ask_case_prompt

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

The description fully discloses the tool's unusual behavior: it returns no content, the prompt reaches the candidate inside the room only, and never through the tool result that would land in the chat transcript. This is critical behavioral context that the schema and annotations do not provide.

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, front-loaded with the core action, and every sentence earns its place. It avoids redundancy and is efficiently 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 no annotations, the description covers all necessary aspects: purpose, return behavior, and expected next step. The output schema exists, so return values are already defined, and the description does not need to repeat them.

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 input schema has zero parameters, so the description does not need to add parameter details. Schema coverage is 100% and the baseline for 0 params is 4; the description could optionally mention the absence of parameters but it is not necessary.

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 ('Read') and resource ('case prompt') with an explicit purpose ('to open the interview'), making it clear and distinct from sibling tools like start_case or say.

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 usage context ('to open the interview') and sets expectations for what follows ('After this, expect clarifying questions before they start structuring'). It does not explicitly name alternative tools or when-not-to-use, but the context is unambiguous enough for an AI agent.

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