Skip to main content
Glama
wspotter

MCP Art Supply Store

by wspotter

suggest_bundle

Generate product bundle recommendations based on frequently purchased together items and complementary art supplies to help customers discover relevant combinations.

Instructions

Suggest product bundles based on frequently bought together items or complementary products.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseSkuYesBase product SKU to build bundle around

Implementation Reference

  • The handler function for the 'suggest_bundle' tool. It retrieves the base product by SKU, finds up to 3 complementary products from the same category, calculates the total bundle price with a 15% discount, and returns a formatted suggestion text.
    case 'suggest_bundle': {
      const baseSku = String(args?.baseSku || '');
      const baseItem = storeData.inventory.find(i => i.id === baseSku);
      
      if (!baseItem) {
        return { content: [{ type: 'text', text: `āŒ Product ${baseSku} not found` }] };
      }
      
      const complementary = storeData.inventory
        .filter(i => i.category === baseItem.category || i.id !== baseSku)
        .slice(0, 3);
      
      const bundlePrice = baseItem.price + complementary.reduce((sum, item) => sum + item.price, 0);
      const bundleDiscount = bundlePrice * 0.15;
      
      return {
        content: [{
          type: 'text',
          text: `šŸŽ Suggested Bundle:\n\nšŸ“¦ Base Item:\n• ${baseItem.name} - $${baseItem.price}\n\nāž• Add to Bundle:\n${complementary.map(item =>
            `• ${item.name} - $${item.price}`
          ).join('\n')}\n\nšŸ’° Bundle Price:\n- Individual Total: $${bundlePrice.toFixed(2)}\n- Bundle Discount (15%): -$${bundleDiscount.toFixed(2)}\n- Bundle Price: $${(bundlePrice - bundleDiscount).toFixed(2)}\nšŸ’µ Customer Saves: $${bundleDiscount.toFixed(2)}`
        }]
      };
    }
  • The tool definition including name, description, and input schema for 'suggest_bundle'. This is part of the tools array registered for listing available tools.
      name: 'suggest_bundle',
      description: 'Suggest product bundles based on frequently bought together items or complementary products.',
      inputSchema: {
        type: 'object',
        properties: {
          baseSku: { type: 'string', description: 'Base product SKU to build bundle around' },
        },
        required: ['baseSku'],
      },
    },
  • src/index.ts:516-518 (registration)
    Registers the handler for ListToolsRequestSchema which returns the full list of tools, including 'suggest_bundle'.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return { tools };
    });
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 suggests bundles but doesn't describe how suggestions are generated (e.g., algorithm details, data sources), whether it's read-only or has side effects, performance characteristics, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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, efficient sentence that front-loads the core purpose without unnecessary details. It uses clear language ('suggest product bundles') and specifies the basis ('frequently bought together items or complementary products'), making it easy to parse. Every word contributes to understanding the tool's function.

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 complexity of a suggestion tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., how suggestions are ranked), output format (e.g., list of bundles with prices), and error cases (e.g., invalid SKU handling). For a tool that likely returns structured data, more context is needed to use it effectively.

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 parameter 'baseSku' documented as 'Base product SKU to build bundle around.' The description adds no additional parameter semantics beyond this, such as SKU format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 tool's purpose: 'Suggest product bundles based on frequently bought together items or complementary products.' It specifies the verb ('suggest') and resource ('product bundles'), and distinguishes itself from siblings like 'get_customer_recommendations' or 'search_products' by focusing on bundle creation rather than general recommendations or product search. However, it doesn't explicitly differentiate from potential overlapping tools like 'calculate_discount' for bundle pricing.

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. It doesn't mention prerequisites (e.g., needing a valid SKU), exclusions (e.g., not for single products), or comparisons to siblings like 'get_customer_recommendations' for individual product suggestions. The context is implied (bundle creation around a base product), but explicit usage instructions are absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wspotter/mcpart'

If you have feedback or need assistance with the MCP directory API, please join our Discord server