draft_ai_disclosure

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

With no annotations, the description carries the behavioral burden. It discloses the conditional nature of the tool, the dependency on a valid journal key, and the no-guessing fallback when journal is absent. The only ambiguity is what happens when a provided journal is valid but does not require a disclosure, which is likely covered by the output schema but not explicitly stated.

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?

Two sentences deliver the core purpose, key condition, and fallback behavior with no filler. The main outcome is front-loaded, and the dependency on `journal` is placed immediately after the purpose, giving an agent the essential information in order.

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 text-generation tool with an output schema and no destructive side effects, the description covers the required inputs, the prerequisite journal key, and the omission fallback. The main unstated detail is the exact branch when a valid journal does not require a disclosure, but that is a modest gap given the output schema exists.

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 0%, so the description must compensate. It does by connecting `journal` to `list_supported_journals` and explaining its role in the requirement check, while also framing `tool_name` and `reason` as the subject and rationale of the generated statement. It stops short of giving format examples, but the property names plus this context are sufficient for correct invocation.

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 identifies a concrete deliverable ('Declaration of generative AI use' statement text) and specifies the two inputs that drive it: tool name and reason. It clearly conditions the task on the target journal requiring a disclosure, but it does not explicitly distinguish the tool from sibling tools like check_citation_integrity.

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?

It explicitly tells callers to pass `journal` from `list_supported_journals` so the tool can check whether a disclosure is required, and it explains the fallback behavior when journal is omitted: return a prompt asking the user. This is clear context for when to call the tool, but it does not state exclusions or name an alternative tool for the same task.

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