whats_new

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals that the tool lists new models and supports filters, but it does not disclose what fields are returned, the default time window (though the schema mentions it), any sorting order, or whether it's a read-only operation. This is a notable gap for a tool with zero annotation support.

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 two sentences: the first states the primary purpose ('Discover recently added AI models') and the second gives a use case and filter options. Every sentence earns its place, and the description is front-loaded with the core function, making it concise and efficient.

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?

The tool is simple (3 optional params, no output schema), but the description does not fully cover important context such as the shape of results or how it differs from the sibling list_models. It mentions 'recently added' which hints at a distinction from listing all models, but it doesn't explicitly state this, leaving some ambiguity for an agent comparing tools.

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 already provides 100% coverage with descriptions for all three parameters (limit, since, category). The description only restates the idea of filtering by time window and category, adding no extra semantic detail or value beyond the schema. Thus, the baseline of 3 is appropriate.

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 uses a specific verb ('Discover') and resource ('recently added AI models'), making the tool's core function immediately clear. It also includes a concrete example question ('what new models dropped this week?') and mentions filtering, which sets it apart from siblings like list_models or get_model_info.

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 provides clear context for when to use the tool by framing it as answering 'what new models dropped this week?' and supporting time-window and category filters. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of full exclusion guidance.

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