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maximeallanic

Carrefour Drive MCP

get_product_recommendations

Get product recommendations for Carrefour France. For product detail pages, retrieve 'Customers also bought' and 'You might also like' zones; for listing pages, fetch category-based suggestions.

Instructions

Retrieves product recommendations for a given page context on Carrefour France. On product detail pages (context=pdp), returns recommendation zones such as 'Customers also bought' and 'You might also like', based on the product EAN, price, categories, and facility service. On product listing pages (context=plp), returns recommendations based on categories. Returns a list of recommendation zones, each with a label and a list of recommended products. (requires an authenticated Carrefour session)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceNoThe price of the product (in centimes or the product's price unit). Used for pdp context.
contextYesThe page context for recommendations. Use 'pdp' for product detail pages, 'plp' for product listing pages.
categoryNoThe primary/leaf category ID for the product or page. Can be empty for plp context.
scenarioNoOptional A/B test scenario identifier (e.g. 'A') to use for recommendation logic.
isNonFoodNoIndicates whether the product is a non-food item. Used for pdp context.
categoriesNoList of category IDs associated with the product or page. Can include multiple IDs for the product's category hierarchy.
product_cdbaseNoThe product EAN/barcode (cdbase identifier) for which recommendations are fetched. Required for pdp context.
dropExtraOffersNoOptional flag to drop extra offers from the recommendation results.
facilityServiceIdNoThe facility/store service ID to scope recommendations to a specific store or drive service.
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses the authentication requirement, explains mode-specific behavior, and describes the high-level output shape. However, it does not mention side effects, failure behavior, or any rate-limit/pagination concerns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, well-structured, and front-loaded with the main purpose. Each sentence adds useful information about behavior, context types, or output format with no redundant filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 9 parameters and no output schema, the description provides a solid high-level contract: input context, parameter relevance, and output structure. It could go deeper on edge cases like optional flags or errors, but the schema plus this description gives an agent sufficient guidance for normal usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds extra semantic value by explaining which parameters are relevant for pdp (EAN, price, categories, facility service) versus plp (categories). This helps the agent decide which optional parameters to populate despite not repeating every schema field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves product recommendations and gives concrete context-specific behavior for both pdp and plp pages, naming example recommendation zones like 'Customers also bought'. This distinguishes it from sibling tools like get_checkout_recommendations or get_personalized_recommendations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit context for using pdp vs plp and notes that an authenticated Carrefour session is required. It does not name sibling tools or explicitly state when not to use this tool, but the page-context guidance is strong.

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

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