explain_inference

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the tool explains reasoning and is 'Calculé par le moteur Python,' but it does not explicitly state whether it is read-only or describe side effects. This is a moderate level of transparency, covering what it does but not a full safety profile.

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 efficiently structured, starting with the core purpose, followed by a usage recommendation and a brief implementation note, then a compact Args list. Every sentence earns its place without unnecessary fluff.

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 moderate complexity (3 params, no output schema, no annotations), the description covers purpose, usage, and all parameters, and hints at output content (membership degrees and rules). It would benefit from a more explicit description of the return format or potential errors, but it is sufficiently complete for an explanation 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?

Schema description coverage is 0%, so the description must compensate fully. It does so by explaining each parameter: path ('chemin du fichier .fis'), inputs ('une valeur par entrée'), and top_k ('nombre maximum de règles détaillées, triées par force décroissante'). This adds meaning beyond the bare schema types.

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 purpose: 'Explique le raisonnement : degrés d'appartenance et règles déclenchées' and positions it as the tool for understanding *why* the system produces a given output. This distinguishes it from siblings like infer and describe_fis, making the scope explicit.

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 explicitly says 'C'est l'outil à privilégier pour comprendre *pourquoi* le système produit une sortie donnée,' providing a clear when-to-use directive. However, it does not mention exclusions or alternatives by name, so it lacks the full when-not/alternatives guidance for a 5.

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