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jikime

Naver Search MCP Server

check_adult_query

Identify adult content in search queries to filter inappropriate results and maintain safe search environments.

Instructions

Determines if the input query is an adult search term.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'check_adult_query' tool. It takes a query string, calls the shared _make_api_call helper with the Naver adult check endpoint, and returns a formatted string result.
    async def check_adult_query(query: str) -> str:
        """
        Determines if the input query is an adult search term.
    
        Args:
            query (str): The keyword to search for
        """
        params = {"query": query}
        return await _make_api_call("adult.json", params, AdultResult, "Adult Search Term")
  • Pydantic BaseModel defining the output schema for the adult query check API response, with a single 'adult' field (likely 0 or 1 indicating if it's adult content).
    class AdultResult(BaseModel): adult: str
  • server.py:419-422 (registration)
    MCP decorator that registers the 'check_adult_query' tool, specifying its name and description. The input schema is inferred from the function signature (query: str).
    @mcp.tool(
      name="check_adult_query",
      description="Determines if the input query is an adult search term."
    )
  • Specific handling logic within the shared _make_api_call helper function for formatting AdultResult responses into Korean text ("일반 검색어" or "성인 검색어"). This is called by the handler.
    elif isinstance(result, AdultResult):
        prompt_string = f"네이버 {search_type_name} 확인 결과:"
        if result.adult == 0:
            return f"{prompt_string} 일반 검색어"
        else:
            return f"{prompt_string} 성인 검색어"
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on how it works: e.g., what criteria define 'adult,' whether it's a classification or binary check, error handling, or performance traits. This leaves significant gaps for an agent to understand its behavior.

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

Conciseness5/5

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

The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose, making it easy to parse. Every word earns its place by directly contributing to understanding the tool's function.

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 (classification task), no annotations, and an output schema (which likely handles return values), the description is minimally adequate. It states the purpose but lacks usage context, behavioral details, or parameter nuances. It meets the baseline for a simple tool but doesn't fully address potential agent confusion.

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

Parameters4/5

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

The description adds meaning beyond the input schema, which has 0% description coverage. It clarifies that the 'query' parameter is an 'input query' to be evaluated as an 'adult search term,' providing context not in the schema. With only one parameter, this is sufficient to compensate for the low schema coverage, though it doesn't detail format or constraints.

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 tool's purpose: 'Determines if the input query is an adult search term.' It specifies a verb ('determines') and resource ('adult search term'), making the intent unambiguous. However, it doesn't differentiate from sibling tools (e.g., search_webkr, search_image), which might also handle queries, so it's not a perfect 5.

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 doesn't mention sibling tools or contexts where this tool is preferred (e.g., for content filtering vs. general search). Without such information, agents may struggle to select it appropriately among the many search-related siblings.

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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