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harimkang

Korea Tourism API MCP Server

find_nearby_attractions

Search for tourism attractions, restaurants, accommodations, and facilities near specific GPS coordinates in Korea using location-based filtering with customizable radius and content types.

Instructions

Find tourism attractions near a specific location in Korea.

This tool performs a location-based search to find tourism items within a specified radius of given GPS coordinates. It's useful for finding nearby attractions, restaurants, or other tourism facilities when you know a specific location.

Args: longitude (float): Longitude coordinate (e.g., 126.9780 for Seoul) latitude (float): Latitude coordinate (e.g., 37.5665 for Seoul) radius (int, optional): Search radius in meters (default: 1000, max: 20000) content_type (str, optional): Type of content to filter. Valid values: - "Tourist Attraction" (default) - "Cultural Facility" - "Festival Event" - "Leisure Activity" - "Accommodation" - "Shopping" - "Restaurant" - "Transportation" language (str, optional): Language for results (default: "en"). Supported: - "en" (English) - "jp" (Japanese) - "zh-cn" (Simplified Chinese) - "zh-tw" (Traditional Chinese) - "de" (German) - "fr" (French) - "es" (Spanish) - "ru" (Russian) page (int, optional): Page number for pagination (default: 1, min: 1) rows (int, optional): Number of items per page (default: 20, max: 100) filter (list[str], optional): List of keys to include in each result item (whitelist). - If filter is None or an empty list ([]), all fields are returned. - If filter contains values, only the specified keys will be included in each item, and all other keys will be removed.

Returns: dict: Nearby tourism attractions with structure: { "total_count": int, # Total number of matching items "num_of_rows": int, # Number of items per page "page_no": int, # Current page number "search_radius": int, # Search radius used "items": [ # List of tourism items { "title": str, # Name of the attraction/place "addr1": str, # Primary address "addr2": str, # Secondary address "areacode": str, # Area code "sigungucode": str, # Sigungu code "cat1": str, # Category 1 code "cat2": str, # Category 2 code "cat3": str, # Category 3 code "contentid": str, # Unique content ID "contenttypeid": str, # Content type ID "createdtime": str, # Creation timestamp "modifiedtime": str, # Last modified timestamp "tel": str, # Phone number "firstimage": str, # URL of main image "firstimage2": str, # URL of thumbnail image "mapx": str, # Longitude "mapy": str, # Latitude "mlevel": str, # Map level "dist": str # Distance from the specified coordinates } # ... more items ] }

Example: find_nearby_attractions(126.9780, 37.5665, 1000, "Tourist Attraction", "en", 1, 10)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
longitudeYes
latitudeYes
radiusNo
content_typeNo
languageNo
pageNo
rowsNo
filterNo

Implementation Reference

  • The 'find_nearby_attractions' tool implementation, which validates input parameters and calls the API client to fetch location-based tourism data.
    async def find_nearby_attractions(
        longitude: float,
        latitude: float,
        radius: int = 1000,
        content_type: str | None = None,
        language: str | None = None,
        page: int = 1,
        rows: int = 20,
        filter: list[str] | None = None,
    ) -> dict:
        """
        Find tourism attractions near a specific location in Korea.
    
        This tool performs a location-based search to find tourism items within a specified
        radius of given GPS coordinates. It's useful for finding nearby attractions,
        restaurants, or other tourism facilities when you know a specific location.
    
        Args:
            longitude (float): Longitude coordinate (e.g., 126.9780 for Seoul)
            latitude (float): Latitude coordinate (e.g., 37.5665 for Seoul)
            radius (int, optional): Search radius in meters (default: 1000, max: 20000)
            content_type (str, optional): Type of content to filter. Valid values:
                - "Tourist Attraction" (default)
                - "Cultural Facility"
                - "Festival Event"
                - "Leisure Activity"
                - "Accommodation"
                - "Shopping"
                - "Restaurant"
                - "Transportation"
            language (str, optional): Language for results (default: "en"). Supported:
                - "en" (English)
                - "jp" (Japanese)
                - "zh-cn" (Simplified Chinese)
                - "zh-tw" (Traditional Chinese)
                - "de" (German)
                - "fr" (French)
                - "es" (Spanish)
                - "ru" (Russian)
            page (int, optional): Page number for pagination (default: 1, min: 1)
            rows (int, optional): Number of items per page (default: 20, max: 100)
            filter (list[str], optional): List of keys to include in each result item (whitelist).
                - If filter is None or an empty list ([]), all fields are returned.
                - If filter contains values, only the specified keys will be included in each item, and all other keys will be removed.
    
        Returns:
            dict: Nearby tourism attractions with structure:
            {
                "total_count": int,     # Total number of matching items
                "num_of_rows": int,     # Number of items per page
                "page_no": int,         # Current page number
                "search_radius": int,   # Search radius used
                "items": [              # List of tourism items
                    {
                        "title": str,           # Name of the attraction/place
                        "addr1": str,           # Primary address
                        "addr2": str,           # Secondary address
                        "areacode": str,        # Area code
                        "sigungucode": str,     # Sigungu code
                        "cat1": str,            # Category 1 code
                        "cat2": str,            # Category 2 code
                        "cat3": str,            # Category 3 code
                        "contentid": str,       # Unique content ID
                        "contenttypeid": str,   # Content type ID
                        "createdtime": str,     # Creation timestamp
                        "modifiedtime": str,    # Last modified timestamp
                        "tel": str,             # Phone number
                        "firstimage": str,      # URL of main image
                        "firstimage2": str,     # URL of thumbnail image
                        "mapx": str,            # Longitude
                        "mapy": str,            # Latitude
                        "mlevel": str,          # Map level
                        "dist": str             # Distance from the specified coordinates
                    }
                    # ... more items
                ]
            }
    
        Example:
            find_nearby_attractions(126.9780, 37.5665, 1000, "Tourist Attraction", "en", 1, 10)
        """
        # Validate and convert content_type
        content_type_id = None
        if content_type:
            content_type_id = next(
                (
                    k
                    for k, v in CONTENTTYPE_ID_MAP.items()
                    if v.lower() == content_type.lower()
                ),
                None,
            )
            if content_type_id is None:
                valid_types = ", ".join(CONTENTTYPE_ID_MAP.values())
                raise ValueError(
                    f"Invalid content_type: '{content_type}'. Valid types are: {valid_types}"
                )
    
        # Call the API client and return dict directly
        results = await get_api_client().get_location_based_list(
            mapx=longitude,
            mapy=latitude,
            radius=radius,
            content_type_id=content_type_id,
            language=language,
            page=page,
            rows=rows,
        )
        # Apply filter if provided
        if filter:
            filter_items = []
            for item in results.get("items", []):
                item = {k: v for k, v in item.items() if k in filter}
                filter_items.append(item)
            results["items"] = filter_items
        # Add search radius to the results
        return {**results, "search_radius": radius}
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: location-based search within a radius, pagination support, filtering capabilities, and the structure of returned data. It covers key aspects like default values, maximum limits, and the effect of the filter parameter. However, it doesn't mention rate limits, authentication requirements, or error conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, args, returns, example) and uses bullet points for readability. While comprehensive, it's appropriately sized for an 8-parameter tool with complex options. Some redundancy exists (e.g., repeating 'optional' in parameter descriptions when the schema already indicates optionality), but overall it's efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, no annotations, no output schema), the description provides complete context. It explains the tool's purpose, documents all parameters thoroughly, describes the return structure in detail, and includes a practical example. The only minor gap is lack of error handling or rate limit information, but for a search tool, this level of documentation is comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. It explains each parameter's purpose, provides examples (e.g., coordinates for Seoul), lists valid values for enums (content_type, language), specifies defaults, ranges, and constraints (max radius, min page), and clarifies the filter parameter's behavior with None vs empty list vs specific values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Find tourism attractions near a specific location in Korea' with specific verb ('find'), resource ('tourism attractions'), and geographic scope ('Korea'). It distinguishes from siblings by focusing on location-based search rather than keyword-based (search_tourism_by_keyword), area-based (get_tourism_by_area), or other specialized searches (find_accommodations, search_festivals_by_date).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: 'when you know a specific location' for finding nearby attractions, restaurants, or tourism facilities. It doesn't explicitly state when NOT to use it or name specific alternatives, but the context implies this is for location-based searches rather than keyword or date-based searches offered by sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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