load_local_model

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions loading into LM Studio memory, implying state change and resource usage, but does not disclose side effects such as whether an already-loaded model is replaced, whether server must be running, or whether the operation is asynchronous or expensive.

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, concise sentence that front-loads the core action and target. There is no fluff, and every word contributes to understanding the tool's 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 one-parameter tool with no annotations and no output schema, the description provides the essential purpose but lacks operational context such as expected outcomes, error scenarios, or follow-up actions. It is minimally viable but not rich enough for an agent to fully anticipate behavior.

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 only one parameter, model_id, and the description adds minimal meaning by calling it a 'specific model'. However, schema_description_coverage is 0%, and the description does not explain the expected format, source, or how to obtain valid model IDs, though the parameter is fairly self-evident from the tool name.

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 action ('Load') and resource ('specific model into LM Studio memory'), making the primary purpose obvious. It is distinct from unload_local_model but does not explicitly differentiate itself from lms_load_cli or other similar loading 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 gives no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparison with sibling tools like lms_load_cli or unload_local_model, leaving the agent to infer usage context.

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