load_model

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

With no annotations provided, the description carries full burden. It explains that the tool loads a model into memory, describes default context_length behavior, and notes engine-specific parameter constraints. It does not mention potential side effects like unloading previous models, but the core behavior is transparent.

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 concise and front-loaded with the purpose. It uses bullet points for engine-specific notes and includes illustrative examples. No superfluous information, though the engine-specific detail could be slightly more streamlined.

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?

Given 6 parameters and 0% schema coverage, the description covers main behavior and key constraints but omits the output schema (exists but not described) and prerequisites like model availability. It is adequate but not fully comprehensive.

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 0%, so the description must compensate. It explains context_length default and engine-specific parameters (flash_attention, num_experts, etc.) but does not clarify the format of model_key beyond examples. The examples help, but the param semantics could be more explicit.

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's purpose: 'Load a model into memory so it is ready for inference.' It uses a specific verb ('load') and resource ('model'), and is easily distinguishable from sibling tools like unload_model and list_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 description provides clear guidance on when to use the tool (to prepare a model for inference) and includes examples. It also specifies engine-specific parameter applicability, but lacks explicit 'when not to use' or alternatives, which is acceptable given the tool's straightforward nature.

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