learn_predict

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

With no annotations, the description carries the full burden, and it does disclose one real side effect: the prediction is written to a prediction log (预测写入预测日志). Beyond that it says nothing about permissions, reversibility, or what a prediction result looks like, leaving most behavioral traits undocumented.

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 single parenthetical is tight and front-loads the core action, but at this length it is under-specified rather than genuinely concise for a six-parameter tool. Nothing is wasted, but too little is said.

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 six-parameter tool with no annotations, no output schema, and zero parameter documentation, the description is far too thin. An agent cannot determine what to pass for atom, tier, event, hint, or next, nor what the prediction returns.

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?

Six parameters with 0% schema description coverage means the description must compensate, yet it explains none of them. The phrase about writing to a prediction log only faintly gestures at the `log` parameter; atom, hint, next, tier, and event are entirely opaque in both schema and description.

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 concrete action and object: predicting the pass probability before answering, with the prediction recorded to a log. This is a specific verb+resource rather than a restatement of the name. It does not, however, distinguish itself from siblings like review_grade or learn_report.

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?

"先预测后作答" (predict first, then answer) implies the ordering context in which the tool is used, which is useful sequencing guidance. But no alternatives are named and no conditions for when-not-to-use it are given, so usage is only implied.

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