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AgentX-ai

Mailchimp MCP Server

by AgentX-ai

list_products

Retrieve all products from a Mailchimp-connected store by providing the store ID to manage product data for email marketing campaigns.

Instructions

List all products in a store

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYesThe store ID

Implementation Reference

  • The core handler function that executes the logic to list products for a given store ID by making a paginated request to the Mailchimp E-commerce API endpoint.
    async listProducts(
      storeId: string
    ): Promise<{ products: MailchimpProduct[] }> {
      return await this.makePaginatedRequest(
        `/ecommerce/stores/${storeId}/products`,
        "created_at",
        "DESC"
      );
    }
  • Tool registration definition including name, description, and input schema for the 'list_products' tool in getToolDefinitions.
    // E-commerce Products
    {
      name: "list_products",
      description: "List all products in a store",
      inputSchema: {
        type: "object",
        properties: {
          store_id: {
            type: "string",
            description: "The store ID",
          },
        },
        required: ["store_id"],
      },
  • Input schema definition validating the required 'store_id' parameter for the tool.
    description: "List all products in a store",
    inputSchema: {
      type: "object",
      properties: {
        store_id: {
          type: "string",
          description: "The store ID",
        },
      },
      required: ["store_id"],
  • Dispatcher handler case in handleToolCall that invokes the service handler and formats the response as MCP tool output.
    case "list_products":
      const products = await service.listProducts(args.store_id);
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              products.products.map((p) => ({
                id: p.id,
                title: p.title,
                type: p.type,
                vendor: p.vendor,
              })),
              null,
              2
            ),
          },
        ],
      };
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 it's a list operation, implying it's read-only, but doesn't cover critical aspects like pagination, rate limits, error handling, or what 'all products' entails (e.g., if it includes archived items). This is a significant gap for a tool with no annotation coverage.

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 function without unnecessary words. It's front-loaded and appropriately sized for a simple list operation, earning its place with zero waste.

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 tool's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It doesn't address behavioral traits like pagination or error handling, and with no output schema, it should at least hint at the return format. This leaves gaps in understanding how to effectively use the tool.

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 'store_id' parameter clearly documented. The description adds no additional meaning beyond the schema, such as explaining the scope of 'store_id' or how it affects the listing. Given the high schema coverage, a 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 verb ('List') and resource ('products in a store'), making the purpose specific and understandable. However, it doesn't distinguish this tool from similar sibling tools like 'list_orders' or 'list_members', which follow the same pattern, so it lacks sibling differentiation.

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, context, or exclusions, such as whether it's for browsing all products versus filtered queries. This leaves the agent with minimal usage direction.

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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