lazymac-k-mcp

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 mentions retrieving 'company info + status', which implies a read-only operation, but doesn't specify details like data sources, accuracy, rate limits, authentication needs, or error handling. For a lookup tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 extremely concise and front-loaded, using a single sentence that efficiently conveys the core functionality. Every word earns its place, with no wasted text, making it easy to scan and understand quickly.

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's complexity (lookup operation with potential data nuances), lack of annotations, no output schema, and vague input schema, the description is incomplete. It doesn't explain what 'company info + status' includes, possible return values, error cases, or integration context. This leaves the agent with insufficient information to use the tool effectively.

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 1 parameter with 100% description coverage, but the schema description ('Free-form params object') is vague. The tool description implies the parameter should be a business registration number, adding semantic meaning beyond the schema. However, it doesn't specify the exact parameter name or format (e.g., string, numeric), so it partially compensates but doesn't fully clarify usage.

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: looking up Korean companies using a business registration number (사업자등록번호) to retrieve company information and status. It specifies the verb 'lookup', the resource 'Korean company', and the key identifier. However, it doesn't explicitly differentiate this from sibling tools like 'k_business_validator' or 'k_public_data', which might offer overlapping functionality.

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 doesn't mention prerequisites, such as needing a valid business registration number, or compare it to siblings like 'k_business_validator' (which might validate businesses) or 'k_public_data' (which could provide broader public data). Usage is implied by the purpose but not explicitly defined.

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