assess_stage

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

No annotations are provided, so the description carries the full burden. It does disclose one meaningful behavioral trait – domain scores are deliberately not averaged (영역 점수를 평균하지 않습니다) – which tells the agent how the judgment is aggregated. It says nothing about whether the call is read-only, whether it persists state, or what the returned judgment looks like.

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?

Purpose is front-loaded in the first clause, followed by compact per-parameter annotation. The dense bracket/enum notation is information-rich but readable. Nothing is obviously padded, though the terse notation demands familiarity.

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?

For a 3-parameter judgment tool with no annotations and no output schema, the parameter documentation is solid but the definition omits what the tool returns and where it sits in the workflow relative to siblings. An agent can construct the call but is left guessing about the result and prerequisites.

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 0%, so the description must compensate, and it largely does: it defines b_stage's domain (B1~B4), points b_basis to the judgment rationale, and enumerates the domains item structure with code (D01~D08), o_stage (O1~O4), status, and confidence enum values. It stops short of covering all allowed values or edge cases, but this is well above the schema's own documentation.

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?

States a specific verb+resource: judging (판정) the consistency (정합성) of growth stage. That is clearly distinct from sibling names like assess_hypotheses or compute_metrics. However, it never names or differentiates itself from related siblings such as assess_hypotheses, so the differentiation is only implicit.

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?

There is no guidance on when to call this tool versus the many siblings (assess_hypotheses, compute_metrics, next_best_evidence). Context must be inferred entirely from the parameter names. The one implied rule ('does not average domain scores') is about computation, not about when to use the tool.

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