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

The description adds a meaningful behavioral caveat beyond the annotations: 'Records instruction following, not proof of autonomy or comprehension.' This clarifies the tool's interpretive status and guards against overclaiming. It does not fully describe side effects or return behavior, but the annotations are all false and do not contradict the description.

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 with no wasted words. The purpose is front-loaded, and the caveat sentence earns its place by shaping agent expectations about what the tool records. It is concise without sacrificing the most important content.

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 two-parameter tool with no output schema, the description is close to sufficient, especially with sibling begin_visit implying where a trace comes from. However, it omits what happens after the choice is recorded, what the response looks like, and what distinguishes the three proposition options. These gaps matter more because there are no annotations or output schema to fill them.

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 only 50%: the trace parameter is documented in the schema, but the choice parameter has only an enum with no explanatory description. The tool description does not compensate by explaining what the proposition values mean or how they map to the choice enum. An agent would have to infer the semantics from context.

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 names a specific action ('choose'), a target ('one proposition'), a mechanism ('using an issued trace'), and a timing condition ('After reading'). It is distinguishable from sibling tools like read_essay and begin_visit, though it never names an alternative explicitly. 'Proposition' remains somewhat jargon-dependent, so it is clear but not maximally explicit.

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 'After reading' provides a clear temporal context, and 'using an issued trace' states a prerequisite. This tells an agent roughly when to call the tool, but there is no explicit exclusion or comparison against siblings such as leave_response or begin_visit. The guidance is adequate, not exhaustive.

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