aa_list_llms

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

No annotations are provided, so the description bears full responsibility. It discloses the filtering, sorting, and limit parameters, which are behavioral traits. However, it omits details like whether the operation is read-only (assumed but not stated), data freshness, rate limits, or error handling. Basic operational context is present but lacks depth.

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 extremely concise and well-structured. It opens with a clear purpose statement, lists parameters in a compact 'Args:' block with one-line explanations, and ends with a 'Returns:' note. Every sentence is relevant, and there is no redundancy.

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 moderate complexity (5 optional parameters) and the presence of an output schema (not shown), the description covers the key inputs and return type. It lacks details on output fields, default sort order, and handling of no results, but these are often covered by the output schema. Minor gaps remain.

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 schema has 0% description coverage, so the description must compensate fully. It does so by explaining each parameter's purpose, filter behavior (case-insensitive substring match), and valid sort keys (intelligence, price, speed, etc.). This adds essential meaning beyond the schema's type and default values.

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 LLM models from Artificial Analysis with optional filtering and sorting. The verb 'list' and resource 'LLM models' are explicit, and it distinguishes itself from sibling tools like aa_get_model (single model) and aa_compare_models (comparison).

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 does not explicitly guide when to use this tool vs alternatives. While the purpose implies it is for listing multiple models, there is no mention of when-not to use it or comparison with sibling tools like aa_get_model for single model retrieval. Usage context is only implied.

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