t2000_models

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

With no annotations provided, the description carries the burden of disclosing behavior. The verb 'List' clearly signals a read-only operation, and the phrase 'served from api.audric.ai' adds endpoint context. It does not mention potential errors or rate limits, but for a simple catalog-listing tool, the read-only nature and source are sufficiently disclosed.

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 a single concise sentence that is front-loaded with the key action and resource, and every clause adds value: it lists the catalog, names the endpoint, enumerates the content, and tells when to use it. There is no fluff or repetition of schema fields.

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 that the tool has no parameters and no output schema, the description adequately covers what the agent needs to know: it returns a list of models with id, privacy tier, and per-1M pricing, and it should be called before t2000_chat. The return fields are explicitly mentioned, so the absence of an output schema is compensated.

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?

This tool takes zero parameters, so there are no parameter semantics to describe. Per the rubric, a tool with no parameters receives a baseline of 4, indicating that the description is not expected to explain parameters.

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 ('List') and identifies the resource ('the Audric Private Inference model catalog served from api.audric.ai'), clearly distinguishing it from sibling tools. It also specifies the included fields (id, privacy tier, per-1M pricing), making the tool's function unambiguous.

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 and explicit guidance to 'Call before t2000_chat to pick a model', telling the agent when to use the tool. It does not mention when not to use it or list alternative tools, but the primary usage trigger is clearly stated.

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