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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful behavioral content by specifying that the output lists project-scoped models and distinguishes between virtual tiers and pass-through catalog IDs, which helps set expectations for the response. No contradiction with annotations.

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 entire description is one tightly written sentence that front-loads the action ('List') and includes only high-value clarifiers: project scope, the two categories of model IDs, and the intended downstream use. There is no filler or redundant repetition of the tool name.

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?

For a zero-parameter, read-only list tool, the description is complete: it identifies what is being listed, the scope, and why an agent would call it. Annotations cover the operational safety aspects, and the mention of model ID categories provides enough output expectation even without an explicit output schema.

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 tool has zero parameters, and the schema description coverage is effectively complete. Per the baseline for zero-parameter tools, the description is not required to elaborate on parameter semantics. It correctly avoids inventing parameter information.

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 states a specific verb ('List'), a specific resource ('AI models available to the project'), and clarifies exactly what is included: virtual tiers and pass-through catalog model IDs. It explicitly ties the tool to scalix_ai_infer, distinguishing it from the inference tool and other sibling tools. This is a clear, non-tautological purpose statement.

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 phrase 'for use in scalix_ai_infer' provides clear context that this tool is the preliminary step before invoking inference, implying when an agent should call it. It does not explicitly state exclusions or name alternative tools, but no other sibling is a plausible alternative for listing models, so the guidance is adequate.

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