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jikime

Naver Search MCP Server

correct_errata

Fix Korean and English keyboard input errors in text queries to improve search accuracy on Naver platforms.

Instructions

Converts Korean/English keyboard input errors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool for the 'correct_errata' tool. It accepts a query string and delegates to the _make_api_call helper to invoke the Naver errata.json API and process the response.
    @mcp.tool(
      name="correct_errata",
      description="Converts Korean/English keyboard input errors."
    )
    async def correct_errata(query: str) -> str:
        """
        Converts Korean/English keyboard input errors.
    
        Args:
            query (str): The keyword to search for
        """
        params = {"query": query}
        return await _make_api_call("errata.json", params, ErrataResult, "Errata Conversion")
  • Pydantic model defining the schema for the Naver errata API response, with a single 'errata' field containing the corrected string or empty if no correction.
    class ErrataResult(BaseModel): errata: str
  • Specific processing logic for ErrataResult within the shared _make_api_call function, which formats the output message based on whether an errata correction was found.
    elif isinstance(result, ErrataResult):
        print(f"ErrataResult: {result}")
        prompt_string = f"네이버 {search_type_name} 확인 결과:"
        if result.errata == "":
            return f"{prompt_string} 오타 없음"
        else:
            return f"{prompt_string} {result.errata}"
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 the tool 'converts' errors, implying a transformation or correction action, but doesn't specify whether this is a read-only operation, if it requires authentication, rate limits, or what the output entails. The description lacks details on behavioral traits beyond the basic purpose.

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 extremely concise and front-loaded: 'Converts Korean/English keyboard input errors.' It's a single sentence that directly states the purpose without any waste, making it efficient and easy to parse. Every word earns its place.

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 complexity (a conversion tool with 1 parameter) and the presence of an output schema (which should cover return values), the description is minimally complete. It states what the tool does but lacks details on usage, parameters, and behavior. With no annotations and low schema coverage, it's adequate but has clear gaps, meeting the baseline for a simple tool.

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

Parameters2/5

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

The input schema has 1 parameter ('query') with 0% description coverage, meaning the schema provides no details about this parameter. The description does not add any meaning beyond the schema; it doesn't explain what 'query' should contain (e.g., text with keyboard errors) or its format. With low schema coverage, the description fails to compensate, leaving the parameter poorly defined.

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: 'Converts Korean/English keyboard input errors.' It specifies the action ('converts') and the resource/scope ('Korean/English keyboard input errors'), making the function understandable. However, it doesn't differentiate from sibling tools, which are all search-related (e.g., search_blog, search_news), 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 any prerequisites, exclusions, or specific contexts for usage, such as when input errors are suspected or how it compares to other tools. This leaves the agent with minimal direction.

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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