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Naver Search MCP Server

datalab_shopping_keyword_by_device

Analyze Naver Shopping keyword trends by device type (PC or mobile) over a specified time period to understand user behavior and optimize marketing strategies.

Instructions

Perform a trend analysis on Naver Shopping keywords by device. (네이버 쇼핑 키워드 기기별 트렌드 분석)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesCategory code
deviceYesDevice type
endDateYesEnd date (yyyy-mm-dd)
keywordYesSearch keyword
startDateYesStart date (yyyy-mm-dd)
timeUnitYesTime unit

Implementation Reference

  • The main handler function that takes input parameters typed as DatalabShoppingKeywordDevice and calls the NaverSearchClient's datalabShoppingKeywordByDevice method with mapped arguments.
    export async function handleShoppingKeywordByDeviceTrend(
      params: DatalabShoppingKeywordDevice
    ) {
      return client.datalabShoppingKeywordByDevice({
        startDate: params.startDate,
        endDate: params.endDate,
        timeUnit: params.timeUnit,
        category: params.category,
        keyword: params.keyword,
        device: params.device,
      });
    }
  • Zod schema defining the input shape for the tool, extending DatalabBaseSchema with category, keyword, and device fields. Used in MCP registration as inputSchema.
    // 키워드 기기별 트렌드 스키마
    export const DatalabShoppingKeywordDeviceSchema = DatalabBaseSchema.extend({
      category: z.string().describe("Category code"),
      keyword: z.string().describe("Search keyword"),
      device: z.enum(["pc", "mo"]).describe("Device type"),
    });
  • src/index.ts:377-390 (registration)
    MCP server tool registration, specifying the tool name, description, input schema, and handler invocation via datalabToolHandlers.
      "datalab_shopping_keyword_by_device",
      {
        description:
          "📱🔍 Analyze keyword performance by device within shopping categories. Use find_category first to get category codes. Perfect for understanding mobile vs desktop shopping behavior for specific products. (쇼핑 키워드 기기별 트렌드 - 먼저 find_category 도구로 카테고리 코드를 찾으세요)",
        inputSchema: DatalabShoppingKeywordDeviceSchema.shape,
      },
      async (args) => {
        const result =
          await datalabToolHandlers.datalab_shopping_keyword_by_device(args);
        return {
          content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
        };
      }
    );
  • Core client method that performs the HTTP POST request to the Naver DataLab API endpoint for shopping keyword trends by device.
    async datalabShoppingKeywordByDevice(
      params: DatalabShoppingKeywordRequest
    ): Promise<DatalabShoppingResponse> {
      return this.post(
        `${this.datalabBaseUrl}/shopping/category/keyword/device`,
        params
      );
    }
  • Entry in datalabToolHandlers map that logs the call and delegates to the main handleShoppingKeywordByDeviceTrend function.
    datalab_shopping_keyword_by_device: (args) => {
      console.error("datalab_shopping_keyword_by_device called with args:", JSON.stringify(args, null, 2));
      return handleShoppingKeywordByDeviceTrend(args);
    },
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states it performs 'trend analysis' but doesn't describe what the analysis returns (e.g., time-series data, metrics), whether it's read-only or has side effects, rate limits, authentication needs, or error conditions. For a tool with 6 required parameters and no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two parallel sentences (English and Korean). Both sentences directly state the tool's purpose without unnecessary elaboration. However, the Korean translation adds redundancy rather than new information, slightly reducing efficiency.

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?

For a tool with 6 required parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the trend analysis returns, how results are structured, or provide context about the Naver Shopping platform. With rich parameter requirements but poor behavioral disclosure, the description leaves too many gaps for effective tool use.

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?

Schema description coverage is 100%, so the schema already documents all 6 parameters with descriptions and enums. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how keyword interacts with device) or provide usage examples. With complete schema coverage, baseline 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 tool performs 'trend analysis on Naver Shopping keywords by device' with a Korean translation. It specifies the verb 'perform trend analysis' and resource 'Naver Shopping keywords by device', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_device' or 'datalab_shopping_keyword_by_age', which would require more specific 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'datalab_shopping_by_device' (which might analyze shopping trends by device without keyword focus) or 'datalab_shopping_keyword_by_age' (which does keyword analysis by age instead of device). There's no context about appropriate use cases or exclusions.

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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