run_llm_as_judge

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It transparently describes the evaluation process (comparing 'answer' to 'expected_behavior') and the structured verdict format (success boolean, 1-5 score, short justification). While it omits details like API costs, rate limits, or potential non-determinism of LLM judges, it provides a solid overview of what the tool does and returns.

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 concise paragraph explaining the purpose and a clear list of parameter definitions. Each sentence adds value, and the format is easy to parse. It is slightly verbose due to the parameter list, but not unnecessarily wordy.

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 tool's modest complexity (3 string parameters, no nested objects, output schema present), the description adequately covers what the tool does, when to use it, and what it returns. It also lists all parameters with explanations. The only minor gap is not discussing edge cases or limitations of the LLM judge, but this is not essential for basic invocation.

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 input schema lists only bare string parameters with no descriptions (0% coverage). The description compensates fully by defining each parameter: 'prompt' as the instruction given to the agent, 'expected_behavior' as the reference good behavior, and 'answer' as the response to evaluate. This gives complete semantic meaning 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 clearly states the tool's function: it evaluates an agent's response via an LLM judge. The verb 'Évalue' and the specific resource 'réponse d'un agent' make the purpose clear, and it distinguishes itself from siblings (generate_adversarial_tasks and get_eval_stats) by focusing on judging/rating answers rather than generating tasks or fetching stats.

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: 'Utile pour noter automatiquement les réponses d'un agent testé sur des tâches adversariales' indicates when to use the tool. It does not explicitly mention alternatives or exclusions, but the tool's distinct role among siblings makes the usage context sufficiently clear.

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