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

The annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered elsewhere. The description contributes that the content originates from a specific source document and enumerates what the answer contains (definition, formula, non-computation conditions), which is genuine added context. It does not say what happens when a metric name is unrecognized (error vs. nearest match vs. suggestion list), which is the one behavior an agent would most want to know.

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?

Two short fragments with zero filler, front-loading the returned content before the examples. Every clause carries information; nothing could be cut without loss.

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?

With one parameter, full schema coverage, and no output schema, the description does the necessary work by telling the agent what the response will contain. The remaining gap is edge-case behavior for unknown or ambiguous metric names, which matters for a lookup tool but is a minor omission at this complexity.

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 coverage is 100% and the single parameter already documents that Korean or English metric names are accepted. The description's examples overlap heavily with the schema's own example list (expected_yield, market_ratio), adding little beyond it. Baseline 3 applies when the schema does the heavy lifting.

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 names a specific resource and its return content: metric definitions, formulas, and conditions under which a metric is not produced, sourced from the llms.txt 'metric definitions' original text. The verb is implicit (the tool name 'explain_metric' carries it), but an agent reading this knows exactly what it retrieves. The sibling tools (court/bankruptcy/onbid search) are clearly a different domain, so confusion risk is low even without an explicit contrast.

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?

Usable examples (expected_yield, market_ratio, 실부담) imply the expected query terms, and the 'llms.txt 「지표 정의」 원문' note implies this is the canonical lookup for metric semantics. However, there is no explicit statement of when to call this versus the sibling search tools, nor any prerequisite or exclusion stated.

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