gluten-free.fr: catalogue, comparatifs et guides sans gluten

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, yet the description adds substantial value: it enumerates the three possible verdicts (autorise, interdit, a_verifier), states that a sourced explanation and article URL accompany the result, and flags the editorial/not-medical-advice nature. This is real behavioral disclosure beyond structured fields.

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 core question is front-loaded in the first clause, followed by examples, then the return semantics and caveat. It's dense but every part earns its place; the example list is slightly long but aids disambiguation.

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 carries the return-value burden well by naming the verdict vocabulary and the accompanying source/URL. The product-vs-ingredient caveat closes the main ambiguity for a single-param lookup 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 coverage is 100% and the schema itself documents the single 'ingredient' parameter with French examples. The description's example list mirrors rather than extends the schema, so it adds little semantic depth beyond the baseline.

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?

States a precise verb+resource ('répond à X contient-il du gluten') scoped to ingredients/common foods, and lists representative examples (blé, semoule, sauce soja, avoine…). An agent can distinguish this from get_product or search_products, which target specific products rather than ingredient-level verdicts.

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?

Clearly frames the context (ingredient or common food, not a specific product) and warns 'pour un produit précis, l'étiquette fait foi', implicitly steering product queries elsewhere. It doesn't explicitly name a sibling tool, but the scope boundary is unambiguous.

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 cover readOnly/idempotent/non-destructive, so the bar is lower, yet the description adds genuine behavioral context: the price is "vérifié de moins d'1 h ou null" (data-freshness semantics) and « Notre choix » is the first currently purchasable product. These details are not derivable from the annotations and help an agent interpret the response correctly.

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 content overview and ends with the recommended use case. Every clause carries information about what is returned; only the enumeration of return fields makes it slightly long, but nothing is wasted.

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 correctly carries the burden of describing the return payload (short answer, criteria, top pick, ranking with URLs) and data freshness. Safety is covered by annotations. It is nearly complete for a single-parameter read tool; only sibling routing is missing.

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% and the slug parameter is documented with an example ("meilleures-pates-sans-gluten-2026") plus a pointer to list_comparatifs. The description adds no further parameter meaning, so the baseline of 3 applies.

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 the verb-resource pairing ("Détail d'un comparatif") and enumerates the returned content: editorial short answer, selection criteria, « Notre choix », and the product ranking. This distinguishes it reasonably from list_comparatifs, though it never names that sibling explicitly, so differentiation is implied rather than stated.

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?

"Idéal pour recommander un produit en citant un classement indépendant" gives one clear use scenario, which is helpful. However there is no explicit when-not-to-use, no prerequisites, and no routing to alternatives like list_comparatifs or get_product for cases where a full comparatif is not needed.

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 readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds genuinely useful behavior beyond that: price freshness ('prix vérifié de moins d'1 h ou null'), fallback to alternatives when a product is unavailable, and that it returns a citable site URL.

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?

Front-loads the core purpose (detailed sheet by slug) before enumerating returned contents and the citation URL. The content list is long but every item is substantive, so density is justified rather than wasteful.

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 carries the burden of describing return values, and it does so thoroughly: description, editorial review, pros/cons, availability, price freshness, related comparisons, alternatives, and the citable URL. Nothing an agent needs to call and interpret this read-only tool correctly is missing.

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 the baseline is 3. The description goes beyond the schema by stating the provenance of the slug value (search_products or get_comparatif), which adds meaning about how the parameter should be sourced rather than just its format.

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?

States a specific verb+resource ('Fiche détaillée d'un produit') retrieved by a named key (slug), and explains that the slug comes from search_products or get_comparatif, which cleanly separates this detail-fetch tool from the search/list siblings. An agent can identify the operation and its input source without opening the schema.

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 gives a clear precondition and routing hint: the slug is obtained 'via search_products ou get_comparatif', so the agent knows it must search first and then fetch. It does not explicitly state when not to use it, but the prerequisite chain is clear enough that this is above implied usage.

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 readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral detail on top: it discloses what comes back (a quick answer plus the guide URL) and the exact behavior when the parameter is omitted. It still says nothing about coverage limits beyond the listed chains.

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 states purpose, the covered chains, the return payload, and the no-argument behavior. The inline chain list is long but functional, since it enumerates the accepted values; nothing is redundant.

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 one-optional-parameter, non-destructive read tool with no output schema, the description supplies enough to call it correctly: the accepted values, the optional parameter behavior, and the return payload. Only cross-tool routing against list_guides is missing.

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 100% and the single store parameter is fully documented in the schema, including the 'vide = liste' semantics. The description echoes the optional/empty behavior but adds no format or matching guidance (e.g. accent/casing tolerance) 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 names a specific resource (a « sans gluten chez … » guide for a given supermarket chain) and the shape of the response (quick answer + guide URL). An agent can tell this is a store-specific gluten-free shopping guide. It does not, however, distinguish itself from the sibling list_guides, which appears closely related.

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: query a named retailer to get its gluten-free guide. The description explains the no-parameter fallback (lists all covered chains) but never says when to prefer this tool over list_guides or get_comparatif, so routing between siblings is left to inference.

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 readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuine value by disclosing the shape of the returned items (title, question, URL), though it says nothing about result volume or pagination.

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?

Two sentences, front-loaded with the purpose and then the routing rule. The long parenthetical category list is somewhat heavy but is functional, since it supplies the keywords an agent needs to recognise a matching request.

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?

No output schema exists, but the description compensates by naming the returned fields (title, question, URL). Combined with the explicit call-order guidance and zero-parameter signature, an agent has everything needed to invoke and sequence it correctly; only volume/pagination behaviour is unaddressed.

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 tool takes zero parameters and the schema is empty, so there is no parameter semantics to explain. Baseline 4 applies; the description correctly does not invent arguments.

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?

States a specific verb ("Liste") and resource ("comparatifs éditoriaux de gluten-free.fr"), and even enumerates the covered categories so the agent can match a user query to the tool. It explicitly distinguishes itself from the sibling get_comparatif, which is a follow-up rather than a substitute.

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?

Gives an explicit trigger ("À appeler en premier quand l'utilisateur demande « quel est le meilleur … sans gluten »") and names the next tool in the chain (get_comparatif). Both when-to-use and the sequencing are stated outright, leaving no inference to the agent.

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 readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real context beyond them: the exact count and themes of the guides and the fact that some are online without registration while others arrive as PDFs by e-mail, which shapes how the agent should present the result.

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?

Two sentences, no filler, with the enumeration of contents front-loaded and the usage note placed last. The first sentence is dense but every clause (URL, format, signup page) conveys distinct 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?

There is no output schema, yet the description compensates by describing the shape of each returned entry: URL, delivery format, and signup page. For a zero-parameter read tool with full annotation coverage, nothing an agent needs in order to call and present it correctly is missing.

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 tool takes no parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description correctly does not invent input options.

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?

States a specific verb (list) and resource (the 6 free gluten-free guides), enumerates their subjects, and details what each entry carries (URL, format, signup page). It is clearly distinguishable from list_comparatifs and search_articles, though it never explicitly names the potentially confusable sibling get_store_guide.

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 closing sentence gives explicit when-to-use guidance: offer it to a user who is starting out or wants a printable list. That is clear context, but no alternative tool or exclusion condition is named, so it falls short of a full when/when-not statement.

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 readOnly/idempotent/non-destructive/openWorld=false, so safety is covered. The description adds valuable non-annotation context by disclosing the return shape (title, résumé, sourced 'réponse rapide', citable URL), which matters since there is no output schema.

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?

Three sentences, front-loaded with purpose, then return values, then usage. No filler; each sentence earns its place, though it is slightly dense in the topic enumeration.

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, describing the return contents is the right call, and usage guidance plus scope are present. Nothing critical is missing for a zero-required-parameter search 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 100%, so the schema already documents limit, query and category fully. The description adds topical examples but no format or syntax detail beyond the schema, so baseline 3 applies.

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?

States a specific verb ('Cherche') and resource ('articles et guides de gluten-free.fr') and enumerates the topical scope. It is clearly distinct from product-oriented siblings like search_products, though it does not explicitly name a sibling tool.

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?

Provides clear when-to-use guidance: 'Appelez-le pour toute question de fond sur le régime sans gluten avant de répondre de mémoire.' It does not name alternatives or state exclusions (e.g., when to prefer check_ingredient_gluten), so it falls 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.

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, and the description adds substantial context beyond them: results are limited to currently purchasable products (never out of stock), price is returned only if verified within the last hour (otherwise null plus the readings date), and the exact fields returned. This is rich operational context not present in structured fields.

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?

One dense paragraph, but front-loaded with the primary when-to-use trigger and free of filler; the return-value and exclusion details each earn their place. It is slightly long and could be split, but nothing is wasted.

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 carries the return-value burden and does so fully (name, brand, category, summary, price with freshness caveat, product URL). Combined with 100% schema coverage and full annotations, an agent has everything needed to call it correctly.

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 100%, so the baseline is 3, but the description adds real meaning: it explains the freshness rule behind price data, which clarifies that max_price_eur filters on verified prices, and ties include_utensils/category='ustensiles' to a default exclusion behavior. That goes beyond the schema text.

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?

States a specific verb (chercher) and resource (produits sans gluten) with scope (catalogue gluten-free.fr) and enumerates the domains it covers (pâtes, farines, pains, biscuits...). An agent can distinguish it from siblings like get_product or search_articles without opening the schema.

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?

Explicitly says when to call it ('À appeler pour répondre à « quelle marque / quel produit sans gluten acheter »') and gives a clear exclusion rule with the override conditions (include_utensils=true or category='ustensiles'). It does not name sibling alternatives for cases like looking up a single known product (get_product), so it falls just 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.