lazymac-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 fails to describe how the tool operates (e.g., read-only vs. mutative, authentication needs, rate limits, or data freshness). The vague mention of 'Korean financial data' offers no insight into behavioral traits beyond the resource scope.

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 very concise—a single phrase listing data types—with no wasted words. It's front-loaded with the core information, though it could be more structured by including a verb. The brevity is efficient but risks under-specification.

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 implied by financial data and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or any behavioral aspects like error handling. For a tool with no structured support, this leaves significant gaps for an agent to use it 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 100% description coverage, documenting a single 'params' object as free-form for GET/POST usage. The description adds no parameter-specific details beyond what the schema provides, such as example keys or data formats. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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 provides Korean financial data covering KOSPI/KOSDAQ, stocks, FX, and Kimchi Premium, which is specific about the resource domain. However, it lacks a clear verb (e.g., 'fetch', 'retrieve', 'analyze') and doesn't differentiate from sibling tools like 'k_exchange_rate' or 'k_public_data', which may overlap in financial data 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 no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, leaving the agent to guess based on the data types listed. No explicit comparison with sibling tools like 'k_exchange_rate' is included.

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