catalogue
Server Details
French gluten-free product catalog and comparisons — verified prices and buyer ratings.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolsget_productAInspect
Récupère la fiche complète d'un produit par son slug. Inclut : nom, marque, catégorie, description, verdict éditorial, prix, note rédaction, note Amazon, ASIN, URL produit gluten-free.fr et URL achat Amazon affiliée.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug produit (ex 'rummo-spaghetti-sans-gluten'). Liste complète disponible via search_products. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description explicitly states a read-only retrieval action ('Récupère') and enumerates the data returned. It does not mention permissions or error scenarios, which are less likely for a simple get; the behavior is transparent for its scope.
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, efficient sentence that lists the included fields without superfluous words. It is well-structured and easy to parse.
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?
The description tells the user exactly what data is included, which is sufficient for understanding the tool's output. It does not specify output format or potential limitations, but given the simple nature of the operation and lack of an output schema, it is adequately complete.
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 schema already fully describes the single 'slug' parameter with an example and a reference to search_products. The description adds no additional semantic detail beyond that, matching the baseline for 100% schema coverage.
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 it retrieves a complete product sheet by slug and lists the specific fields included. It distinguishes itself from siblings like search_products (which searches) and list_comparatifs (which lists comparisons).
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 when a slug is available and full product details are needed. It does not explicitly contrast with sibling tools, but the slug-based lookup is clear. A minor addition about when to prefer this over search_products would have earned a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_comparatifsAInspect
Liste les 8 comparatifs structurés disponibles avec, pour chacun, le slug, le titre éditorial, l'URL et la liste des slugs produits inclus dans le classement (ordre = ranking éditorial).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It fully discloses the output: exactly 8 items, their fields, and the editorial ordering. The read-only nature is implied by 'Liste' but not explicitly stated, so it misses a little effectiveness despite strong return-value disclosure.
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 sentence front-loads the verb 'Liste' and packs all essential information (count, fields, ordering) into about 25 words with no filler.
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 parameterless, read-only listing tool with no output schema, the description is complete: it specifies count, exact fields returned, and ordering. No additional context is necessary.
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 has zero parameters, so the baseline is 4. There are no parameter details needed or provided, and nothing more is required.
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 a specific action ('Liste les 8 comparatifs structurés disponibles') and enumerates the exact returned fields (slug, titre éditorial, URL, slugs produits). This distinguishes it from siblings get_product and search_products by targeting comparatifs specifically.
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 provides no guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. It only states what the tool does, leaving the agent to infer appropriate contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsAInspect
Recherche dans le catalogue de produits sans gluten Amazon vérifiés. Filtre par catégorie (valeurs : 'epicerie' = pâtes/farines/sauces, 'snacking' = biscuits/madeleines, 'cuisine' = pains/farines pâtisserie, 'petit-dejeuner' = céréales/biscuits matin, 'boissons' = bières SG, 'livres' = guides). Filtre par marque (Schär, Rummo, Barilla, Gerblé, Caputo, etc.) ou par mots-clés (utiliser 'pates', 'pain', 'farine', 'biscuit' dans query pour chercher dans le nom). Retourne nom, marque, prix EUR, note rédaction /5, note Amazon /5, lien produit gluten-free.fr et lien achat Amazon affilié.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | Filtre marque (ex 'Schär', 'Rummo'). | |
| limit | No | Nombre max de résultats (défaut 10, max 50). | |
| query | No | Mot-clé libre cherché dans le nom, la marque, la description (case-insensitive). | |
| category | No | Filtre catégorie : 'epicerie' (pâtes/farines/sauces), 'snacking' (biscuits), 'cuisine' (pains/farines pâtisserie), 'petit-dejeuner' (céréales), 'boissons' (bières SG), 'livres'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses return fields (nom, marque, prix, notes, liens) and mentions the limit parameter via context, but does not explain ordering, pagination, error behavior, or that the operation is read-only (though that is implicit for a search). It adds useful return-format details but remains somewhat superficial compared to possible behavioral caveats.
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 concise paragraph that front-loads the purpose and then lists filters and return fields. It is efficient with no fluff, though a slightly structured format (e.g., bullet points) could improve readability without adding length. Overall, it is appropriately compact for the information density.
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 no output schema and no annotations, the description adequately explains what the tool returns and how to filter. It covers all parameters and offers sufficient guidance for typical search use cases. It does not mention error handling or empty results, but for a search tool with optional filters, this is a minor gap. Siblings are unlikely to require additional context.
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. The description adds extra value by giving concrete keyword suggestions (e.g., 'pates', 'pain', 'farine', 'biscuit') and expanding category examples beyond the schema's brief list. This goes slightly beyond the schema descriptions, enhancing usability.
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 its function: searching a verified gluten-free Amazon product catalog. It lists specific filters (category, brand, keywords) and returns a defined set of fields. It distinguishes itself from siblings (get_product, list_comparatifs) by being the search-oriented tool, though it does not explicitly name them.
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 clear guidance on how to use the tool: filter by category, brand, or keywords, with explicit examples for each. It implies this is the tool to use for searching the catalog, but does not explicitly mention alternative tools like get_product or list_comparatifs, nor does it state when not to use it. Thus clear context without exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
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Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a distinct purpose: retrieving a single product by slug, listing comparative articles, and searching products with filters. No overlap or ambiguity.
All tool names follow the same verb_noun pattern with lowercase underscore: get_product, list_comparatifs, search_products. Consistent and clear.
Three tools is minimal but appropriate for a read-only catalogue server, covering the core needs of fetching, listing, and searching. Could be slightly more extensive but not inadequate.
The server provides essential read operations: get by slug, list comparatifs, and search. Missing a generic list all products or an update/create endpoint, but for a catalogue and comparison resource it is largely complete.