Check (yes/no probability)

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose the output shape (calibrated probability 0-1) and the input type (state plus yes/no question), but it does not mention side effects, determinism, error behavior, or threshold interpretation. This is minimally informative but not richly transparent.

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 short sentences. It front-loads the core behavior, then adds concrete use cases and an example. Every sentence earns its place and there is no filler.

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?

With only two parameters and no output schema, the description adequately communicates the return value (calibrated probability 0-1) and the intended role of the tool. It could be more complete by naming sibling alternatives or clarifying behavioral guarantees, but this is sufficient for basic correct invocation.

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 100%, so the parameters are already fully documented. The description adds practical context by framing 'state' as the context/input and 'instructions' as the yes/no question, but it does not significantly expand on the schema.

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 states a specific behavior: answer a yes/no question about the state with a calibrated probability from 0 to 1. It also gives use cases like gates, filters, and guardrails. It does not explicitly name sibling tools, but the 'calibrated probability' and 'yes/no' framing helps distinguish it from classify/score/gate/decide.

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 clearly says when to use it: for gates, filters, and guardrails, with a concrete example. However, it does not explicitly say when not to use it or which sibling tool might be a better alternative, so it stops short of full routing guidance.

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