Picksly

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.

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

Annotations already establish the safe read-only, idempotent, non-destructive profile, and the description adds substantive value by enumerating what comes back (overall and per-profile scores, per-criterion scores, best new and refurbished offers). It does not cover behavior for unknown models, defaulted parameters, or result volume, so it stops short of full transparency.

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 front-loaded sentence that lists the returned data and the lookup key with zero filler; every clause carries information.

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 no output schema, the description usefully summarizes the returned content, so an agent knows what it will get for a 4-parameter read-only lookup. Minor gaps remain around behavior on unmatched model names and the effect of the pays/langue defaults.

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 already 100%, so the baseline is 3; the description adds meaning with concrete model-name examples ('Galaxy S25', 'iPhone 16 Pro') that clarify the expected query format beyond the schema's 'Model name, brand optional'.

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 precisely (scores overall and per buyer profile, per-criterion scores, best new and refurbished offers) and scopes it to a single model looked up by name, with concrete examples. It implicitly separates itself from comparer_modeles by stressing 'one model', though no sibling is named explicitly.

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 phrase 'for one model found by name' implies this is the single-model lookup path, distinct from the comparison and best-picks siblings, but there is no explicit when-to-use / when-not guidance and no named alternative.

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

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and closed-world. The description adds useful behavioral context beyond annotations by stating that results include current price, merchant, purchase link, and a Picksly page explaining the score.

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 a single front-loaded sentence that identifies the core task first and then lists the returned fields. There is no redundant or wasted language.

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?

For a read-only recommendation tool with no output schema, the description covers the essential behavior and return contents well. It is slightly incomplete in that it does not describe pagination, ranking methodology, or output structure, but the schema and annotations carry much of the remaining detail.

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 100%, so all seven parameters are already documented in the schema. The description echoes the category, buyer profile, and optional maximum budget but adds little semantic detail beyond what the schema provides, making the baseline 3 appropriate.

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 retrieval task: 'Best-rated models of a category for a buyer profile and an optional maximum budget.' This is much clearer than the tool name alone, but it does not explicitly distinguish the tool from siblings such as comparer_modeles or fiche_modele.

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 implies usage through the buyer-profile and budget filters, but it gives no explicit when-to-use guidance, prerequisites, or comparison against the sibling tools. An agent can infer the use case, but is not told when to prefer this over comparer_modeles or fiche_modele.

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