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

Beyond the readOnly/non-destructive annotations, the description discloses non-obvious behavior: similar datasets are folded together under `group=True` and expanded via `group_ids` with `show`, `n` is capped at 20, synonyms are auto-applied (expanded_terms), and region_gap/region_fallback/scope_warning signals are emitted when regional data is missing or out of scope. This is substantive context the annotations cannot carry.

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 bullet structure is scannable and the routing rule is front-loaded, but the entire content is duplicated in Korean and English, roughly doubling length. It also re-describes the return payload in the 반환 section even though an output schema exists, which is redundant by the stated criteria.

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 6-parameter search tool with an output schema, the description covers routing, parameter meaning, folding/expansion mechanics, region fallback behavior, and the recommended next step (`show`). Nothing an agent needs to invoke or chain this tool 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 description coverage is 0%, so the description carries the full burden, and it mostly succeeds: it explains `fields` (topic+column combination), `group` (folding behavior and default), `n` (max 20), `dtype` (API/FILE), and `org` (institution), plus the required `query` via worked examples. Only minor gaps remain (e.g., exact enum values for dtype).

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 and resource in both Korean and English ("주제로 공공데이터를 찾습니다" / "Search Korean public datasets by topic") and immediately distinguishes itself from the `fields` sibling by scope: topic-based search vs. column-only filtering. An agent can separate `search` from `fields` without opening either schema.

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?

Explicitly states the routing rule: topic + column constraints go here with `fields=[...]`, while column-only queries belong to the `fields` tool. It also adds concrete example queries and notes that supplying dtype (API/FILE) and org improves accuracy, covering both when-to-use and how-to-use.

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