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

Beyond the read-only/idempotent annotations, it discloses that every answer carries `superseded_by`, that a silent domain falls back to BASE and is flagged with `base_layer_hit` (excluded from headline counts), that the dataset has no expert review, and that `found: true` means only a code match exists. These are exactly the caveats an agent needs to avoid over-reading a result.

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 sentence and the density is high, with almost every clause carrying a caveat the agent needs. It is a single dense block rather than a structured list, and the trailing CC BY 4.0 note is marginal, but little is genuinely wasted.

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?

There is no output schema, so the description carries the burden of explaining the return value — and it does: direction, strength weight scale (3/2/1), scope (전칭/한정), grounding rule ids, citation, plus the lookup_context fields. Together with the BASE fallback rule, nothing essential for correct interpretation is missing.

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 already 100%, so the baseline is 3; the description earns above baseline by clarifying that `domain` echoes the request while `lookup_context.answer_domain` shows where the answer came from, and that the `condition` axes are never actually recorded in v0.9. That is semantic value the schema cannot express.

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 states a concrete verb and resource: ask the kr-lnpd@0.9 reference dataset which of two AIO value codes Korean statutes rank first within a domain. It also scopes the source (statute-only, not court decisions), which implicitly separates it from the org-priority sibling. It stops short of naming `aio_lookup_org_priority` as the counterpart, so the differentiation is inferable rather than explicit.

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?

It tells the agent this is the legacy September 2026 statute-only edition and points to the current unified hierarchy with a concrete URL and data path, which is real routing guidance. It does not state a crisp 'use tool X instead when Y', so the agent must draw the conclusion itself.

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