Data Platform MCP Tools

MCP (Model Context Protocol) lets your AI chat work with live product data while it chats with a user. It does this by exposing specific “tools” that the chat can call, e.g. retrieving product details. Zoovu provides MCP servers that handle these tools for you.

Tool 1: products_get_details

Description: Gets complete product details using a product identifier.

Parameters

Parameter	Type	Required	Description
identifier	string	Yes	Product identifier (e.g., SKU, product ID)
conversationContext	string	Yes	Recent conversation context
response_format	enum	No	Response format: 'json' or 'markdown' (default: json)

Returns: Complete product information including name, link, images, categories, all specifications, and last update timestamp.

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Tool 2: products_get_details_by_name

Description: Gets product details by product name. Use this as a fallback when the product identifier is not available.

Parameters

Parameter	Type	Required	Description
name	string	Yes	Exact product name
conversationContext	string	Yes	Recent conversation context
response_format	enum	No	Response format: 'json' or 'markdown' (default: json)

Returns: Same as products_get_details - complete product information.

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Tool 3: products_search_with_filters

Description: Search for products using queries, filters, and sorting options.

Core parameters

Parameter	Type	Required	Default	Description
query	string	No	"*"	Search query. Shorter queries (1-2 words) usually work better.
conversationContext	string	No	-	Recent conversation context
response_format	enum	No	"json"	Response format: 'json' or 'markdown'
cursor	string	No	-	Pagination cursor from previous response (for getting next page)
\{FilterName\}_min / \{FilterName\}_max	number	No	-	Dynamic filtering parameters based on search configuration (see below)
sortBy	enum	No	-	Sorting options based on search configuration (see below). Not available if no sorting options are configured.

Dynamic filter parameters

This tool includes dynamic filter parameters based on your search filter configuration.

• Numeric filters: Numeric filters marked as "visible" in your search filter configuration can be used as dynamic min/max parameters.
• Up to 12 filters: The 12 numeric filters with the highest product counts will be used.
• Min/max pairs: Each filter provides both minimum and maximum parameters based on the filter names (i.e. \{FilterName\}_min and \{FilterName\}_max).
• Possible range of values: The available range of values is part of the dynamic parameter's description.

Example dynamic filter parameters:

• Price_min / Price_max: Filter by price range
• Weight_min / Weight_max: Filter by weight range
• Other filters based on your catalog setup

Sorting parameter

The tool may also include a sortBy parameter with options based on your search sorting configuration:

Example sorting options:

• Date_Added_DESC: Newest products first
• Price_ASC: Cheapest first
• Price_DESC: Most expensive first
• Product_Name_ASC: Alphabetical order
• Popular_DESC: Most popular first

Available filters and sorting options depend on your search configuration.

Returns:

• List of matching products (identifier, name, link, image, price, key specifications)
• Pagination cursor for retrieving next page
• Total results count
• Current page information

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Tool 4: products_get_related

Description: Find products related to a specific product (alternatives, compatible items, accessories, etc.).

Availability: This tool is only available if product relations are configured in your project. If no relations are defined, this tool will not appear.

If your project uses MCP and Relations, you can adjust the descriptions of relation types directly on the Relations page. Clear descriptions help the AI understand when each relation type should be used, which improves how queries are resolved.

Parameters

Parameter	Type	Required	Description
identifier	string	Yes	Product identifier to find relations for
conversationContext	string	Yes	Recent conversation context
relationTypes	array	No	Filter by specific relation types (see below)
response_format	enum	No	Response format: 'json' or 'markdown' (default: json)

Relation types

The relationTypes parameter allows filtering by specific relationship categories. Available relation types are configured in your project and vary by catalog.

Common relation type examples:

• Compatible: Products that work together
• Alternative: Substitute or replacement products
• Custom relation types

Each relation type groups multiple specific relationships. When fewer relation types are specified, more results per type will be returned.

The specific relation types and relationships available depend entirely on your project configuration. If no relation types are specified, all configured relations will be included.

Returns: Related products grouped by relationship tag and relationship name, including:

• Relationship descriptions
• List of related products with identifier, name, link, image, price, and key specifications

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Tool 5: products_ask_assistant

Description: Ask our AI shopping expert Zoe about product features, materials, comparisons, buying advice, or general product questions.

Availability: This tool is only available if the Zoovu Advisor integration is configured in your project. Configuration requires:

• Zoovu Advisor setup in Advisor Studio
• zoeAppKey configured in your MCP config

If not configured, this tool will not appear.

Parameters

Parameter	Type	Required	Description
question	string	Yes	Question about products, features, materials, comparisons, or buying guidance
identifiers	array	No	Product identifiers for questions about specific products
conversationContext	string	Yes	Recent conversation context

When to use this tool

The AI assistant uses this tool when the user needs expert knowledge or guidance rather than searching for specific products. Example user queries:

• "What's the difference between memory foam types?"
• "When should I buy organic cotton vs regular cotton?"
• "What features should I look for in X?"
• "Is this product good for sensitive skin?"
• "Compare these two items"

Returns: Natural language answer from the AI shopping assistant based on product knowledge, specific product data (if identifiers provided), and the user's locale/region settings.

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Tool availability

Tool	Always available?	Notes
products_get_details	Yes	Core tool
products_get_details_by_name	Yes	Core tool
products_search_with_filters	Yes	Additional parameters vary by configuration
products_get_related	Conditional	Only if relations are configured
products_ask_assistant	Conditional	Only if Zoe is configured

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Response formats

All tools support two response formats via the response_format parameter:

• json (default): Structured data format suitable for programmatic processing
• markdown: Human-readable formatted text with proper headings, lists, and organization

The AI assistant selects the appropriate format based on the conversation context and receives the full response from the MCP server.

Customizing how results are presented to end users

While the MCP server always returns complete product information, you can control how the AI assistant presents this information to end users in the chat window by configuring the assistant's system instructions or prompts in your MCP client application.

Examples of presentation instructions:

• "Always present product information in a clean, readable format without showing prices"
• "When showing product results, display only the product name, identifier, and a brief description"
• "Summarize technical specifications in simple language for non-technical users"
• "Always include product images in your responses when available"
• "Present product comparisons in a side-by-side table format"

These instructions don't change what data the MCP server returns - they control how the AI assistant communicates that information to end users in the chat interface. Configure these instructions when setting up your chatbot or AI agent in the MCP client application.