list_local_models

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It states the action (list) but does not disclose the return format, whether it is a read-only operation, or how the 'detailed' parameter affects behavior. This is minimal disclosure at best.

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, short sentence with no redundancy, making it very concise. However, it is so brief that it misses important information, but conciseness itself is good.

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's simplicity (one optional param, no output schema), a fuller description could still clarify the meaning of 'detailed' and what 'available' vs 'loaded' means. The current description leaves significant gaps about return values and parameter effects.

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 input schema has a single 'detailed' boolean with no schema-level description (0% coverage), and the tool description does not mention this parameter at all. The agent has no way to know what 'detailed' does, making parameter semantics completely unhelpful.

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 uses the specific verb 'List' and identifies the resource as 'loaded and available models,' which clearly states the tool's function. However, it does not distinguish this from sibling tools like lms_ls or query_local_llm, which may also list models, so it lacks sibling differentiation.

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 such as lms_ls or query_local_llm. There is no mention of use cases, prerequisites, or exclusions.

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