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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal that the tool exposes the complete set of questions, options, and verdicts, which is meaningful. However, it does not describe the return format, payload size, or any operational characteristics beyond content coverage.

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 a single, tightly written sentence that names the content and the intended use without filler. Every phrase adds information, and the purpose is front-loaded.

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 tool with no parameters, the description sufficiently explains what will be returned and why an agent would call it. The main gap is that the representation of the decision tree is not specified, and since there is no output schema, the agent must infer the shape from sibling conventions.

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 has zero parameters and the schema already reflects that with 100% coverage. The description adds no parameter detail, but none is needed; the baseline of 4 for parameterless tools applies.

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 deliverable: every question, option, and verdict in the guide, for end-to-end reasoning. It is clear about what the tool returns, though it does not explicitly differentiate it from siblings like checker_start or checker_answer.

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 phrase 'for reasoning end to end' implies a batch/full-tree use case, suggesting it is appropriate when the whole decision tree is needed rather than step-by-step traversal. However, it does not explicitly say when to prefer this over checker_start, checker_answer, or other siblings, nor does it state any exclusions.

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