Skip to main content
Glama
isnow890

Naver Search MCP Server

datalab_shopping_by_gender

Analyze Naver Shopping trends by gender to identify consumer preferences. Input category, time range, and gender for detailed insights.

Instructions

Perform a trend analysis on Naver Shopping by gender. (네이버 쇼핑 성별 트렌드 분석)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesCategory code
endDateYesEnd date (yyyy-mm-dd)
genderYesGender
startDateYesStart date (yyyy-mm-dd)
timeUnitYesTime unit

Implementation Reference

  • The core handler function that takes typed parameters (DatalabShoppingGender) and invokes the Naver client method to fetch shopping category trends by gender.
    export async function handleShoppingByGenderTrend(
      params: DatalabShoppingGender
    ) {
      return client.datalabShoppingByGender({
        startDate: params.startDate,
        endDate: params.endDate,
        timeUnit: params.timeUnit,
        category: params.category,
        gender: params.gender,
      });
    }
  • Zod schema defining the input parameters for the datalab_shopping_by_gender tool: startDate, endDate, timeUnit (inherited), category, and gender.
    export const DatalabShoppingGenderSchema = DatalabBaseSchema.extend({
      category: z.string().describe("Category code"),
      gender: z.enum(["f", "m"]).describe("Gender"),
    });
  • src/index.ts:320-337 (registration)
    MCP tool registration in the main server, specifying the tool name, description, input schema (derived from DatalabShoppingGenderSchema), and thin wrapper handler that delegates to datalabToolHandlers.
    "datalab_shopping_by_gender",
    {
      description:
        "👥 Analyze shopping trends by gender (Male vs Female). Use find_category first. BUSINESS CASES: Gender-targeted marketing, product positioning, demographic analysis. EXAMPLE: '화장품 쇼핑에서 남녀 비율은?' For current gender trends, use get_current_korean_time to set proper analysis period. (성별 쇼핑 트렌드 분석 - 먼저 find_category 도구로 카테고리 코드를 찾고, 현재 성별 트렌드 분석시 get_current_korean_time으로 적절한 분석 기간 설정)",
      inputSchema: DatalabShoppingGenderSchema.pick({
        startDate: true,
        endDate: true,
        timeUnit: true,
        category: true,
        gender: true,
      }).shape,
    },
    async (args) => {
      const result = await datalabToolHandlers.datalab_shopping_by_gender(args);
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
  • Client method that performs the actual HTTP POST request to Naver DataLab API endpoint for shopping trends by gender.
    async datalabShoppingByGender(
      params: DatalabShoppingGenderRequest
    ): Promise<DatalabShoppingResponse> {
      return this.post(`${this.datalabBaseUrl}/shopping/category/gender`, params);
    }
  • Entry in datalabToolHandlers map that logs the call and delegates to the specific handleShoppingByGenderTrend function.
    datalab_shopping_by_gender: (args) => {
      console.error("datalab_shopping_by_gender called with args:", JSON.stringify(args, null, 2));
      return handleShoppingByGenderTrend(args);

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/isnow890/naver-search-mcp'

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