list_models

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

No annotations provided. The description only states the return format but does not disclose any behavioral traits such as idempotency, authentication needs, rate limits, or side effects. For a read-only listing, the lack of explicit safety indication is a gap.

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 very short, with one sentence and a return format. While concise, it omits necessary detail for a standalone definition. It is not overly verbose but sacrifices completeness.

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, the description offers a partial return format but lacks completeness on behavior, pagination, or error conditions. The tool's complexity is low (1 optional param), but the description still falls short of being fully informative.

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 0%, yet the description adds no explanation of the 'model_type' parameter beyond what the enum provides. The description says 'for LLM nodes' but the parameter includes non-LLM types, creating confusion. No parameter semantics are conveyed.

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 it lists AI models for LLM nodes, with a specific verb and resource. However, the description mentions 'LLM nodes' while the parameter includes other model types like text-embedding and rerank, creating slight ambiguity. It distinguishes from sibling list tools like list_tools or list_apps.

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 guidance on when to use this tool vs. alternatives. With many sibling list tools (list_agent_strategies, list_apps, etc.), the description lacks context on when list_models is appropriate versus others like list_node_types or list_tools.

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