get_use_case

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

No annotations are provided, so the description carries the full burden. It discloses the key behavioral aspect: the 'scoped' flag and its meaning, including the full-access fallback when scoped is false. This goes beyond a mere statement of purpose and informs the agent about the tool's output semantics.

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 and front-loaded with the core purpose. It avoids redundancy and includes only essential usage and behavioral detail, making it concise and well-structured.

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 an output schema exists, the description provides the necessary context: what the tool returns, how to interpret 'scoped', and when to call it. No additional detail is needed for effective use.

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?

With zero parameters, there is nothing to explain. The baseline for a no-parameter tool is 4, and the description does not need to compensate for any missing 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 clearly states the action ('Return') and the resource ('this assistant's use-case scope'), and specifies the components (datasets, models, decisions). This distinguishes it from sibling tools like list_ml_projects or list_models_and_decisions, which are about external resources rather than introspection.

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 explicitly instructs 'Call this first to learn which resources you may work with', providing clear when-to-use guidance. It does not mention when-not-to-use or alternatives, but given the tool's unique introspective role, this is sufficient.

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