list_inference_models

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

With no annotations, the description carries the full behavioral burden, and it only partially does so: it discloses the selection criteria (specific extensions, plus .bytes files that contain model markers). It says nothing about scope (whole project vs. a folder), whether results include paths/metadata, or whether the scan is expensive. The 'with model markers' rule is a useful behavioral detail but not enough on its own.

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?

It is short, but it is a sentence fragment with no subject or verb, which reads like a schema comment rather than an instruction set. Brevity here comes at the cost of clarity rather than from efficient phrasing.

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?

There is no output schema, no annotations, and no parameters, so the description is the only source of meaning — and it omits what is returned (paths? asset objects?), the search scope, and how it relates to the many sibling list tools. What it does supply (the extension list and marker rule) is too thin to call the definition complete.

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 takes zero parameters, so there is nothing for the description to add at the parameter level; the baseline of 4 applies. The description does not misleadingly imply any filter options exist.

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 names the resource (on-device model assets) and enumerates the file extensions that qualify, so an agent can infer this is a lister of inference-model files. However, it is a bare noun phrase with no verb, and it does not distinguish itself from the closely related sibling list_ml_model_assets or explain how 'inference models' differ from generic ML models. Purpose is inferable but never stated.

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?

There is no when-to-use guidance, no mention of alternatives, and no exclusion criteria. The agent is left to guess whether this complements or duplicates list_ml_model_assets and list_assets_by_extension.

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