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jikime

Naver Search MCP Server

search_news

Search for news articles on Naver using keywords, with options to sort by relevance or date and navigate through result pages.

Instructions

Searches for news on Naver using the given keyword. The page parameter allows for page navigation and sort='sim'/'date' is supported.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
displayNo
pageNo
sortNosim

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function implementing the 'search_news' tool. It calculates pagination parameters, prepares API params, and delegates to the shared _make_api_call helper to fetch and format Naver news search results.
    async def search_news(query: str, display: int = 10, page: int = 1, sort: str = "sim") -> str:
        """
        Searches for news on Naver using the given keyword. The page parameter allows for page navigation and sort='sim'/'date' is supported.
    
        Args:
            query (str): The keyword to search for
            display (int, optional): The number of results to display. Default is 10.
            page (int, optional): The starting page number. Default is 1.
            sort (str, optional): The sorting criteria. Default is "sim" (similarity).
        """
        start = calculate_start(page, display)
        display = min(display, 100)
        params = {"query": query, "display": display, "start": start, "sort": sort}
        return await _make_api_call("news.json", params, NewsResult, "News")
  • server.py:379-382 (registration)
    The @mcp.tool decorator that registers the 'search_news' tool with the FastMCP server, including name and description.
    @mcp.tool(
      name="search_news",
      description="Searches for news on Naver using the given keyword. The page parameter allows for page navigation and sort='sim'/'date' is supported."
    )
  • Pydantic model defining the structure for Naver news search API response, used for validation and parsing in the handler.
    class NewsResult(SearchResultBase): items: List[NewsItem]
  • Pydantic model for individual news search result items, extending DescriptionItem with news-specific fields like original link and publication date.
    class NewsItem(DescriptionItem):
        originallink: Optional[str] = None
        pubDate: Optional[str] = None
  • Helper function to compute the 'start' parameter for paginated Naver API calls, capping at 1000.
    def calculate_start(page: int, display: int) -> int:
        """Calculates the start value for the API call based on the page number and display count."""
        if page < 1:
            page = 1
        start = (page - 1) * display + 1
        # 네이버 API의 start 최대값(1000) 제한 고려
        return min(start, 1000)
Behavior2/5

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

With no annotations provided, the description carries full burden but lacks critical behavioral details. It doesn't disclose rate limits, authentication needs, error handling, or response format (though an output schema exists). The mention of sort and page parameters adds some context but is insufficient for a mutation-like search operation.

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 brief and front-loaded with the core purpose. Both sentences add value: the first defines the tool, and the second clarifies parameter usage. No wasted words, though it could be more structured.

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

Completeness3/5

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

Given 4 parameters with 0% schema coverage and no annotations, the description is incomplete—it misses details on 'query' and 'display'. However, an output schema exists, reducing the need to explain return values. The tool's moderate complexity warrants more guidance on usage and behavior.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It explains 'page' for navigation and 'sort' with values 'sim'/'date', covering 2 of 4 parameters. However, 'query' and 'display' are not explained, leaving gaps. The baseline is adjusted upward due to partial coverage.

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

Purpose4/5

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

The description clearly states the action ('Searches for news') and resource ('on Naver'), specifying the platform and content type. It distinguishes from siblings like search_blog or search_image by focusing on news, though it doesn't explicitly contrast with them.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like search_webkr or search_blog. The description mentions sort options but doesn't explain when to choose 'sim' versus 'date' or when pagination is needed, leaving usage context implicit.

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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