unload_model

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

No annotations are present, so the description carries the full burden. It goes well beyond the tool name by disclosing that unloading frees GPU memory, does not deregister the model, only evicts resident weights, and that the model can later be reloaded. This gives an agent an accurate mental model of the side effects.

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 compact and front-loaded: purpose, behavioral consequence, and usage context appear in three tight sentences. Every sentence adds value and none repeat schema or annotation data.

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 unload operation, the description covers what happens, what remains after the operation, and when it is useful. It does not mention return values or error behavior, which could matter without an output schema, but the core information needed to select and invoke the tool is present.

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 only defines a required 'model' string with no description, and schema description coverage is 0%. The description adds some semantics by implying the model is a registered model on the server, but it never specifies how the model identifier should be supplied or where it comes from. Given a single obvious parameter, the description partially compensates but does not fully document the expected value.

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 starts with a specific verb and resource: 'Free a model's GPU memory on the remote mlx-serve server.' It clearly differentiates from siblings like load_model by explaining that the model stays registered and only resident weights are evicted. The behavior is unambiguous and not a tautology.

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 gives a clear usage context: 'Useful after heavy media generation to reclaim unified memory.' It implies the relationship with load_model by noting the model can be reloaded later, but it does not explicitly name when not to use it or provide an alternative tool route. This is clear context without formal exclusions.

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