List models

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

Annotations already declare readOnlyHint=true and idempotentHint=true, clearly indicating a safe read operation. The description adds behavioral context about view defaults and recommended_for usage, enhancing transparency beyond annotations.

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 relatively short and front-loaded with the core purpose. It could be slightly more structured, but it contains no filler and every sentence adds value.

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?

Without an output schema, the description gives a high-level idea of the output (list, OpenAI-compatible shape) but does not detail the structure or fields returned. For a list tool, this is acceptable but leaves some ambiguity.

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?

All 5 parameters are well-described in the schema (100% coverage). The description adds extra meaning for view (explains defaults and use cases) and recommended_for (non-chat recommendations), providing value beyond the schema.

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 clearly states the tool lists available models, and distinguishes between views for model selection vs. full OpenAI-compatible shape. It differentiates from siblings like get_model and create tools by focusing on listing.

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?

Provides explicit guidance on when to use view=compact vs view=full, and mentions non-chat recommendations via recommended_for. However, it does not explicitly contrast with other list-like tools (e.g., list_files) or specify when not to use.

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