List LLM Models

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds that results are limited to models in use and include usage stats, but it discloses no further behavioral traits such as trace-based analysis, result limits, or aggregation details.

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 short and front-loaded, with the core action in the first sentence. The second sentence is slightly redundant but adds a discovery-oriented framing, so it isn't wasted.

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 rich annotations, a 100%-documented optional parameter set, and an output schema, the description provides enough context for invocation. It could be more explicit about sibling tool distinctions and the trace-derived nature of the results, but nothing critical 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?

Schema description coverage is 100%: all five parameters (limit, end_time, start_time, service_name, gen_ai_system) are already described inline with types, defaults, and examples. The tool description adds no parameter-specific meaning, which is acceptable because the schema carries the weight.

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 states a specific verb ('List'), resource ('LLM models'), and scope ('all ... being used'), and adds that it returns usage statistics. It is not a tautology, though it doesn't explicitly distinguish itself from the sibling tool get_llm_model_stats.

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 second sentence ('Discovers what models are deployed and tracks their usage patterns') gives clear context for when this tool is useful. It doesn't name alternatives or state exclusions, so it stops short of a 5.

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