Naver Search MCP Server

by isnow890
Verified

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Integrations

  • Optional containerization platform for deploying the MCP server in an isolated environment.

  • Enables comprehensive search across various Naver services (web, news, blogs, shopping, images, KnowledgeiN, books, encyclopedia, academic papers, local places) and data trend analysis through Naver Search API and DataLab API.

  • Required runtime environment for the MCP server, version 18 or higher needed for server operation.

Naver Search MCP Server

MCP server for Naver Search API and DataLab API integration, enabling comprehensive search across various Naver services and data trend analysis.

Prerequisites

  • Naver Developers API Key (Client ID and Secret)
  • Node.js 18 or higher
  • NPM 8 or higher
  • Docker (optional, for container deployment)

Getting API Keys

  1. Visit Naver Developers
  2. Click "Register Application"
  3. Enter application name and select ALL of the following APIs:
    • Search (for blog, news, book search, etc.)
    • DataLab (Search Trends)
    • DataLab (Shopping Insight)
  4. Set the obtained Client ID and Client Secret as environment variables

Installation

To install Naver Search MCP Server automatically via Smithery, use one of these commands based on your AI client:

For Claude Desktop:

npx -y @smithery/cli@latest install @isnow890/naver-search-mcp --client claude

For Cursor:

npx -y @smithery/cli@latest install @isnow890/naver-search-mcp --client cursor

For Windsurf:

npx -y @smithery/cli@latest install @isnow890/naver-search-mcp --client windsurf

For Cline:

npx -y @smithery/cli@latest install @isnow890/naver-search-mcp --client cline

The installer will prompt you for:

  • NAVER_CLIENT_ID
  • NAVER_CLIENT_SECRET

Option 2: Manual Installation

Environment Variables

# Windows set NAVER_CLIENT_ID=your_client_id set NAVER_CLIENT_SECRET=your_client_secret # Linux/Mac export NAVER_CLIENT_ID=your_client_id export NAVER_CLIENT_SECRET=your_client_secret

Run with NPX

npx @modelcontextprotocol/server-naver-search

Run with Docker

docker run -i --rm \ -e NAVER_CLIENT_ID=your_client_id \ -e NAVER_CLIENT_SECRET=your_client_secret \ mcp/naver-search

Cursor Desktop Configuration

Add to claude_desktop_config.json:

{ "mcpServers": { "naver-search": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-naver-search"], "env": { "NAVER_CLIENT_ID": "your_client_id", "NAVER_CLIENT_SECRET": "your_client_secret" } } } }

For Docker:

{ "mcpServers": { "naver-search": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "NAVER_CLIENT_ID=your_client_id", "-e", "NAVER_CLIENT_SECRET=your_client_secret", "mcp/naver-search" ] } } }

Tool Details

Search Tools

Each search tool accepts these parameters:

  • query: Search term (required)
  • display: Number of results to show (default: 10)
  • start: Start position (default: 1)
  • sort: Sort method (sim: similarity, date: date)

Available search tools:

  • search_webkr: Search Naver web documents
  • search_news: Search Naver news
  • search_blog: Search Naver blogs
  • search_shop: Search Naver shopping
  • search_image: Search Naver images
  • search_kin: Search Naver KnowledgeiN
  • search_book: Search Naver books
  • search_encyc: Search Naver encyclopedia
  • search_academic: Search Naver academic papers
  • search_local: Search Naver local places

DataLab Tools

  • datalab_search
    • Analyze search term trends
    • Parameters:
      • startDate: Analysis start date (YYYY-MM-DD)
      • endDate: Analysis end date (YYYY-MM-DD)
      • timeUnit: Analysis time unit (date/week/month)
      • keywordGroups: Array of keyword groups
        • groupName: Group name
        • keywords: Array of keywords
  • datalab_shopping_category
    • 쇼핑 카테고리 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드와 이름 배열
  • datalab_shopping_by_device
    • 기기별 쇼핑 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • device: 기기 유형 (pc: PC, mo: 모바일)
  • datalab_shopping_by_gender
    • 성별 쇼핑 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • gender: 성별 (f: 여성, m: 남성)
  • datalab_shopping_by_age
    • 연령대별 쇼핑 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • ages: 연령대 배열 (예: ["10", "20", "30"])
  • datalab_shopping_keywords
    • 쇼핑 키워드 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • keyword: 키워드와 이름 배열
  • datalab_shopping_keyword_by_device
    • 쇼핑 키워드 기기별 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • keyword: 검색 키워드
      • device: 기기 유형 (pc: PC, mo: 모바일)
  • datalab_shopping_keyword_by_gender
    • 쇼핑 키워드 성별 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • keyword: 검색 키워드
      • gender: 성별 (f: 여성, m: 남성)
  • datalab_shopping_keyword_by_age
    • 쇼핑 키워드 연령별 트렌드 분석
    • 매개변수:
      • startDate: 분석 시작일 (YYYY-MM-DD)
      • endDate: 분석 종료일 (YYYY-MM-DD)
      • timeUnit: 분석 시간 단위 (date: 일간, week: 주간, month: 월간)
      • category: 쇼핑 카테고리 코드
      • keyword: 검색 키워드
      • ages: 연령대 배열 (예: ["10", "20", "30"])

Build

Docker build:

docker build -t mcp/naver-search .

License

MIT License

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An MCP server that enables searching various content types (news, blogs, shopping, images, etc.) through Naver's search API.

  1. Prerequisites
    1. Getting API Keys
      1. Installation
        1. Option 1: Quick Install via Smithery (Recommended)
        2. Option 2: Manual Installation
      2. Cursor Desktop Configuration
        1. Tool Details
          1. Search Tools
          2. DataLab Tools
        2. Build
          1. License
            ID: kpv7kdyboo