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isnow890

Naver Search MCP Server

datalab_shopping_category

Analyze Naver Shopping category trends by specifying time units, date ranges, and category codes to identify market patterns and consumer behavior.

Instructions

Perform a trend analysis on Naver Shopping category. (네이버 쇼핑 카테고리별 트렌드 분석)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesArray of category name and code pairs
endDateYesEnd date (yyyy-mm-dd)
startDateYesStart date (yyyy-mm-dd)
timeUnitYesTime unit

Implementation Reference

  • Handler function that executes the core logic by delegating to the Naver client for shopping category trend analysis.
    export async function handleShoppingCategoryTrend(params: DatalabShopping) {
      return client.datalabShoppingCategory(params);
    }
  • Zod schema defining the input parameters for the datalab_shopping_category tool, including category details.
    // 쇼핑 카테고리 스키마
    export const DatalabShoppingSchema = DatalabBaseSchema.extend({
      category: z
        .array(
          z.object({
            name: z.string().describe("Category name"),
            param: z.array(z.string()).describe("Category codes"),
          })
        )
        .describe("Array of category name and code pairs"),
    });
  • src/index.ts:283-296 (registration)
    MCP server tool registration for datalab_shopping_category, including description and input schema reference.
    server.registerTool(
      "datalab_shopping_category",
      {
        description:
          "🛍️ STEP 2: Analyze shopping category trends over time. Use find_category first to get category codes. BUSINESS CASES: Market size analysis, seasonal trend identification, category performance comparison. EXAMPLE: Compare '패션의류' vs '화장품' trends over 6 months. For current period analysis, use get_current_korean_time to set proper date ranges. (네이버 쇼핑 카테고리별 트렌드 분석 - 먼저 find_category 도구로 카테고리 코드를 찾고, 현재 기간 분석시 get_current_korean_time으로 적절한 날짜 범위 설정)",
        inputSchema: DatalabShoppingSchema.shape,
      },
      async (args) => {
        const result = await datalabToolHandlers.datalab_shopping_category(args);
        return {
          content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
        };
      }
    );
  • Client method that performs the actual HTTP POST request to Naver DataLab API for shopping categories.
    async datalabShoppingCategory(
      params: DatalabShoppingCategoryRequest
    ): Promise<DatalabShoppingResponse> {
      return this.post(`${this.datalabBaseUrl}/shopping/categories`, params);
  • Handler map entry that logs args and delegates to the specific trend handler.
    datalab_shopping_category: (args) => {
      console.error("datalab_shopping_category called with args:", JSON.stringify(args, null, 2));
      return handleShoppingCategoryTrend(args);

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