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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description does add real value by describing the output shape (scores, prices, per-buyer-profile winner, page link), which substitutes for the missing output schema. It says nothing, however, about failure behavior when a model name is not recognized, which is a real risk given free-text model parameters.

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?

A single dense sentence that front-loads the primary output (side-by-side scores and prices) before the secondary ones. No filler, though the sentence fragment style makes the list of outputs slightly harder to parse than a short structured form would be.

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?

With five parameters, no output schema and free-text model names, the description does the important work of explaining what comes back. The remaining gap is behavioral edge cases (unknown model, mismatched categories), which the sentence 'of the same category' hints at but does not resolve.

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 coverage is 60% – categorie, pays and langue are documented in the schema, but modele_a and modele_b have no descriptions beyond length bounds. The description contributes the constraint that both models must belong to the same category, which is genuinely useful. It does not clarify whether model names must match exactly or are fuzzy-matched, leaving the weakest-documented parameters under-specified.

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 names the resource (two models of the same category) and enumerates the deliverable (scores, prices, per-profile winner, comparison page), which is far more specific than the title 'Compare two models'. It implicitly separates itself from fiche_modele by requiring a pair of models, but never names the siblings to make the distinction 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?

Usage is only implied: an agent can infer this is the tool for head-to-head evaluation rather than a single-product lookup. There is no explicit guidance on when to prefer this over fiche_modele or meilleurs_choix, and no preconditions such as both models needing to exist in the same category store.

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