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jikime

Naver Search MCP Server

search_cafe_article

Search for Naver cafe articles using keywords, with options to navigate pages and sort by relevance or date.

Instructions

Searches for cafe articles 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 async function implementing the search_cafe_article tool logic, which calculates pagination parameters, prepares API params, and delegates the API call and response formatting to the shared _make_api_call helper.
    async def search_cafe_article(query: str, display: int = 10, page: int = 1, sort: str = "sim") -> str:
        """
        Searches for cafe articles 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("cafearticle.json", params, CafeArticleResult, "Cafe Article")
  • server.py:454-457 (registration)
    The @mcp.tool decorator that registers the search_cafe_article tool with MCP, specifying its name and description.
    @mcp.tool(
      name="search_cafe_article",
      description="Searches for cafe articles on Naver using the given keyword. The page parameter allows for page navigation and sort='sim'/'date' is supported."
    )
  • Output schema definition: Pydantic-style model for parsing the Naver Cafe Article search API response, inheriting from SearchResultBase and containing a list of CafeArticleItem objects.
    class CafeArticleResult(SearchResultBase): items: List[CafeArticleItem]
  • Item schema for cafe articles: Extends DescriptionItem (which likely includes title, link, description) with cafe-specific fields cafename and cafeurl.
    class CafeArticleItem(DescriptionItem):
        cafename: Optional[str] = None
        cafeurl: Optional[str] = None
Behavior2/5

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 mentions page navigation and sort options, which adds some context, but fails to cover critical aspects like rate limits, authentication needs, pagination behavior, error handling, or what the output contains. For a search tool with 4 parameters, this leaves significant gaps in understanding how it behaves.

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 appropriately sized with two sentences that are front-loaded: the first states the core purpose, and the second adds parameter context. There's minimal waste, though it could be slightly more structured (e.g., bullet points for parameters). It earns its place by conveying essential information efficiently.

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 the tool's moderate complexity (4 parameters, no annotations, but has an output schema), the description is partially complete. It covers the basic purpose and some parameter semantics, but gaps remain in usage guidelines, behavioral transparency, and full parameter documentation. The output schema existence means return values don't need explanation, but overall it's adequate with clear room for improvement.

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?

The description adds meaning for 'page' (navigation) and 'sort' (supported values 'sim'/'date'), which helps interpret these parameters beyond their schema titles. However, with 0% schema description coverage and 4 parameters total, it doesn't fully compensate: 'query' and 'display' are left unexplained, and it lacks details like format constraints or default behaviors. The baseline is 3 due to partial compensation.

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 cafe articles') and resource ('on Naver'), making the purpose immediately understandable. It distinguishes from siblings by specifying 'cafe articles' rather than other content types like blogs, news, or images. However, it doesn't explicitly contrast with similar tools like search_blog or search_webkr beyond the resource type.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'sort' options but doesn't explain when to choose 'sim' (likely similarity) versus 'date', nor does it compare to sibling tools like search_blog or search_news for different content types. Usage is implied by the keyword search functionality but lacks explicit context or exclusions.

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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