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boldcommerce

Magento 2 MCP Server

by boldcommerce

get_related_products

Retrieve related products for a specific product using its SKU. This tool helps display cross-sell and upsell items in Magento 2 stores.

Instructions

Get products related to a specific product by SKU

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skuYesThe SKU (Stock Keeping Unit) of the product

Implementation Reference

  • Handler function that calls Magento API endpoint `/products/{sku}/links/related` to get related product SKUs, then fetches full details for each using `formatProduct`.
    async ({ sku }) => {
      try {
        const relatedProducts = await callMagentoApi(`/products/${sku}/links/related`);
        
        if (!relatedProducts || relatedProducts.length === 0) {
          return {
            content: [
              {
                type: "text",
                text: `No related products found for SKU: ${sku}`
              }
            ]
          };
        }
        
        // Get full details for each related product
        const productPromises = relatedProducts.map(related => 
          callMagentoApi(`/products/${related.linked_product_sku}`)
            .then(product => formatProduct(product))
            .catch(err => ({ sku: related.linked_product_sku, error: err.message }))
        );
        
        const products = await Promise.all(productPromises);
        
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(products, null, 2)
            }
          ]
        };
      } catch (error) {
        return {
          content: [
            {
              type: "text",
              text: `Error fetching related products: ${error.message}`
            }
          ],
          isError: true
        };
      }
    }
  • Input schema defining the required 'sku' parameter as a string.
      sku: z.string().describe("The SKU (Stock Keeping Unit) of the product")
    },
  • mcp-server.js:533-583 (registration)
    Tool registration using `server.tool()` including name, description, input schema, and handler reference.
    server.tool(
      "get_related_products",
      "Get products related to a specific product by SKU",
      {
        sku: z.string().describe("The SKU (Stock Keeping Unit) of the product")
      },
      async ({ sku }) => {
        try {
          const relatedProducts = await callMagentoApi(`/products/${sku}/links/related`);
          
          if (!relatedProducts || relatedProducts.length === 0) {
            return {
              content: [
                {
                  type: "text",
                  text: `No related products found for SKU: ${sku}`
                }
              ]
            };
          }
          
          // Get full details for each related product
          const productPromises = relatedProducts.map(related => 
            callMagentoApi(`/products/${related.linked_product_sku}`)
              .then(product => formatProduct(product))
              .catch(err => ({ sku: related.linked_product_sku, error: err.message }))
          );
          
          const products = await Promise.all(productPromises);
          
          return {
            content: [
              {
                type: "text",
                text: JSON.stringify(products, null, 2)
              }
            ]
          };
        } catch (error) {
          return {
            content: [
              {
                type: "text",
                text: `Error fetching related products: ${error.message}`
              }
            ],
            isError: true
          };
        }
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool's function but omits critical details: it doesn't specify the return format (e.g., list of SKUs, product details), pagination, rate limits, authentication needs, or error handling. For a read operation with no annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly for an AI agent.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex data (related products). It doesn't explain what 'related' means, the return format, or any behavioral traits like performance or limitations. For a tool with no structured output documentation, the description should provide more context to be fully helpful.

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 has 100% description coverage, with the 'sku' parameter clearly documented. The description adds no additional semantic context beyond what the schema provides, such as examples of valid SKU formats or constraints. Baseline score of 3 is appropriate since the schema does the heavy lifting, but the description doesn't enhance parameter understanding.

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

Purpose4/5

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

The description clearly states the action ('Get') and target ('products related to a specific product by SKU'), distinguishing it from siblings like get_product_by_sku (which retrieves the product itself) or search_products (which searches broadly). However, it doesn't specify the relationship criteria (e.g., based on categories, purchases, or attributes), leaving some ambiguity.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like get_product_categories (for category-based relations) or search_products (for general product searches). The description implies usage when related products are needed, but lacks explicit context or exclusions, such as whether it's for upselling, recommendations, or inventory management.

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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