list_local_models

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

No annotations are provided, so the description carries the full burden. 'List' implies a non-destructive read, and the loaded-vs-available distinction adds some behavioral context, but there is no mention of connection requirements to LM Studio or what happens if the service is unreachable. For a zero-argument read tool this is acceptable, though thin.

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?

A single, front-loaded sentence with no filler. Every word earns its place.

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 read tool with no output schema, the description is nearly sufficient. The one omission is any hint about the underlying LM Studio dependency, but little else an agent needs is missing.

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?

Zero parameters, so the baseline of 4 applies. There is nothing for the description to disambiguate or compensate for.

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?

States a specific verb (list) and resource (loaded and available models in LM Studio), and the scope distinction between 'currently loaded' and 'available' is explicit. It is reasonably distinguishable from check_lm_studio_status and ask_local_llm, though it does not name them.

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?

Usage is implied by the verb – an agent would use this to discover what models exist before invoking ask_local_llm – but the description states no when-to-use condition, prerequisites, or alternatives. Adequate but with a clear gap.

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