k_business_validator

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 behavioral disclosure. It states what the tool does but does not explain how it behaves: it doesn't specify validation rules (e.g., format checks, API calls), error handling, rate limits, authentication needs, or output format. For a validation tool with zero annotation coverage, this leaves critical behavioral traits undocumented.

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 concise and front-loaded, consisting of a single, clear sentence: 'Validate Korean BRN (사업자등록번호), corp registration, phones'. It efficiently communicates the core purpose without unnecessary words, though it could benefit from additional context to improve completeness.

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 complexity of validation tasks and the lack of annotations and output schema, the description is incomplete. It does not explain what validation entails, what the output looks like (e.g., success/failure, error messages), or how to interpret results. For a tool with no structured behavioral or output information, the description should provide more context to be fully helpful.

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 has 100% description coverage, documenting that 'params' is a free-form object passed as query string or JSON body. The description does not add any parameter-specific details beyond this schema, such as expected fields or examples for validation inputs. With high schema coverage, the baseline score is 3, as the schema handles parameter documentation adequately.

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: 'Validate Korean BRN (사업자등록번호), corp registration, phones'. It specifies the verb ('validate') and the resources (Korean business registration numbers, corporate registrations, phones), making the intent unambiguous. However, it does not differentiate this tool from potential sibling tools like 'email_validator' or 'json_schema_validator', which also perform validation tasks, so it lacks sibling differentiation.

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 no guidance on when to use this tool versus alternatives. It does not mention any specific contexts, prerequisites, or exclusions for validation, nor does it refer to sibling tools like 'k_company_lookup' or 'k_public_data' that might handle related data. Without such guidance, users must infer usage based on the tool name alone.

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