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

No annotations are provided, so the description carries the full burden. It states the operation returns the next step, but does not disclose what happens on invalid inputs, whether any state is mutated, or the nature of a final verdict. For a decision-taking tool, such context is important.

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 compact sentence that conveys the core interaction. No wasted words, though it could have added a bit more detail without becoming verbose.

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 2-parameter tool with no output schema and no annotations, the description gives the barest interaction model. It lacks details on edge cases, response format, usage context, and relationship to sibling tools, making it insufficient for robust autonomous 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 coverage is 0%, so the description must compensate. It mentions the question id and the choice index, but does not explain what the 'choice' index refers to (e.g., zero-based vs. one-based), or the expected format of 'question'. The schema only specifies types, so the meaning is insufficiently defined.

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 ('return'), resource (next question or final verdict), and states the input (question id and chosen option index). It clearly indicates the tool's function, though it does not explicitly differentiate it from sibling tools in the description itself.

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 gives implicit usage context: it should be used when you have a question id and a chosen option. However, it does not provide explicit guidance on when to use this tool versus alternatives like checker_tree, or what to do if the question is invalid or the choice is out of range.

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