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

Beyond the sole destructiveHint=false annotation, the description adds meaningful behavioral context: difficulty changes stakeholder intensity, the rubric stays unchanged, real stakes/earned PASS are involved, and the learner should get a choice first. It does not address auth_token or potential side effects, but the output schema likely covers return shape, making this a solid disclosure level.

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 compact and front-loaded with the core purpose, then efficiently explains the main knobs (difficulty, persona_style) and the invariant rubric. Phrases like 'real stakes, earned PASS' add a bit of flavor, but every clause carries meaningful information without excessive bloat.

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?

Given the bare schema and minimal annotations, the description covers the core usage scenario well: what boss battle is, gating, customization options, and the fact that rubric is unchanged. It could be more explicit about when to use this instead of get_lesson or start_exam, and auth_token is omitted, but overall it is sufficiently complete for a single-purpose action tool.

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?

With 0% schema description coverage, the description compensates by explaining difficulty values ('friendly', 'standard', 'brutal') and persona_style examples ('skeptical CTO', 'hostile board member'). However, it does not clarify the required course/module parameters beyond the phrase 'module's', nor does it mention auth_token, so the compensation is only partial.

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?

Clearly identifies the resource (the module's boss-battle simulation) and the action ('Get the module's boss-battle simulation + how to run it'), and the mention of gating/PASS distinguishes it from plain lesson tools. However, the verb 'Get' is slightly less direct than the tool's title 'Run boss battle', and the exact boundary with get_lesson could be sharper.

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?

'Gated like lessons' and 'Offer the learner the choice first' imply when and how to invoke the tool, while the difficulty levels provide context for tailoring. It does not explicitly name sibling alternatives or state when not to use this tool, so the guidance remains implicit rather than fully explicit.

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