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.

⚠️ Smithery Installation Notice: Due to compatibility issues with the Smithery platform, npx installation is recommended starting from version 1.0.40. Smithery installation is only supported up to version 1.0.30.

Version History

1.0.44 (2025-08-31)

get_current_korean_time tool added - Essential time context tool for Korean timezone
Enhanced all existing tool descriptions to reference time tool for temporal queries
Improved temporal context handling for "today", "now", "current" searches
Comprehensive Korean time formatting with multiple output formats

1.0.4 (2025-08-21)

find_category tool added - with fuzzy matching and ranking system support
Enhanced parameter validation with Zod schema
Improved category search workflow

1.0.30 (2025-08-04)

MCP SDK upgraded to 1.17.1
Fixed compatibility issues with Smithery specification changes
Added comprehensive DataLab shopping category code documentation

1.0.2 (2025-04-26)

README updated: cafe article search tool and version history section improved

1.0.1 (2025-04-26)

Cafe article search feature added
Shopping category info added to zod
Source code refactored

1.0.0 (2025-04-08)

Initial release

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

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

Tool Details

Available tools:

🕐 Time & Context Tools

get_current_korean_time: Get current Korean time (KST) with comprehensive date/time information. Essential for understanding "today", "now", or "current" context in Korean timezone. Always use this tool when temporal context is needed for searches or analysis.

🆕 Category Search

find_category: Category search tool - No more need to manually check category numbers via URL for trend and shopping insight searches. The LLM will find it out as you say.

Search Tools

search_webkr: Search Naver web documents
search_news: Search Naver news
search_blog: Search Naver blogs
search_cafearticle: Search Naver cafe articles
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
datalab_shopping_category: Analyze shopping category trends
datalab_shopping_by_device: Analyze shopping trends by device
datalab_shopping_by_gender: Analyze shopping trends by gender
datalab_shopping_by_age: Analyze shopping trends by age group
datalab_shopping_keywords: Analyze shopping keyword trends
datalab_shopping_keyword_by_device: Analyze shopping keyword trends by device
datalab_shopping_keyword_by_gender: Analyze shopping keyword trends by gender
datalab_shopping_keyword_by_age: Analyze shopping keyword trends by age group

Complete Category List:

For a complete list of category codes, you can download from Naver Shopping Partner Center or extract them by browsing Naver Shopping categories.

🎯 Business Use Cases & Scenarios

🛍️ E-commerce Market Research

// Fashion trend discovery
find_category("fashion") → Check top fashion categories and codes
datalab_shopping_category → Analyze seasonal fashion trends
datalab_shopping_age → Identify fashion target demographics
datalab_shopping_keywords → Compare "dress" vs "jacket" vs "coat"

📱 Digital Marketing Strategy

// Beauty industry analysis
find_category("cosmetics") → Find beauty categories
datalab_shopping_gender → 95% female vs 5% male shoppers
datalab_shopping_device → Mobile dominance in beauty shopping
datalab_shopping_keywords → "tint" vs "lipstick" keyword performance

🏢 Business Intelligence & Competitive Analysis

// Tech product insights
find_category("smartphone") → Check electronics categories
datalab_shopping_category → Track iPhone vs Galaxy trends
datalab_shopping_age → 20-30s as main smartphone buyers
datalab_shopping_device → PC vs mobile shopping behavior

📊 Seasonal Business Planning

// Holiday shopping analysis
find_category("gift") → Gift categories
datalab_shopping_category → Black Friday, Christmas trends
datalab_shopping_keywords → "Mother's Day gift" vs "birthday gift"
datalab_shopping_age → Age-based gift purchasing patterns

🎯 Customer Persona Development

// Fitness market analysis
find_category("exercise") → Sports/fitness categories
datalab_shopping_gender → Male vs female fitness spending
datalab_shopping_age → Primary fitness demographics (20-40s)
datalab_shopping_keywords → "home workout" vs "gym" trend analysis

📈 Advanced Analysis Scenarios

Market Entry Strategy

Category Discovery: Use find_category to explore market segments
Trend Analysis: Identify growing vs declining categories
Demographic Targeting: Age/gender analysis for customer targeting
Competitive Intelligence: Keyword performance comparison
Device Strategy: Mobile vs PC shopping optimization

Product Launch Planning

Market Validation: Category growth trends and seasonality
Target Customers: Demographic analysis for product positioning
Marketing Channels: Device preferences for advertising strategy
Competitive Landscape: Keyword competition and opportunities
Pricing Strategy: Category performance and price correlation

Performance Monitoring

Category Health: Monitor product category trends
Keyword Tracking: Track brand and product keyword performance
Demographic Shifts: Monitor changing customer demographics
Seasonal Patterns: Plan inventory and marketing campaigns
Competitive Benchmarking: Compare performance against category averages

Quick Reference: Popular Category Codes

Category	Code	Korean
Fashion/Clothing	50000000	패션의류
Cosmetics/Beauty	50000002	화장품/미용
Digital/Electronics	50000003	디지털/가전
Sports/Leisure	50000004	스포츠/레저
Food/Beverages	50000008	식품/음료
Health/Medical	50000009	건강/의료용품

💡 Tip: Use find_category with fuzzy searches like "beauty", "fashion", "electronics" to easily find categories.

Installation

Method 1: NPX Installation (Recommended)

The easiest way to use this MCP server is through NPX. For detailed package information, see the NPM package page.

Claude Desktop Configuration

Add to Claude Desktop config file (%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS/Linux):

{
  "mcpServers": {
    "naver-search": {
      "command": "npx",
      "args": ["-y", "@isnow890/naver-search-mcp"],
      "env": {
        "NAVER_CLIENT_ID": "your_client_id",
        "NAVER_CLIENT_SECRET": "your_client_secret"
      }
    }
  }
}

Cursor AI Configuration

Add to mcp.json:

{
  "mcpServers": {
    "naver-search": {
      "command": "npx",
      "args": ["-y", "@isnow890/naver-search-mcp"],
      "env": {
        "NAVER_CLIENT_ID": "your_client_id",
        "NAVER_CLIENT_SECRET": "your_client_secret"
      }
    }
  }
}

Method 2: Local Installation

For local development or custom modifications:

Step 1: Download and Build Source Code

Clone with Git

git clone https://github.com/isnow890/naver-search-mcp.git
cd naver-search-mcp
npm install
npm run build

Or Download ZIP File

Download the latest version from GitHub Releases
Extract the ZIP file to your desired location
Navigate to the extracted folder in terminal:

cd /path/to/naver-search-mcp
npm install
npm run build

⚠️ Important: You must run npm run build after installation to generate the dist folder that contains the compiled JavaScript files.

Step 2: Claude Desktop Configuration

After building, you'll need the following information:

NAVER_CLIENT_ID: Client ID from Naver Developers
NAVER_CLIENT_SECRET: Client Secret from Naver Developers
Installation Path: Absolute path to the downloaded folder

Windows Configuration

Add to Claude Desktop config file (%APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "naver-search": {
      "type": "stdio",
      "command": "cmd",
      "args": [
        "/c",
        "node",
        "C:\\path\\to\\naver-search-mcp\\dist\\src\\index.js"
      ],
      "cwd": "C:\\path\\to\\naver-search-mcp",
      "env": {
        "NAVER_CLIENT_ID": "your-naver-client-id",
        "NAVER_CLIENT_SECRET": "your-naver-client-secret"
      }
    }
  }
}

macOS/Linux Configuration

Add to Claude Desktop config file (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "naver-search": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/naver-search-mcp/dist/src/index.js"],
      "cwd": "/path/to/naver-search-mcp",
      "env": {
        "NAVER_CLIENT_ID": "your-naver-client-id",
        "NAVER_CLIENT_SECRET": "your-naver-client-secret"
      }
    }
  }
}

Path Configuration Important Notes

⚠️ Important: You must change the following paths in the above configuration to your actual installation paths:

Windows: Change C:\\path\\to\\naver-search-mcp to your actual downloaded folder path
macOS/Linux: Change /path/to/naver-search-mcp to your actual downloaded folder path
Build Path: Make sure the path points to dist/src/index.js (not just index.js)

Finding your path:

# Check current location
pwd

# Absolute path examples
# Windows: C:\Users\username\Downloads\naver-search-mcp
# macOS: /Users/username/Downloads/naver-search-mcp
# Linux: /home/username/Downloads/naver-search-mcp

Step 3: Restart Claude Desktop

After completing the configuration, completely close and restart Claude Desktop to activate the Naver Search MCP server.

Alternative Installation Methods

Method 3: Legacy Smithery Installation (Only for v1.0.30 and below)

⚠️ Note: This method only works for versions 1.0.30 and below due to platform compatibility issues.

For Claude Desktop:

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

For other AI clients:

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

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

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

Method 4: Docker Installation

For containerized deployment:

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

Docker configuration for Claude Desktop:

{
  "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"
      ]
    }
  }
}

Build

Docker build:

docker build -t mcp/naver-search .

License

MIT License