generate_adversarial_tasks

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

With no annotations, the description carries the burden. It explains that each task includes expected behavior for the judge and that the return is a list of annotated tasks. However, it does not discuss side effects, permissions, or limitations, leaving some behavioral aspects undisclosed.

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 well-structured: a clear one-sentence purpose, a usage condition, then a concise Args/Returns breakdown. Every section contributes essential information without redundancy.

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 output schema exists, the description covers the necessary context: purpose, when to use, parameters, and return type. It aligns with sibling tools and provides enough detail for an agent to invoke it appropriately.

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?

The schema itself has 0% description coverage on properties, but the description compensates thoroughly. It explains 'category' as the failure family to target and 'count' with its range (1-50) and default (5). This adds meaningful context beyond the 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 action: 'Génère des tâches de test adversariales pour éprouver un agent LLM' (generates adversarial test tasks to test an LLM agent). This clearly distinguishes it from sibling tools like run_llm_as_judge (which evaluates) and get_eval_stats (which retrieves metrics).

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 provides explicit guidance on when to call the tool: 'Appelle cet outil quand tu veux construire un jeu de tests ciblant un mode de défaillance précis' (call when building a test suite targeting a specific failure mode). It lists example failure modes but does not mention when not to use it or name alternatives, stopping short of a 5.

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