get_models_pricing

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

There are no annotations, so the description must carry the behavioral disclosure burden. It clearly indicates a read/listing operation and reveals the default behavior of returning all models when no type is specified. It does not mention return format, pagination, or whether some models might be omitted, but for a simple listing tool this is arguably adequate.

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 a single compact sentence that front-loads the tool's purpose and immediately follows with the key default behavior. There is no redundant or filler content; every phrase earns its place.

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?

With no output schema, the description partially explains the return content: models, credit prices, and per-model input_schema. However, it does not describe the response structure, units, or how the listed models are organized, leaving some ambiguity for an agent that must parse the result.

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?

The input schema already fully documents the single optional 'type' parameter with an enum and description. The description adds meaningful behavior beyond the schema by explicitly stating that omitting 'type' returns all models, which helps an agent understand the default semantics.

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 clear verb ('Daftar' = list) and resource (models with credit prices and supported input_schema), which is specific. It subtly differentiates from the sibling 'list_models' by emphasizing pricing and per-model parameter schema, though it does not explicitly contrast them.

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 provides useful usage context for the optional 'type' parameter via 'Tanpa type = semua model' (without type = all models). However, it gives no explicit guidance on when to use this tool instead of sibling 'list_models' or other generation tools, leaving the selection partially to inference.

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