JVLink MCP Server

Instructions

Implementation Reference

• src/jvlink_mcp_server/server.py:485-501 (handler)

  The tool 'jockey_stats' is registered here, which calls '_get_jockey_stats' from high_level_api.py.

  @mcp.tool(name="jockey_stats")
  def analyze_jockey_stats(
      jockey_name: str,
      venue: Optional[str] = None,
      year_from: Optional[str] = None,
      distance: Optional[int] = None
  ) -> dict:
      """騎手の成績を分析
  
      騎手名を指定して、勝率・複勝率・騎乗数などを調べられます。
      競馬場や距離でのフィルタリングも可能です。
      """
      with DatabaseConnection() as db:
          return _get_jockey_stats(
              db, jockey_name=jockey_name, venue=venue,
              year_from=year_from, distance=distance
          )

• src/jvlink_mcp_server/database/high_level_api.py:206-294 (handler)

  The internal implementation '_jockey_stats_impl' executes the SQL query for jockey statistics.

  def _jockey_stats_impl(
      db_connection,
      jockey_name: str,
      venue: Optional[str] = None,
      year_from: Optional[str] = None,
      distance: Optional[int] = None,
      source: str = 'jra'
  ) -> Dict[str, Any]:
      """騎手成績の共通実装（JRA/NAR兼用）"""
      tables = _SOURCE_TABLES[source]
      conditions = []
      query_params: List = []
      condition_desc = [f"騎手名: {jockey_name}（部分一致）"]
      if source == 'nar':
          condition_desc.append("NAR地方競馬")
  
      conditions.append("s.KisyuRyakusyo LIKE ?")
      query_params.append('%' + jockey_name + '%')
      conditions.append("s.KakuteiJyuni IS NOT NULL")
      conditions.append("s.KakuteiJyuni > 0")
  
      if venue:
          venue_code = _resolve_venue(venue, source)
          conditions.append("s.JyoCD = ?")
          query_params.append(venue_code)
          condition_desc.append(f"{venue}競馬場")
  
      if year_from:
          year_val = _validate_year(year_from)
          conditions.append("s.Year >= ?")
          query_params.append(year_val)
          condition_desc.append(f"{year_val}年以降")
  
      if distance:
          conditions.append("r.Kyori = ?")
          query_params.append(distance)
          condition_desc.append(f"{distance}m")
  
      where_clause = " AND ".join(conditions)
  
      if distance:
          query = f"""
          SELECT s.KisyuRyakusyo as jockey_name, COUNT(*) as total_rides,
              SUM(CASE WHEN s.KakuteiJyuni = 1 THEN 1 ELSE 0 END) as wins,
              SUM(CASE WHEN s.KakuteiJyuni IN (1, 2) THEN 1 ELSE 0 END) as places_2,
              SUM(CASE WHEN s.KakuteiJyuni IN (1, 2, 3) THEN 1 ELSE 0 END) as places_3
          FROM {tables['se']} s
          JOIN {tables['ra']} r
              ON s.Year = r.Year AND s.MonthDay = r.MonthDay AND s.JyoCD = r.JyoCD
              AND s.Kaiji = r.Kaiji AND s.Nichiji = r.Nichiji AND s.RaceNum = r.RaceNum
          WHERE {where_clause}
          GROUP BY s.KisyuRyakusyo
          """
      else:
          query = f"""
          SELECT s.KisyuRyakusyo as jockey_name, COUNT(*) as total_rides,
              SUM(CASE WHEN s.KakuteiJyuni = 1 THEN 1 ELSE 0 END) as wins,
              SUM(CASE WHEN s.KakuteiJyuni IN (1, 2) THEN 1 ELSE 0 END) as places_2,
              SUM(CASE WHEN s.KakuteiJyuni IN (1, 2, 3) THEN 1 ELSE 0 END) as places_3
          FROM {tables['se']} s
          WHERE {where_clause}
          GROUP BY s.KisyuRyakusyo
          """
  
      df = db_connection.execute_safe_query(query, params=tuple(query_params))
  
      if df.empty:
          return {
              'jockey_name': jockey_name, 'total_rides': 0, 'wins': 0,
              'places_2': 0, 'places_3': 0, 'win_rate': 0.0,
              'place_rate_2': 0.0, 'place_rate_3': 0.0,
              'conditions': ', '.join(condition_desc), 'query': query
          }
  
      total_rides = int(df['total_rides'].sum())
      wins = int(df['wins'].sum())
      places_2 = int(df['places_2'].sum())
      places_3 = int(df['places_3'].sum())
      matched_jockey = df.loc[df['total_rides'].idxmax(), 'jockey_name']
  
      return {
          'jockey_name': matched_jockey, 'total_rides': total_rides,
          'wins': wins, 'places_2': places_2, 'places_3': places_3,
          'win_rate': (wins / total_rides * 100) if total_rides > 0 else 0.0,
          'place_rate_2': (places_2 / total_rides * 100) if total_rides > 0 else 0.0,
          'place_rate_3': (places_3 / total_rides * 100) if total_rides > 0 else 0.0,
          'conditions': ', '.join(condition_desc),
          'matched_jockeys': df['jockey_name'].tolist(), 'query': query
      }