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devlimelabs

Lulu Print MCP Server

by devlimelabs

get-product-details

Retrieve specifications and details for Lulu print products using product IDs to access accurate information for print-on-demand services.

Instructions

Get specifications and details for a Lulu product

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_idYesLulu product ID

Implementation Reference

  • src/server.ts:57-90 (registration)
    Registration of the 'get-product-details' MCP tool, including description, input schema, and handler function.
    server.tool(
      "get-product-details",
      "Get specifications and details for a Lulu product",
      {
        product_id: z.string().describe("Lulu product ID")
      },
      async ({ product_id }) => {
        try {
          // TODO: Implement actual API call
          const mockProduct = {
            product_id,
            name: "Sample Product",
            type: "Paperback",
            dimensions: "6x9 inches",
            message: "This is mock data - actual API integration pending"
          };
          
          return {
            content: [{
              type: "text" as const,
              text: JSON.stringify(mockProduct, null, 2)
            }]
          };
        } catch (error: any) {
          return {
            content: [{
              type: "text" as const,
              text: `Error: ${error.message}`
            }],
            isError: true
          };
        }
      }
    );
  • Handler function for 'get-product-details' tool that returns mock product details based on product_id, with error handling.
    async ({ product_id }) => {
      try {
        // TODO: Implement actual API call
        const mockProduct = {
          product_id,
          name: "Sample Product",
          type: "Paperback",
          dimensions: "6x9 inches",
          message: "This is mock data - actual API integration pending"
        };
        
        return {
          content: [{
            type: "text" as const,
            text: JSON.stringify(mockProduct, null, 2)
          }]
        };
      } catch (error: any) {
        return {
          content: [{
            type: "text" as const,
            text: `Error: ${error.message}`
          }],
          isError: true
        };
      }
    }
  • Input schema for the 'get-product-details' tool, defining product_id as a required string using Zod.
    {
      product_id: z.string().describe("Lulu product ID")
    },
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. The description states it 'gets' details, implying a read-only operation, but doesn't clarify permissions, rate limits, error conditions, or what 'specifications and details' includes. For a tool with zero annotation coverage, this is insufficient behavioral context.

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 with the core action and resource, making it easy to parse. Every word earns its place, achieving optimal conciseness.

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

Completeness3/5

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

Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, and output format. Without annotations or output schema, the agent must rely heavily on the description, which is incomplete for informed tool selection.

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 schema description coverage is 100%, with the single parameter 'product_id' documented as 'Lulu product ID'. The description doesn't add any parameter-specific information beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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 resource ('specifications and details for a Lulu product'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'calculate-print-job-cost', which appears unrelated but could potentially overlap in product context. The description is specific but lacks sibling distinction.

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. The description doesn't mention prerequisites, context, or exclusions. With only one sibling tool that seems unrelated (cost calculation), the lack of explicit usage guidelines leaves the agent to infer based on tool names alone.

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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