Naver Search MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NAVER_CLIENT_ID | No | Naver Developers client ID (used with NAVER_CLIENT_SECRET) | |
| NCP_APIGW_API_KEY | No | NAVER API HUB client secret (used with NCP_APIGW_API_KEY_ID) | |
| NAVER_CLIENT_SECRET | No | Naver Developers client secret (used with NAVER_CLIENT_ID) | |
| NCP_APIGW_API_KEY_ID | No | NAVER API HUB client ID (used with NCP_APIGW_API_KEY) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_webkrB | ๐ Search Korean web documents and general content. Comprehensive search across Korean websites and online content. Find articles, information, and documents from various Korean sources. (๋ค์ด๋ฒ ์น๋ฌธ์ ๊ฒ์ - ํ๊ตญ ์น์ฌ์ดํธ ์ข ํฉ ๊ฒ์) |
| search_newsA | ๐ฐ Search latest Korean news articles from major outlets. Perfect for current events, breaking news, and recent developments. Covers politics, economy, society, and international news. (๋ค์ด๋ฒ ๋ด์ค ๊ฒ์ - ์ต์ ๋ด์ค์ ์์ฌ ์ ๋ณด) |
| search_blogA | โ๏ธ Search personal blogs and reviews for authentic user experiences. Great for product reviews, personal stories, detailed tutorials, and real user opinions. Find genuine Korean perspectives. (๋ค์ด๋ฒ ๋ธ๋ก๊ทธ ๊ฒ์ - ์ค์ ์ฌ์ฉ์ ํ๊ธฐ์ ๊ฐ์ธ์ ๊ฒฝํ) |
| search_cafearticleB | โ Search Naver Cafe articles for community discussions and specialized content. Find niche communities, hobby groups, and specialized discussions on various topics. (๋ค์ด๋ฒ ์นดํ๊ธ ๊ฒ์ - ์ปค๋ฎค๋ํฐ ํ ๋ก ๊ณผ ์ ๋ฌธ ์ ๋ณด) |
| search_imageB | ๐ผ๏ธ Search for images with Korean context and relevance. Find visual content, infographics, charts, and photos related to your search terms. Great for visual research and content discovery. (๋ค์ด๋ฒ ์ด๋ฏธ์ง ๊ฒ์ - ์๊ฐ์ ์ปจํ ์ธ ๋ฐ๊ฒฌ) |
| search_kinB | โ Search Naver KnowledgeiN for Q&A and community-driven answers. Find solutions to problems, get expert advice, and discover community insights on various topics. (๋ค์ด๋ฒ ์ง์iN ๊ฒ์ - ์ง๋ฌธ๊ณผ ๋ต๋ณ, ์ปค๋ฎค๋ํฐ ์ง์) |
| search_encycA | ๐ Search Naver Encyclopedia for authoritative knowledge and definitions. Best for academic research, getting reliable information, and understanding Korean concepts and terminology. (๋ค์ด๋ฒ ์ง์๋ฐฑ๊ณผ ๊ฒ์ - ์ ๋ขฐํ ์ ์๋ ์ ๋ณด์ ์ ์) |
| search_localB | ๐ Search for local businesses, restaurants, and places in Korea. Find location information, reviews, contact details, and business hours for Korean establishments. (๋ค์ด๋ฒ ์ง์ญ ๊ฒ์ - ์ง์ญ ์ ์ฒด์ ์ฅ์ ์ ๋ณด) |
| datalab_searchB | ๐ Analyze search keyword trends over time using Naver DataLab. Track popularity changes, seasonal patterns, and compare multiple keywords. Perfect for market research and trend analysis. (๋ค์ด๋ฒ ๋ฐ์ดํฐ๋ฉ ๊ฒ์์ด ํธ๋ ๋ ๋ถ์) |
| datalab_shopping_categoryA | ๐๏ธ STEP 2: Analyze shopping category trends over time. Use find_category first to get category codes. BUSINESS CASES: Market size analysis, seasonal trend identification, category performance comparison. EXAMPLE: Compare 'ํจ์ ์๋ฅ' vs 'ํ์ฅํ' trends over 6 months. (๋ค์ด๋ฒ ์ผํ ์นดํ ๊ณ ๋ฆฌ๋ณ ํธ๋ ๋ ๋ถ์ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_by_deviceA | ๐ฑ Analyze shopping trends by device (PC vs Mobile). Use find_category first. BUSINESS CASES: Mobile commerce strategy, responsive design priority, device-specific campaigns. EXAMPLE: 'PC ์ฌ์ฉ์๊ฐ ๋ ๋ง์ด ๊ตฌ๋งคํ๋ ์นดํ ๊ณ ๋ฆฌ๋?' (๊ธฐ๊ธฐ๋ณ ์ผํ ํธ๋ ๋ ๋ถ์ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_by_genderA | ๐ฅ Analyze shopping trends by gender (Male vs Female). Use find_category first. BUSINESS CASES: Gender-targeted marketing, product positioning, demographic analysis. EXAMPLE: 'ํ์ฅํ ์ผํ์์ ๋จ๋ ๋น์จ์?' (์ฑ๋ณ ์ผํ ํธ๋ ๋ ๋ถ์ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_by_ageA | ๐ถ๐ฆ๐จ๐ด Analyze shopping trends by age groups (10s, 20s, 30s, 40s, 50s, 60s+). Use find_category first. BUSINESS CASES: Age-targeted products, generational preferences, lifecycle marketing. EXAMPLE: '๊ฐ๋ฐ ๋๊ตฌ๋ ์ด๋ ์ฐ๋ น๋๊ฐ ๋ง์ด ๊ตฌ๋งคํ๋?' (์ฐ๋ น๋ณ ์ผํ ํธ๋ ๋ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_keywordsA | ๐ Compare specific keywords within a shopping category. Use find_category first. BUSINESS CASES: Product keyword optimization, competitor analysis, search trend identification. EXAMPLE: Within 'ํจ์ ' category, compare '์ํผ์ค' vs '์์ผ' vs '๋๋ ์ค' trends. (์นดํ ๊ณ ๋ฆฌ ๋ด ํค์๋ ๋น๊ต - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_keyword_by_deviceB | ๐ฑ๐ 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. (์ผํ ํค์๋ ๊ธฐ๊ธฐ๋ณ ํธ๋ ๋ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_keyword_by_genderA | ๐ฅ๐ Analyze keyword performance by gender within shopping categories. Use find_category first to get category codes. Essential for gender-targeted marketing and product positioning strategies. (์ผํ ํค์๋ ์ฑ๋ณ ํธ๋ ๋ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| datalab_shopping_keyword_by_ageB | ๐ถ๐ฆ๐จ๐ด๐ Analyze keyword performance by age groups within shopping categories. Use find_category first to get category codes. Perfect for age-targeted marketing and understanding generational shopping preferences. (์ผํ ํค์๋ ์ฐ๋ น๋ณ ํธ๋ ๋ - ๋จผ์ find_category ๋๊ตฌ๋ก ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์ผ์ธ์) |
| find_categoryA | ๐ STEP 1: Find shopping categories with Korean search terms. Search in KOREAN (ํจ์ , ํ์ฅํ, ๊ฐ์ ์ ํ, etc.) to find category codes needed for datalab tools. Smart fuzzy matching finds similar categories even with partial matches. (์นดํ ๊ณ ๋ฆฌ ๊ฒ์: ํ๊ตญ์ด๋ก ๊ฒ์ํ์ฌ ๋ฐ์ดํฐ๋ฉ ๋ถ์์ ํ์ํ ์นดํ ๊ณ ๋ฆฌ ์ฝ๋๋ฅผ ์ฐพ์์ฃผ๋ ํ์ ๋๊ตฌ) |
| brand_compare_trendB | ๐ Compare relative search/shopping-click trends of several book brands on one common scale (anchor keyword rescaling across batches). Values are relative indexes, not counts. Supports dryRun, call budget and xlsx/csv export. (๋์ ๋ธ๋๋ ์ฌ๋ฌ ๊ฐ์ ๊ฒ์์ด ํธ๋ ๋ ๋๋ ์ผํ ํด๋ฆญ ์ถ์ด๋ฅผ ๊ธฐ์ค ๋ธ๋๋๋ก ํ์ฐํด ํ ์ฒ๋๋ก ๋น๊ต. ๊ฐ์ ์๋ ์ง์) |
| brand_mentionsA | ๐ Collect blog/cafe/web posts that mention book brands, count them per month (approximate sample), and list co-mentioned candidate terms with examples. Candidate terms are not verified brands โ judge them yourself. Supports dryRun, call budget and xlsx/csv export. (๋์ ๋ธ๋๋๋ฅผ ์ธ๊ธํ ๋ธ๋ก๊ทธ/์นดํ/์น ๊ธ์ ์์งํด ์๋ณ ์ง๊ณ์ ๋์ ์ธ๊ธ ํ๋ณด ๋จ์ด๋ฅผ ์ ๊ณต) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 20 tools
The search_* tools are clearly separated by content vertical (web, news, blog, cafe, image, Kin, encyclopedia, local). The datalab_shopping_* family has overlapping-looking variants (category vs keyword, by device/gender/age), but descriptions and examples make the intended distinctions mostly clear.
Names are consistently snake_case and grouped by recognizable prefixes: search_*, datalab_*, and brand_*. Minor deviations exist, such as find_category lacking a prefix and inconsistent keyword/keywords usage in datalab_shopping_* names.
Twenty tools is borderline heavy for the server's purpose. The datalab_shopping_* matrix of category/keyword by device/gender/age creates eight granular tools that could arguably be parameterized into fewer operations.
The surface covers many major Naver search verticals and provides broad DataLab trend analysis plus brand trend/mention tools. Some notable Naver verticals such as shopping search, video, and book search are absent, but core search and analytics workflows are covered.