage (validate a DIČ) but does not mention check_vat_payer or any exclusion criteria, leaving the agent to infer which tool is 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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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.