council_is_model_replied

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

With no annotations, the description carries full behavioral burden. It explicitly discloses that the tool returns only True/False and never the hat label or answer, which is a critical behavioral guarantee. It also implies it is a safe check (no side effects) and is used specifically to preserve blindness, adding context beyond a bare function name.

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 exceptionally concise—two sentences with no filler. It front-loads the core purpose, then adds a critical behavioral caveat and usage context. Every word earns its place.

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 simple boolean-check tool with an output schema, the description covers purpose, behavior, parameter roles, and usage intent. It doesn't discuss error conditions or edge cases (e.g., invalid council_id), but those are not necessary for a tool of this simplicity. The description is complete enough for the agent to use it correctly in normal scenarios.

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?

Schema description coverage is 0%, so the description must compensate. It does explain the role of 'model' as the participant whose answer status is checked, and 'council_id' as the context. However, it does not specify any conventions for model values (e.g., name format or source) beyond the schema's type, leaving the agent to infer from sibling tools. This is adequate but has a clear gap.

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 a specific verb ('has model finished answering') and resource (council), and immediately distinguishes this tool from siblings by emphasizing the 'blind check' nature that returns only True/False without exposing hat label or answer. This makes it unambiguous and separates it from council_poll or council_reveal.

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 context: use this when you need to confirm a model participated without breaking hat blindness for scoring/synthesis. It does not explicitly mention alternatives or exclusions, but the context is strong enough to guide the agent's selection among sibling tools.

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