unload_local_model

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

No annotations are provided, so the description carries the full burden. It states the effect (free memory/VRAM) but does not disclose reversibility, permission requirements, or any side effects beyond memory release. This is a significant gap for a mutation tool.

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, front-loaded sentence with no redundant words. It efficiently states the action and purpose.

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 tool with one parameter and no output schema, the description covers the core purpose. However, missing usage guidelines and behavioral details leave the description incomplete for an AI agent that needs to know when and how to invoke the tool safely.

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 schema has one required parameter (model_id) with no description, and the tool description also does not explain it. Schema description coverage is 0%, and the description does not compensate. However, the parameter is self-explanatory given the tool name, so the impact is minor.

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 specific verb 'unload' with a clear resource 'model instance' and states the purpose 'to free up memory/VRAM'. It clearly distinguishes from sibling tools like load_local_model and list_local_models.

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 phrase 'to free up memory/VRAM' implies when to use the tool, but there is no explicit guidance on prerequisites or alternatives. It doesn't state when not to use it or whether to check loaded models first.

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