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.

Version History

###### 1.0.45 (2025-09-28)

• Resolved Smithery compatibility issues so you can use the latest features through Smithery
• Replaced the Excel export in category search with JSON for better compatibility
• Restored the search_webkr tool for Korean web search
• Fully compatible with Smithery platform installation

###### 1.0.44 (2025-08-31)

• Added the get_current_korean_time tool for essential Korea Standard Time context
• Referenced the time tool across existing tool descriptions for temporal queries
• Improved handling of "today", "now", and "current" searches with temporal context
• Expanded Korean date and time formatting outputs with multiple formats

###### 1.0.40 (2025-08-21)

• Added the find_category tool with fuzzy matching so you no longer need to check category numbers manually in URLs
• Enhanced parameter validation with Zod schema
• Improved the category search workflow
• Implemented a level-based category ranking system that prioritizes top-level categories

###### 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

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

Tool Details

Available tools:

🕐 Time & Context Tools

• get_current_korean_time: Fetch the current Korea Standard Time (KST) along with comprehensive date and time details. Use this whenever a search or analysis requires temporal context such as "today", "now", or "current" in Korea.

🆕 Category Search

• find_category: Category search tool so you no longer need to manually check category numbers in URLs for trend and shopping insight searches. Just describe the category in natural language.

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

1. Category Discovery: Use find_category to explore market segments

2. Trend Analysis: Identify growing vs declining categories

3. Demographic Targeting: Age/gender analysis for customer targeting

4. Competitive Intelligence: Keyword performance comparison

5. Device Strategy: Mobile vs PC shopping optimization

Product Launch Planning

1. Market Validation: Category growth trends and seasonality

2. Target Customers: Demographic analysis for product positioning

3. Marketing Channels: Device preferences for advertising strategy

4. Competitive Landscape: Keyword competition and opportunities

5. Pricing Strategy: Category performance and price correlation

Performance Monitoring

1. Category Health: Monitor product category trends

2. Keyword Tracking: Track brand and product keyword performance

3. Demographic Shifts: Monitor changing customer demographics

4. Seasonal Patterns: Plan inventory and marketing campaigns

5. Competitive Benchmarking: Compare performance against category averages

Quick Reference: Popular Category Codes

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

Installation

Method 1: NPX Installation (Recommended)

The most reliable 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: Smithery Installation (Alternative - Known Issues)

⚠️ Important Notice: Smithery installations can run into connection timeouts and freezes because of issues in the Smithery WebSocket relay infrastructure. This is a known platform limitation rather than a bug in this MCP server. For stable usage, we strongly recommend sticking with Method 1 (NPX installation).

Known issues on Smithery:

• Server initialization may hang or time out
• Error -32001: Request timed out can appear
• WebSocket connections can drop immediately after the handshake
• The server can exit unexpectedly before processing requests

If you still want to try Smithery:

##### 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

If you encounter timeouts on Smithery, switch back to Method 1 (NPX) for a stable experience.

Method 3: 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

1. Download the latest version from GitHub Releases

2. Extract the ZIP file to your desired location

3. 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 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