MCP
For agent-native systems, LIM is also available via MCP, allowing it to be exposed as a callable tool within an LLM-orchestrated workflow.
This enables:
Tool-based invocation from agents.
Cleaner separation between reasoning and retrieval.
Easier orchestration in IDEs and agent frameworks that support MCP.
You can use an MCP inspector to test the MCP protocol locally.
MCP tool
The currently registered MCP tool is:
getRecommendation
Authentication
Requests must include a bearer token in the header.
Authorization: Bearer xxx-xx-xxxxxxxxxxxxRequest Payload
At a minimum, LIM requires the fields below:
prompt: Free-form text describing the inferred user intent. This can be:A direct user prompt
A normalized intent string generated by an agent
A short task-oriented description (e.g. “a gift for a tea lover”)
customerId: The Nosto unique identifier representing the shopper.Used to enable session-level and cross-session personalization when available. Partners can get the customerId by accessing the Nosto's 2c.cId cookie for each visitor.
partnerId(NEW): Your unique Nosto partner identifier provided by the partner management team.chatId(NEW): Your unique identifier for the conversation, to help answer follow up questions within a conversation
Example request:
Required Fields for Shopify Merchants (NEW):
For merchants using Shopify Markets, Nosto LIM resolves the correct catalog — currency, availability, and language — based on the storefront's country and locale. To support this, partners must send two additional fields for all Shopify merchants:
country: The ISO 3166 alpha-2 country code of the Shopify store. Supported values.locale: The language code of the Shopify store. Supported values.
Note: Shopify Markets merchants authenticate using the API token from their Nosto Markets Master account, not a per-storefront token. This is the single account Nosto uses to resolve catalog data across all connected markets — see Authentication.
Optional Fields:
LIM supports the following optional fields:
Ranking-Only Mode: The tool supports a
rankingOnly(boolean) flag that controls how the LLM processes product results:rankingOnly:false(default): The LLM returns both a ranked selection of products and the agent response (a natural language conversational response).rankingOnly:true: The LLM only ranks/selects the best matching products without generating agent insights. Using this option reduces latency.
Max Agent Recommendations Controls: The tool supports the
maxAgentRecommendations(integer | null) field that controls the number of products returned. The default is 3, and the value range is 1 to 20.Agent Context: The tool supports the
giveAgentContext(boolean) flag. Whentrue, LIM returns a concise context describing why LIM selected the returned products.Agent Insight Format: The /api endpoint supports the
responseFormat(string) field to define how the natural language response is returned; by default is plain text, but for partners looking to show the response directly to shoppers, themarkdownoption is also supported.
Response
Nosto LIM returns a ranked list of product recommendations, suitable for direct rendering or further reasoning by an agent.
agentInsight: A natural language conversational response (returned by default. When the flagrankingOnlyis set to true, it's returned as an empty string)
Example “These shoes feature air-cushioned soles: Dr. Martens Cherry Shoe - An original 3-eye shoe with distinctive air-cushioned soles and classic Docs DNA, priced at €100. 3989 Bex Brogue - This shoe combines durability and comfort with Dr. Martens’ air-cushioned sole, priced at €120. NMD_R1 Primeknit ‘Tricolor Black’ - Offers responsive cushioning with a Boost sole, priced at €100.”
agentContext: A concise context for agents describing why LIM selected the returned products (returned when thegiveAgentContextflag is set to true)
Example: "User asks for a sweater recommendation for summer; shopping intent true; Selected in-catalog 100% cashmere sweaters as lightweight layering options; Chose 3 products from the keyword/vector summer cashmere search results"
agentProductRecommendations: A list of products based on agentic recommendations (which combines Nosto Semantic Search, Core Recommendation Pipeline with Predictive, Semantic, and Visual AI + LLM ranking)
MCP server Configuration
The following configuration can be used with MCP-compatible tools and IDEs (e.g. Cursor):
Once configured, LIM becomes available to the agent as a callable external capability.
Last updated
Was this helpful?