Czech Company Registry (ARES) Tools

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

With no annotations, the description carries the full behavioral burden. It does disclose the core transformation and the code components returned, but it says nothing about failure on unresolvable addresses, ambiguity handling, or whether any external state is affected.

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?

One compact sentence with no filler; the transformation is front-loaded and the parenthetical adds precise output details. Every token contributes to the tool's meaning.

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 single parameter and the explicit output components, an agent can reasonably call the tool. However, with no output schema, the description leaves some ambiguity about the exact return structure and how invalid or unresolvable addresses are handled.

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 input schema fully documents the single address parameter, including a concrete example, so the baseline is 3. The phrase 'free-text Czech address' reinforces the expected input format but adds no schema-independent detail.

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 names a concrete transformation (free-text Czech address to RÚIAN-standardized form) and specifies the output artifacts (full KOD ADM, codes for obec/část/ulice, correct PSC). This clearly distinguishes it from siblings focused on VAT, company lookup, or entity status.

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 use case is only implied by the description: it is for standardizing free-text Czech addresses, which none of the siblings cover. There is no explicit when-to-use or when-not-to-use statement, nor any alternative routing.

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

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

No annotations are provided, so the description carries the full burden. It claims the data is 'Authoritative' and from the RZP register, but does not disclose behavioral traits beyond the schema, such as read-only nature, error handling, or rate limits. The batch limit (up to 20) is already in the schema, so the description adds minimal behavioral context.

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, with the core purpose and output fields front-loaded, followed by usage context. There is no redundant wording, making it efficient and easy to parse.

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 simple single-parameter tool with no output schema, the description lists the output fields (payer yes/no, effective from, tax office, trade licences) and the register source, which helps an agent understand the response. It lacks details like error handling or rate limits, but those are less critical for a read-only status check.

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?

Schema description coverage is 100%, so the baseline is 3. The description repeats the batch limit (up to 20) which is already in the schema, but adds no extra meaning about the ids parameter beyond what the schema provides.

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 title and description clearly state the tool's purpose: checking Czech VAT (DPH) status. The description specifies the resource (RZP register) and the exact data returned (payer yes/no, effective from, tax office, trade licences). This distinguishes it from siblings like entity_status or company_lookup, which are broader in scope.

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 provides a clear use case: 'Use before issuing/czech invoicing.' However, it does not explicitly state when not to use the tool or mention alternatives among the sibling tools, such as company_lookup for general company information. This gives context but lacks exclusions.

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

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

No annotations exist, so the description carries the full burden. 'Lookup' clearly indicates a non-mutating read, but nothing is disclosed about not-found behavior, rate limits, authentication needs, or response shape. This is adequate for a simple read operation but leaves notable gaps.

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 sentences with no filler: the first lists the returned fields, the second specifies the input format. Everything earns its place and the key purpose is front-loaded.

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 simple one-parameter lookup with no output schema, the description covers the essentials: what the tool returns and what input it accepts. It stops short of explaining error/not-found behavior or explicitly routing to sibling tools, but given the low complexity, it is largely 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?

Schema coverage is 100% (the id property already includes examples), so baseline is 3. The description adds value by explicitly stating the format constraints ('IČO (8 digits) or DIČ (CZ+10)'), which reinforces and clarifies the schema's examples beyond a mere restatement.

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?

States a specific verb ('lookup'), resource ('Czech company' in ARES), and the returned fields (name, legal form, address, NACE, data-box, VAT DIČ). This clearly distinguishes it from siblings like search_company (search by other criteria) and check_vat (VAT-specific) without needing to inspect schemas.

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?

Implies usage when you have an IČO or DIČ and need basic register data, but gives no explicit 'use this when' or 'not when' guidance. It does not mention alternatives such as check_vat or search_company, leaving the agent to infer which tool fits which scenario.

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

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. While 'snapshot' implies a read-only operation, the description does not explicitly confirm it is non-destructive, nor does it mention potential errors (e.g., entity not found), authorization requirements, or rate limits. It adds minimal context beyond the basic purpose.

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, compact sentence that front-loads the core concept ('KYC-style snapshot') and immediately enumerates the key data points (active/cancelled, registers, insolvency record). It is entirely free of filler and efficiently communicates the tool's essence.

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 tool has only one parameter and no output schema, the description must convey what the snapshot returns. It lists the specific registers (VR, RZP, NRPZS, etc.), the active/cancelled status, and the conditional insolvency record, which covers the main expected outputs. However, it does not specify the response format (e.g., JSON structure) or handle edge cases like missing entity, which slightly limits completeness.

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 input schema fully documents the single parameter 'id' as 'IČO or DIČ' (100% coverage). The description adds no additional meaning to this parameter—it does not clarify format expectations, examples, or variations. With high schema coverage, the baseline is 3, and the description does not elevate it.

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's purpose: a KYC-style snapshot of entity status (active/cancelled), the registers it appears in (with specific register abbreviations), and an insolvency-register record if present. It specifies the resource (entity) and the exact data categories returned, distinguishing it from sibling tools like company_lookup or validate_cz which serve different lookup/validation purposes.

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 does not provide any guidance on when to use this tool versus its siblings (address_check, check_vat, company_lookup, search_company, validate_cz). It only describes what it does, leaving the agent to infer that it is appropriate for risk assessment or register lookups. There is no mention of alternatives or exclusions.

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

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

With no annotations provided, the description carries the full transparency burden. It discloses meaningful operational details: prefix matching, case-insensitivity, absence of wildcards, and the shape of the return payload. It does not mention rate limits or error behavior, but for a public read-only search tool the disclosed semantics give an agent realistic expectations.

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 dense sentences with zero filler. The core behavior and return value are front-loaded, and the response-type hint ('for further lookups') is included without redundancy. Every phrase earns its place.

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 simple 2-parameter search tool with no output schema, the description supplies the essential missing details: the matching rule and the high-level return fields. It could explicitly expand 'IČOs' for non-Czech agents or describe the result structure, but the description is sufficient for an agent to invoke it and interpret the response at a workflow level.

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?

Schema description coverage is 100% for both parameters, so the baseline is 3. The description's 'prefix match' phrase adds no new meaning because the schema already labels name as 'Company name or name prefix'. The limit parameter is fully documented in the schema, so the description need not repeat it.

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?

States a specific verb ('search'), a specific resource (Czech companies in ARES), and precise matching behavior (prefix match, case-insensitive, no wildcards). It also lists what the tool returns (IČOs, names, addresses), making its function distinct from likely siblings like check_vat or company_lookup without needing to open their schemas.

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?

Conveys clear usage context: use this when you have a company name and need identifiers/addresses to feed 'further lookups'. It stops short of explicitly naming sibling alternatives or exclusion conditions, but the search-focused wording and the ARES source give the agent enough behavioral context 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.

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

With no annotations, the description carries the burden. It usefully discloses that validation is checksum/format only and purely local, but it does not describe what the function returns for valid vs invalid inputs, how multiple/empty parameters are handled, or any explicit side-effect guarantees. Basic transparency, but incomplete.

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 efficient, front-loaded sentences with no redundancy. The validation target, identifier list, and offline property are all conveyed in minimal space.

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?

The description covers all four identifier types and their validation algorithms, but because there is no output schema it omits the return shape and success/failure signaling. It also leaves unclear that, given zero required parameters, at least one identifier should be supplied, making the calling contract incomplete.

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?

Schema coverage is 0%, so the description must compensate. It adds algorithm-level meaning: IČO mod-11, DIČ as CZ+IČO or birth number, IBAN mod-97 CZ-specific, and variable symbol. It could be stronger by explicitly mapping each algorithm to the exact schema property names and noting that at least one parameter should be provided.

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 a specific action (validate) and resource (Czech identifiers), enumerates the exact types (IČO, DIČ, IBAN, variable symbol), and marks the operation as offline. This distinguishes it from network-based siblings like check_vat, company_lookup, or search_company.

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 phrase 'Pure local, no network' implies this is the offline formatting/checksum alternative to online lookup tools, giving clear usage context. It does not explicitly name sibling alternatives or state when not to use it, so it falls just short of a 5.

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