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Get Act Structure (local module)

get_act_structure

Retrieve the hierarchical structure of an Australian Act, showing parts, divisions, sections, schedules, and clauses as a nested tree. Works offline using installed local data.

Instructions

Return the containment tree of an Act (Act -> Part -> Division -> section/schedule/clause) by walking 'act_provision' edges in an installed local data module (offline, closed-world). Returns a nested tree or a typed not-found result. Requires @duckdb/node-api and at least one installed module.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actYesAct work identity or citation
depthNoMax tree depth; default 12 (also the cycle backstop)
formatNojson
moduleNo
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that the tool returns a tree or not-found result and mentions the algorithm (walking edges) and requirements (DuckDB, installed module). However, it does not explicitly state that the tool is read-only, idempotent, or free of side effects. The transparency is adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no extraneous content. It front-loads the purpose and structure, then adds essential context (offline, closed-world) and requirements. Every sentence serves a clear function, making it highly efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, no output schema), the description covers the essential purpose and constraints but lacks details on return format structure, parameter roles for module and format, and error handling beyond a typed not-found. With no output schema, more detail on the returned tree would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50% (act and depth have descriptions, module and format do not). The tool description adds no parameter information beyond what the schema provides. It does not explain the 'module' parameter's purpose or the effect of 'format', leaving potential ambiguity for the agent. This fails to compensate for the schema's gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the containment tree of an Act by walking specific edges, and identifies the resource (Act) and action (return tree). It distinguishes from sibling tools by specifying 'installed local data module (offline, closed-world)', setting it apart from online legislation tools like search_legislation or fetch_document_text.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: it works offline with installed modules and returns a tree structure. While it doesn't explicitly name alternatives, the 'offline, closed-world' phrasing implies when to use it (when local data is available) and when not (e.g., for online or real-time data). This effectively guides selection among siblings.

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

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