list_models

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

No annotations are provided, so the description carries the full burden. However, it does not disclose any behavioral traits such as authentication requirements, rate limits, or whether the list is sorted. For a simple read-only list, this is a minor 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 consists of one concise sentence that covers the core functionality without any extraneous words. It is efficiently front-loaded with the essential information.

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 parameter, no output schema), the description is sufficient for understanding the primary purpose. However, it does not specify what the return value contains (e.g., model IDs, names) or any pagination behavior, which could leave some uncertainty for the agent.

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 coverage is 100%: the single parameter 'limit' has a clear description in the schema. The description does not add additional meaning beyond what the schema already provides, so baseline 3 applies.

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 explicitly states the action ('list'), the resource ('Claude models'), and the scope ('available to the configured Anthropic API key'). It clearly distinguishes from sibling tools 'ask_claude' and 'count_tokens' by focusing on model listing.

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 implies usage when the agent needs to query available models, but does not provide explicit guidance on when to use vs. alternatives, nor does it mention any prerequisites or exclusions. Sibling tools have different purposes, so ambiguity is low.

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