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yunkee-lee

MCP Naver Maps

by yunkee-lee

geocode

Convert addresses into geographic coordinates using Naver Maps data for Korean locations, supporting both Korean and English language responses.

Instructions

Searches for address information related to the entered address.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesaddress to search for
languageNolanguage used in responsekor

Implementation Reference

  • The MCP tool handler for 'geocode'. Decorated with @mcp.tool for registration. Defines input schema using Pydantic Field annotations on parameters. Implements tool logic by delegating to NaverMapsClient.geocode and handling exceptions with error dict.
    @mcp.tool(description="Searches for address information related to the entered address.")
    async def geocode(
      address: str = Field(description="address to search for", min_length=1),
      language: Literal["kor", "eng"] = Field("kor", description="language used in response"),
    ) -> GeocodeResponse | Dict:
      """
      Returns:
        GeocodeResponse: An object containing metadata and a list of matching addresses
      """
      try:
        return await naver_maps_client.geocode(address, language)
      except Exception as ex:
        return {"success": False, "error": str(ex)}
  • Pydantic model defining the output schema for the geocode tool response, including metadata, list of addresses, status, and error handling.
    class GeocodeResponse(BaseModel):
      status: str = Field(description="Status of the request")
      meta: Meta = Field(description="Metadata of the response")
      addresses: List[Address] = Field(description="List of matching addresses")
      errorMessage: str = Field(description="Error message (only present for HTTP 500 errors)")
  • Helper function in NaverMapsClient class that executes the actual Naver Maps Geocode API call, including parameter construction, HTTP request via _get, and response parsing into GeocodeResponse model.
    async def geocode(
      self,
      query: str,
      language: str,
      page: int = 1,
      count: int = 10,
    ) -> GeocodeResponse:
      """
      https://api.ncloud-docs.com/docs/application-maps-geocoding
      """
      path = f"{self.MAP_BASE_URL}/map-geocode/v2/geocode"
      params = {
        "query": query,
        "language": language,
        "page": page,
        "count": count,
      }
      response_json = await self._get(path, self.naver_maps_headers, params)
      return GeocodeResponse(**response_json)
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. While 'searches' implies a read-only operation, it doesn't specify details like rate limits, authentication needs, error handling, or what 'address information' includes (e.g., coordinates, formatted address). This is a significant gap for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy to parse quickly. 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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output format. Without an output schema, the description should ideally hint at what 'address information' includes, but it doesn't, leaving gaps in completeness.

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 input schema has 100% description coverage, with clear documentation for both parameters (address and language). The description adds no additional meaning beyond the schema, such as examples or usage notes. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

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: 'Searches for address information related to the entered address.' This specifies the verb ('searches for') and resource ('address information'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from the sibling tool 'localSearch', which might have overlapping functionality.

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 the sibling tool 'localSearch' or any other context for usage, such as prerequisites or exclusions. This leaves the agent with no explicit direction on tool selection.

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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