Load Model

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does convey the primary effect (loading into the endpoint) and that params are optional, but it does not mention side effects on the previously loaded model, prerequisites (e.g., model being downloaded), or whether the call blocks until the load completes.

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 two sentences with no fluff: the first states the action, the second clarifies the key parameter and optional nature of the rest. Every clause earns its place and 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 tool with a nested params object and no output schema, the description is adequate but incomplete. It covers the core action and model_id source, but omits prerequisites (e.g., endpoint running, model downloaded), potential side effects, and any indication of the response or failure 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?

Schema description coverage is 0%, so the description must compensate. It adds crucial meaning for model_id by defining it as the key from list_models, and it indicates that params are optional with examples (context_length, flash_attention). However, it does not explain the semantics of the remaining nested parameters (num_experts, eval_batch_size, etc.), leaving significant gaps.

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 states a specific verb ('Load') and a precise resource ('a model into the active profile's LM Studio endpoint'), clearly distinguishing this tool from siblings such as list_models, get_loaded_model, and unload_model. It also ties the action to the active profile, which is a concrete context.

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 usage context by explaining that model_id is the key shown by list_models, implying the workflow of listing models first. It does not explicitly list excluded alternatives, but the tool's purpose is distinct enough and the connection to list_models gives practical guidance.

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