list_models

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

No annotations are provided, so the description carries the behavioral burden. It communicates that the output is a summary ('ringkas') and scoped to available models, but it does not disclose output shape, type-filtering behavior, pagination, auth requirements, or rate limits. Adequate but with notable gaps.

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?

Two short sentences with zero padding: the first states the purpose, the second routes to the pricing sibling. The key information is front-loaded and every word 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?

For a simple one-parameter list tool, the description plus the input schema is nearly sufficient for correct invocation. It directs agents to get_models_pricing when richer data is needed. The main gap is the lack of an explicit return structure, but the low complexity and 'ringkas' qualifier mitigate that.

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 100% and the single 'type' parameter already has an enum and description. The tool description adds no parameter-level detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 specifies the verb ('Daftar') and resource ('model AI yang tersedia di Bumi Digital'), and explicitly marks the output as '(ringkas)' or summarized. It also distinguishes itself from get_models_pricing, so an agent can select the right sibling without opening schemas.

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?

It explicitly states the alternative tool and the condition for using it: 'Untuk detail parameter & harga gunakan get_models_pricing.' This gives clear when-not-to-use guidance and names the sibling, leaving little to inference.

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