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

No annotations are provided, so the description carries full burden. It describes the tool as a read-only listing of model metadata, which is clear. It does not mention authentication or rate limits, but for a simple list tool, the description is sufficient.

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, focused sentence with no unnecessary words. It efficiently conveys the tool's purpose and output content.

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 no output schema and no annotations, the description provides a solid overview. It is complete for a parameterless list tool, though it could mention whether the list is exhaustive or paginated. Still, it's adequate.

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?

With zero parameters, the description adds significant meaning by specifying the output includes params, dense/MoE, and native context. This goes beyond the empty schema and helps the agent understand what information will be returned.

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 lists local LLM model classes with specific attributes (params, dense/MoE, native context). The verb 'list' and resource 'model classes' are precise, and it distinguishes itself from sibling tools like list_hardware.

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 explicit guidance on when to use this tool vs alternatives. While the name and description imply it's for listing models, there is no mention of when not to use it or comparison with siblings like can_i_run_it or recommend_quant.

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