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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds meaningful beyond-annotation context: it returns only candidates, not a final engine decision, and enumerates the five methods included. That is useful behavioral disclosure not present in the annotations.

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 tight sentences. It front-loads the core resource and scope, adds the critical 'no final engine decision' caveat, and includes practical query examples without any filler.

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 rich schema, annotations, and output schema, the description is nearly complete: it states the tool's purpose, scope, methods, non-decision behavior, and example user intents. It does not explicitly situate itself against each sibling tool, but the provided usage triggers reduce ambiguity enough.

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 input schema already documents all 15 parameters and their constraints. The description adds no parameter-level meaning beyond the schema, which matches the baseline of 3.

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 identifies the resource: classical Yongshin (favorable element) candidates from five named methods, with each method's pick. It also explicitly states what the tool does not do ('No final engine decision'), which distinguishes it from any final-answer sibling and makes its scope unambiguous.

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 gives concrete user-intent triggers ('Use for 'my favorable element candidates', '용신 후보''), which tells an agent when to invoke this tool. It does not explicitly name sibling tools to avoid or state when not to use it, so it falls just short of full usage guidance.

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