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boldcommerce

Magento 2 MCP Server

by boldcommerce

get_product_stock

Retrieve current stock levels for Magento 2 products using their SKU to manage inventory and prevent overselling.

Instructions

Get stock information for a product by SKU

Input Schema

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

Implementation Reference

  • Handler function that fetches product stock information from the Magento API endpoint `/stockItems/{sku}` using the provided SKU and returns the data as JSON or an error response.
    async ({ sku }) => {
      try {
        const stockData = await callMagentoApi(`/stockItems/${sku}`);
        
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(stockData, null, 2)
            }
          ]
        };
      } catch (error) {
        return {
          content: [
            {
              type: "text",
              text: `Error fetching stock information: ${error.message}`
            }
          ],
          isError: true
        };
      }
    }
  • Input schema using Zod, defining the required 'sku' parameter as a string with description.
    {
      sku: z.string().describe("The SKU (Stock Keeping Unit) of the product")
    },
  • mcp-server.js:586-616 (registration)
    Registration of the 'get_product_stock' tool via server.tool(), specifying name, description, input schema, and handler function.
    server.tool(
      "get_product_stock",
      "Get stock information for a product by SKU",
      {
        sku: z.string().describe("The SKU (Stock Keeping Unit) of the product")
      },
      async ({ sku }) => {
        try {
          const stockData = await callMagentoApi(`/stockItems/${sku}`);
          
          return {
            content: [
              {
                type: "text",
                text: JSON.stringify(stockData, null, 2)
              }
            ]
          };
        } catch (error) {
          return {
            content: [
              {
                type: "text",
                text: `Error fetching stock information: ${error.message}`
              }
            ],
            isError: true
          };
        }
      }
    );
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but fails to mention critical details like whether this is a read-only operation, potential rate limits, authentication requirements, or the format of the returned stock information. This leaves significant gaps in understanding the tool's behavior.

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, direct sentence that efficiently conveys the core purpose without any unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick comprehension.

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 effective tool use. It does not cover behavioral aspects like safety, performance, or return values, which are crucial for an AI agent to invoke the tool correctly in a real-world context.

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, clearly documenting the 'sku' parameter. The description adds no additional semantic context beyond what the schema provides, such as examples or constraints, so it meets the baseline score without enhancing 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 verb ('Get') and resource ('stock information for a product by SKU'), making the purpose immediately understandable. However, it does not explicitly differentiate from sibling tools like 'get_product_by_sku', which might retrieve broader product details rather than just stock information, leaving room for 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?

The description provides no guidance on when to use this tool versus alternatives such as 'get_product_by_sku' or 'search_products', nor does it mention any prerequisites or exclusions. This lack of context could lead to incorrect tool selection by an AI agent.

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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