essay_exam_by_topic

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

With no annotations, the description carries the behavioral disclosure burden. It clearly discloses that all essay questions are generated in one sitting, AI model answers are included by default, and show_answer=False postpones the answers. Minor side effects such as whether the attempt is recorded or how both topic parameters are combined are not disclosed, but the key behavior is transparent.

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 the core purpose front-loaded and the parameter constraint immediately after. Every sentence earns its place with no filler or repetition.

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 simple 3-parameter tool with no output schema, the description makes the main behavior and return content (questions plus AI answers) understandable. Minor omissions such as error behavior when neither parameter is provided and possible progress-recording side effects keep it from a perfect score.

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%, yet the description fully compensates: it maps topic_point to '考點', topic_subject to '子科目', states the at-least-one requirement, and explains show_answer's default and self-testing behavior. This adds significant meaning beyond the bare schema.

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 specific verb and resource: it generates a topic-based essay mock exam containing all essay questions for an exam point or sub-subject, with AI model answers attached by default. This clearly differentiates it from sibling practice/search tools by emphasizing the 'all questions at once' mock-exam scope.

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 provides clear usage context: use this when you want a comprehensive essay exam on a topic_point or topic_subject, and it explains how to use show_answer=False for self-testing. It does not explicitly name alternative sibling tools or state when not to use it, so it stops short of full exclusion guidance.

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