get_verbal_reasoning_scaffold

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

With no annotations, the description fully carries the burden. It explicitly states the tool is read-only and does not solve questions or compute answers, which clearly discloses its behavioral boundaries.

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 main purpose, and every sentence adds value without redundancy.

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?

The tool is simple (zero parameters, no annotations, but has output schema). The description covers what the tool does and what it does not do, which is complete for its intended 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?

The input schema has 0 parameters, so schema coverage is 100%. No parameter description is needed, and the tool's description adds no param info but is not required.

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 tool returns a 'read-only method scaffold for verbal reasoning' and lists specific features it provides (question-type routing, discourse-structure analysis guidance, etc.), distinguishing it from sibling scaffolds for other reasoning types.

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?

Although no explicit when-to-use or when-not-to-use guidance is given, the domain-specific name and the list of features imply usage for verbal reasoning tasks. The exclusion statement ('does not solve questions') helps set expectations.

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