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Glama

list_vertical_variables

Retrieve breakdown categories for Indonesian statistical variables, such as gender or age groups, to analyze data by specific dimensions using BPS official data.

Instructions

Daftar variabel vertikal (breakdown/disaggregasi) untuk variabel tertentu. Contoh: jenis kelamin, kelompok umur.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoKode domain BPS0000
varNoID variabel untuk melihat vertikal variabelnya
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('list'), which implies it's non-destructive, but doesn't address other behavioral aspects like authentication needs, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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

Conciseness4/5

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

The description is concise and front-loaded, consisting of one clear sentence stating the purpose followed by examples. There's no wasted text or redundancy. However, it could be slightly improved by structuring it to explicitly separate purpose from examples, but it's already efficient and easy 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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on behavior, usage context, and output. Without annotations or an output schema, the agent must infer these aspects, making the description incomplete for optimal tool invocation. It meets the minimum viable standard but has clear gaps.

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

Parameters3/5

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

The schema description coverage is 100%, with both parameters ('domain' and 'var') documented in the schema. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't clarify what 'vertical variables' means in relation to the 'var' parameter). According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Daftar variabel vertikal (breakdown/disaggregasi) untuk variabel tertentu' (List vertical variables/breakdowns/disaggregations for a specific variable). It specifies the verb ('list') and resource ('vertical variables'), and provides concrete examples ('jenis kelamin, kelompok umur' - gender, age groups). However, it doesn't explicitly differentiate from sibling tools like 'list_variables' or 'list_derived_variables', which is why it doesn't reach a score of 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_variables' (which might list all variables) or 'list_derived_variables' (which might list derived variables), nor does it specify prerequisites or exclusions. The only implied usage is for getting breakdowns of a specific variable, but this is basic and lacks context for tool selection.

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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