list_models

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does this well by describing the return value: each model's id and capability flags for chat/vision, image, speech, music, video, and 3D mesh. The operation is clearly a read-only listing, and the default-model behavior adds useful context beyond the basic action.

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 three sentences with no waste: it states the purpose, details the returned capabilities, and explains how the list is used by other tools. The most important information is front-loaded.

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 zero-parameter listing tool with no output schema, the description fully covers what the agent needs: what the tool returns, which capabilities are reported, and how to integrate those ids into generation-tool calls. Nothing important is missing.

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 is empty with zero parameters, so there is nothing to explain. The description appropriately focuses on output semantics rather than parameter mechanics, matching the baseline for a parameterless tool.

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 uses a clear, specific verb ('List') and resource ('models available on the remote mlx-serve server') and expands on capabilities. It is clearly distinguishable from sibling generation and model-management tools.

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 explicitly tells the agent to pick model ids from this list for the model argument of generation tools, and clarifies that media tools without an explicit model use the server default. This is direct, actionable guidance for when to use the tool and how to use its results.

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