lazymac-k-mcp

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It lists data types but doesn't disclose how the tool behaves—e.g., whether it fetches real-time or historical data, requires authentication, has rate limits, or returns structured vs. raw data. This leaves critical operational traits unclear for a financial data tool.

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, efficient phrase listing key domains without redundancy. It's appropriately sized and front-loaded with the core topic ('Korean financial data'), though it could be more structured (e.g., separating data types with commas or bullets). Every word earns its place, but minor improvements in formatting could enhance clarity.

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 financial data tools, no annotations, no output schema, and a free-form parameter, the description is incomplete. It doesn't explain return values, error handling, or usage constraints, leaving significant gaps for an agent to operate effectively. The listed domains are insufficient to compensate for missing behavioral and output details.

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%, with one parameter ('params') documented as a free-form object. The description adds no parameter-specific semantics beyond implying financial domains, which doesn't clarify how to structure the params object. Baseline 3 is appropriate as the schema handles documentation, but the description fails to compensate with meaningful context for the free-form input.

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 lists domains (Korean financial data, stocks, FX, Kimchi Premium, news) but lacks a specific verb or action. It states what data is available rather than what the tool does (e.g., retrieve, analyze, or monitor). This makes the purpose vague, though it distinguishes from non-financial siblings like address or privacy tools.

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. It doesn't mention sibling tools like k_exchange_rate or k_public_data, leaving the agent to guess based on domain overlap. The description implies financial data contexts but offers no explicit usage rules or exclusions.

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