lms_get

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 fails to mention that downloading writes files to disk, requires network access, or may modify the local model registry. This lack of side-effect information is a significant gap.

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 efficient sentence with no wasted words, making it concise and easy to parse. It lacks depth but is appropriately sized for the limited scope described.

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 tool can perform two distinct actions (search vs. download) and has no output schema, the description fails to explain return values, error conditions, or behavior when no model is found. It also does not account for individual parameter combinations, leaving the agent with incomplete expectations.

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 100% (2 of 2 parameters described), so the schema carries the parameter semantics. The description adds minimal meaning beyond the schema, only implying that 'query' is used for searching and 'download' triggers a download.

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 searches for or downloads models from LM Studio Hub, using specific verbs and a resource. It distinguishes from sibling tools that operate on local models (e.g., lms_ls, list_local_models), though it could be more precise about the search result format.

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 no guidance on when to use this tool versus alternatives like lms_import or lms_ls. It does not mention any prerequisites, use cases, or exclusions, leaving the agent without context for tool selection.

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