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Carrefour Drive MCP

Carrefour Drive MCP

by maximeallanic
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

get_checkout_recommendations

Retrieve personalized product recommendations, frequent purchases, and promotions at checkout for a given store and basket service to display cross-sell and upsell suggestions.

Instructions

Retrieves product recommendations during checkout on Carrefour France for a specific facility/store and basket service type. Returns recommendation zones such as frequent purchases, personalized recommendations, and current promotions, each with a list of products and delivery type. Useful for displaying cross-sell or upsell suggestions to the user at checkout. (requires an authenticated Carrefour session)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "facility_id": "<facility_id>",
      "basket_service": "<basket_service>"
    },
    "name": "get_checkout_recommendations"
  }
}

Input Schema

NameRequiredDescriptionDefault
facility_idYesThe facility/store identifier (e.g. '0678-150-7052')
basket_serviceYesThe basket service type (e.g. 'drive_clcv' for drive click-and-collect, 'drive', 'delivery', etc.)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the authentication requirement ('requires an authenticated Carrefour session') and outlines the output structure (zones with product lists and delivery type). However, it does not mention whether the operation is read-only, potential side effects, error handling, or response pagination. It covers key behavior but lacks depth.

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 and well-structured: two sentences plus a parenthetical. The first sentence states the core action and scope, the second describes the output, and the parenthetical adds a key requirement. No redundant fluff—every sentence earns its place.

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 there is no output schema, the description provides sufficient detail about the expected return (recommendation zones with products and delivery type) and highlights the authentication prerequisite. For a retrieval tool with two parameters, this covers the essential context. It could benefit from mentioning whether results are localized, but it is sufficiently complete for an agent to use it effectively.

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

Parameters3/5

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

The input schema describes both parameters (facility_id and basket_service) with examples and explanations. The description does not add new details about these parameters beyond the schema context, but since schema coverage is 100%, the baseline of 3 is appropriate. The description does reinforce that these two parameters define the checkout context, but that's minimal added value.

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 verb 'Retrieves' and resource 'product recommendations during checkout on Carrefour France for a specific facility/store and basket service type.' It distinguishes from siblings like get_product_recommendations by explicitly focusing on the checkout context and mentioning multiple recommendation zones (frequent purchases, personalized, promotions), which makes its scope unique.

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 a clear usage context: 'Useful for displaying cross-sell or upsell suggestions to the user at checkout.' This implies when to call the tool (during checkout) and its purpose, but it does not explicitly mention exclusions or alternatives like 'for non-checkout recommendations, use get_product_recommendations.' Given the sibling tools available, this is a minor gap.

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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