lms_ls

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 only states the action without explicitly noting that the operation is read-only, non-destructive, or what side effects (if any) might occur. The 'on disk' qualifier adds minor context but lacks details about return format or system state changes.

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 with no superfluous words. It gets directly to the point and is easy to parse, making efficient use of the available space.

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 zero-parameter, no-output-schema tool, the description is adequately complete to convey the core function. However, it omits any mention of the return value (e.g., a list of model names), which would be helpful for an agent to anticipate the tool's result. The lack of annotations and output schema is partially offset by the simplicity of the operation.

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 tool has zero parameters, so the baseline score is 4. The description correctly avoids inventing parameters and does not need to compensate for schema gaps since there are none.

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 action (list), the resource (models), and a specific scope (currently available on disk). This effectively distinguishes it from siblings like lms_ps (processes) and lms_get (single model retrieval), making the purpose unambiguous.

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?

No guidance is provided regarding when to use this tool versus alternatives. There is no mention of preferred contexts, exclusions, or relationships to sibling tools such as list_local_models or lms_load_cli, leaving the agent to infer usage from the name alone.

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