Kakao Map MCP Server
Provides location-based place recommendations in South Korea using Kakao Map's keyword search API, enabling queries for places like restaurants, shops, public facilities, and tourist attractions.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Kakao Map MCP Server서울 강남역 근처 카페 추천해줘"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Kakao Map MCP Server
한국어 | English
카카오맵 API를 활용하여 대한민국 내 위치 기반 장소 추천을 제공하는 MCP 서버입니다. 한국어 쿼리에 최적화되어 있습니다.

Tool: kakao_map_place_recommender
설명: 사용자 질의에 따라 대한민국 내 다양한 관련 장소(예: 식당, 상점, 공공시설, 관광명소)를 추천합니다. 카카오맵 API 키워드 검색을 사용합니다.
query(필수): 장소 유형 및 위치를 설명하는 한국어 키워드. 예시: '이태원 맛집', '서울 병원', '강남역 근처 카페'.
Related MCP server: MCP Kakao Local
Configuration
환경 변수
KAKAO_API_KEY: 카카오 API 키 (필수)
REST API 키 확인: 애플리케이션 설정(
[내 애플리케이션] > [앱 설정] > [요약 정보])으로 이동합니다. 제공된 여러 키 중에서 REST API 키를 찾아 복사합니다. 이 도구에는 이 특정 키가 필요합니다.카카오맵 API 활성화: 애플리케이션에 카카오맵 API가 활성화되어 있는지 확인합니다.
[내 애플리케이션] > [카카오맵] > [활성화 설정]으로 이동하여[상태]를ON으로 설정합니다. (참고: 기존 앱에 API를 추가하는 경우, 추가적인 권한 신청 및 승인이 필요할 수 있습니다.)참고: 자세한 내용은 공식 문서를 참조하세요: 카카오 로컬 API 공통 가이드.
Available Tools
1 toolkakao_map_place_recommenderA
Recommends relevant places in South Korea, such as restaurants, cafes, parks, hospitals, banks, shops, or tourist attractions, based on user queries seeking suggestions.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Korean keywords for searching places in South Korea. Typically combines place type and location (e.g., '이태원 맛집', '서울 병원', '강남역 영화관'). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It accurately indicates that the tool performs a read-only recommendation operation, but it does not mention return format, potential limitations, or any underlying data source. This is acceptable for a simple recommender but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core purpose (recommends places in South Korea) and then expands with concrete examples. It contains no fluff or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity—one parameter and no output schema—the description sufficiently explains what it does and what kind of input it expects. A minor gap is that it doesn't hint at the response structure, but this is not a major issue for a straightforward recommendation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with a detailed description of the 'query' parameter, including specific examples and guidance on combining place type and location. The tool description adds no additional parameter semantics, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Recommends' and clearly identifies the resource as 'places in South Korea', with a diverse list of place types (restaurants, cafes, parks, etc.). This makes the tool's function unambiguous and distinguishes it from other potential tools even without sibling context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies usage when users seek place recommendations in South Korea. Although no explicit 'when not to use' or alternatives are listed, there are no sibling tools to differentiate from, so the implied context is sufficient for an agent to decide when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion between tools. The single tool has a clear, distinct purpose.
The tool name follows a consistent snake_case pattern and is descriptive. With only one tool, there are no inconsistencies to evaluate.
A single tool for a server named 'Kakao Map' is too few for the apparent scope. Map services typically include search, geocoding, directions, and other features, so one recommendation tool feels inadequate.
The server only offers place recommendation, missing essential map operations like searching by keyword, getting details, geocoding, or route planning. This is severely incomplete for a map-focused server.
Maintenance
Resources
Unclaimed servers have limited discoverability.
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