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
1.0.4 (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)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
🆕 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_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_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
- 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