replicate_list_models

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

With no annotations, the description must disclose behavioral traits. It only states 'List public models' but omits details like pagination (cursor parameter hints at it), rate limits, data freshness, or response structure. This is insufficient for an agent to understand the tool's behavior fully.

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 (one sentence). While brevity is valued, it lacks any structure (e.g., bullet points or separate sections) that would aid quick comprehension. It is not front-loaded with key information, and the single sentence could be expanded slightly without becoming verbose.

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 (list models) and the absence of an output schema, the description is incomplete. It does not cover pagination, authentication requirements, or what the returned data contains. The low schema coverage (0%) exacerbates the problem, leaving the agent underinformed.

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 input schema has two parameters (api_token required, cursor optional) with 0% description coverage. The description does not explain the purpose of either parameter, such as api_token being for authentication or cursor for pagination. This severely hampers correct invocation.

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 action (List) and resource (public models on Replicate). It is specific enough to distinguish from sibling tools like 'anthropic_list_models' or 'cohere_list_models' by naming the platform. However, it lacks details on scope (e.g., all public models? any filters?) which would improve clarity.

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 is provided on when to use this tool versus alternatives. There is no mention of prerequisites, typical use cases (e.g., exploring available models before creating predictions), or when not to use it. The agent is left to infer by context.

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