Server Quality Checklist

Tool Scores

• get_eval_statsA
  4.3/5.0
  Behavior4/5

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

  With no annotations, the description carries the transparency burden. It adds value by stating that no API key is required and that it returns read-only statistics, implying no side effects. However, it doesn't explicitly state it is a read-only operation or mention rate limits/pagination.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is succinct and front-loaded with the main action. It uses a brief paragraph and a 'Returns:' label, making it easy to parse without fluff.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a zero-parameter tool with an output schema, the description is complete: it states the purpose, lists all expected output fields, and notes authentication requirements. No further context is needed.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The tool has zero parameters, so the schema already covers everything. The description adds no parameter info, but baseline 4 applies because there's nothing to explain.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's function: returning aggregated statistics on all registered evaluations. It lists the specific metrics (total, successes, success rate, average score), which distinguishes it from the sibling tools that generate tasks or run a judge.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  While the description implies the tool is for retrieving overall evaluation stats, it does not provide explicit guidance on when to use it versus the sibling tools, nor does it mention alternative tools for specific cases. The context is clear but no exclusions are given.

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

• generate_adversarial_tasksA
  4.4/5.0
  Behavior3/5

  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.

  Conciseness5/5

  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.

  Completeness5/5

  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.

  Parameters5/5

  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.

  Purpose5/5

  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.

  Usage Guidelines4/5

  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.

• run_llm_as_judgeA
  4.4/5.0
  Behavior4/5

  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.

  Conciseness4/5

  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.

  Completeness4/5

  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.

  Parameters5/5

  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.

  Purpose5/5

  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.

  Usage Guidelines4/5

  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.