get_law_system_tree

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

With no annotations, the description carries full behavioral burden. It discloses endpoint sharing with a sibling and specifies the output format (indented tree of statute hierarchy). While it doesn't detail all edge cases (e.g., depth limits), it sufficiently explains the tool's behavior for an AI agent.

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 concise (two sentences plus a follow-up pointer) and front-loads the core purpose and hierarchy levels. Every sentence adds value, with no redundancy. Ideal length for quick comprehension.

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?

Despite no output schema, the description clearly explains the tree output's structure (hierarchy levels). It mentions endpoint sharing and provides next-step references, making the tool's context fully understandable for an AI agent to decide when and how to invoke it.

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% with descriptions for both parameters. The description adds value by indicating both parameters come from search_law results, providing origin context beyond the schema. This extra context justifies a score above 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 explicitly states the tool visualizes a hierarchy (법령 체계도) as a tree (트리 시각화), listing the layers from 법률 to 행정규칙. It also distinguishes itself from the sibling get_related_laws by noting the shared endpoint but different output format (tree vs. flat list), making the purpose very clear.

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 explicitly contrasts with get_related_laws: tree for hierarchy understanding vs. flat list for search/discovery. It also suggests follow-up tools (get_law_text, get_delegated_laws), providing clear when-to-use and alternatives.

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