lms_ps

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 mentions 'CLI:' but doesn't explain what that implies (e.g., external command execution, side effects, or output format). The read-only nature is implied by 'List' but not explicitly stated, and there is no detail on error behavior or interaction with the local server.

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 is front-loaded with the verb and resource. Every word serves a purpose and there is no fluff.

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 the low complexity (no parameters, no output schema), the description could still be more complete by explaining what 'loaded in memory' means, what the return value looks like, and how it differs from similar tools like 'list_local_models' or 'lms_ls'. As written, it leaves ambiguity in a toolset with many closely related options.

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 schema is fully covered. The description doesn't need to elaborate on parameter semantics, and the baseline of 4 applies as there are no parameters to clarify.

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 verb 'List' and a specific resource ('models currently loaded in memory'), making the primary purpose unambiguous. However, it does not explicitly distinguish this from sibling tools like 'list_local_models' or 'lms_ls', which could overlap in scope.

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?

There is no guidance on when to use this tool versus alternatives. The description merely states what it does without any context on prerequisites, typical scenarios, or exclusions.

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