list_oscal_models

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

No annotations and no output schema are provided, so the description carries the full burden, and it does disclose the shape of the return value field-by-field. It does not state that the operation is a safe read-only enumeration or whether the model set is static, but the zero-parameter surface keeps the risk low.

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?

Two sentences, no filler: the first defines output contents, the second defines the use case and consumers. The routing guidance is placed after the content statement, which is the right order for a lookup tool.

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 no-parameter listing tool with no output schema, the description covers both what comes back and why an agent would call it. The only remaining gap is the lack of any hint about dataset size or whether the list is stable across calls.

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 no parameters, so there is nothing to disambiguate; baseline 4 applies. The description correctly implies no inputs are required by framing the call as a pure enumeration.

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?

States a specific verb and resource ('List the OSCAL model names') and enumerates the metadata returned (description, layer, formal/short names, release status), which makes the payload concrete. It doesn't explicitly contrast itself with the nearest sibling list_oscal_resources, so an agent must infer the model-vs-resource distinction.

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 second sentence gives an explicit trigger — needing a valid `model_name` for get_oscal_schema or a `model` for the Metaschema tools — and names the concrete downstream tools. There are no when-not conditions, but for a zero-parameter lookup tool the positive routing is sufficient to choose it.

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