MLIT Data Platform MCP Server

Instructions

            使用ケース:
            1. ユーザー入力('東京', 'Tokyo', '13')を正規化 → '13' + '東京都'
            2. 市区町村名から5桁コードを取得
            3. 曖昧な入力の候補一覧取得

            このツールを他の検索ツールの前に実行することで、正確なprefecture_code/municipality_codeを取得できます

Implementation Reference

• src/client.py:1204-1304 (handler)

  The `normalize_codes` method in `MLITClient` class handles the logic for normalizing prefecture and municipality names/codes into standardized identifiers. It uses internal caches for prefectures and municipalities and provides several strategies for matching.

  async def normalize_codes(self, params: NormalizeCodesInput) -> NormalizeCodesOutput:
      out = NormalizeCodesOutput(
          normalization_meta={
              "input_prefecture": params.prefecture,
              "input_municipality": params.municipality,
              "matched_strategy": None,
          }
      )
      pref_in = (params.prefecture or "").strip()
      pref_rows = await self._load_pref_list()
  
      pref_code: Optional[str] = None
      pref_name: Optional[str] = None
  
      if pref_in:
          pcanon = self._canon(pref_in)
          if pcanon.isdigit():
              for r in pref_rows:
                  code = str(r["code"])
                  if code == pcanon or code.lstrip("0") == pcanon.lstrip("0"):
                      pref_code, pref_name = code, r["name"]
                      out.normalization_meta["matched_strategy"] = "pref:code"
                      break
          if pref_code is None:
              for r in pref_rows:
                  nm = r["name"]
                  nm_core = self._strip_pref_suffix(nm)
                  in_core = self._strip_pref_suffix(self._nfkc(pref_in))
                  if nm == pref_in or nm_core == in_core:
                      pref_code, pref_name = str(r["code"]), nm
                      out.normalization_meta["matched_strategy"] = "pref:jp_exact"
                      break
          if pref_code is None and pcanon.isascii():
              alias = self._pref_romaji_map().get(pcanon.replace(" ", "").replace("-", ""))
              if alias:
                  for r in pref_rows:
                      if r["name"] == alias:
                          pref_code, pref_name = str(r["code"]), r["name"]
                          out.normalization_meta["matched_strategy"] = "pref:romaji"
                          break
          if pref_code is None:
              for r in pref_rows:
                  nm = r["name"]
                  if self._strip_pref_suffix(nm) in self._nfkc(pref_in):
                      pref_code, pref_name = str(r["code"]), nm
                      out.normalization_meta["matched_strategy"] = "pref:jp_contains"
                      break
  
      muni_in = (params.municipality or "").strip()
      muni_code: Optional[str] = None
      muni_name: Optional[str] = None
      candidates: List[MunicipalityCandidate] = []
  
      if muni_in:
          mcanon = self._canon(muni_in)
          if mcanon.isdigit() and len(mcanon) >= 5:
              if pref_code:
                  mlist = await self._load_muni_list(pref_code)
                  for r in mlist:
                      if str(r["code_as_string"]) == mcanon:
                          muni_code, muni_name = r["code_as_string"], r["name"]
                          out.normalization_meta["matched_strategy"] = "muni:code_in_pref"
                          break
              if muni_code is None and not pref_code:
                  out.warnings.append("municipality_code_provided_but_prefecture_unknown")
          else:
              if pref_code:
                  mlist = await self._load_muni_list(pref_code)
                  for r in mlist:
                      if r["name"] == self._nfkc(muni_in):
                          muni_code, muni_name = r["code_as_string"], r["name"]
                          out.normalization_meta["matched_strategy"] = "muni:jp_exact"
                          break
                  if muni_code is None:
                      for r in mlist:
                          if self._nfkc(muni_in) in r["name"]:
                              candidates.append(MunicipalityCandidate(
                                  municipality_code=str(r["code_as_string"]),
                                  municipality_name=str(r["name"])
                              ))
                      if len(candidates) == 1:
                          muni_code, muni_name = candidates[0].municipality_code, candidates[0].municipality_name
                          candidates = []
                          out.normalization_meta["matched_strategy"] = "muni:jp_contains_unique"
                      elif len(candidates) > 1:
                          out.normalization_meta["matched_strategy"] = "muni:jp_contains_ambiguous"
                          out.warnings.append("ambiguous_municipality: multiple candidates")
              else:
                  out.warnings.append("municipality_without_prefecture: provide prefecture for disambiguation")
  
      out.prefecture_code = pref_code
      out.prefecture_name = pref_name
      out.municipality_code = muni_code
      out.municipality_name = muni_name
      out.candidates = candidates
  
      if pref_code and not muni_code and not muni_in:
          out.warnings.append("attribute_only_hint: some MLIT attribute-only queries require term:\"\" or an additional attribute (e.g., address)")
  
      return out

• src/schemas.py:291-302 (schema)

  Input and output schemas for `normalize_codes` tool.

  class NormalizeCodesInput(BaseModel):
      prefecture: Optional[str] = None
      municipality: Optional[str] = None
  
  
  class MunicipalityCandidate(BaseModel):
      municipality_code: str
      municipality_name: str
  
  
  class NormalizeCodesOutput(BaseModel):
      prefecture_code: Optional[str] = None