MCP Logger

Instructions

Implementation Reference

• src/main.py:250-290 (handler)

  Implementation of the get_workouts tool, which queries the workouts table with various filters and hydrates the results with related exercise and set data.

  def get_workouts(
      from_date: Optional[str] = None,
      to_date: Optional[str] = None,
      workout_type: Optional[str] = None,
      tag: Optional[str] = None,
      exercise_name_contains: Optional[str] = None,
      limit: int = 20,
      offset: int = 0,
  ) -> dict[str, list[dict[str, Any]]]:
      """Query workouts with various filters."""
      conn = get_connection()
      cursor = conn.cursor()
  
      filters: list[str] = []
      params: list[Any] = []
  
      if from_date:
          filters.append("date_time >= ?")
          params.append(f"{_ensure_date(from_date)}T00:00:00")
      if to_date:
          filters.append("date_time <= ?")
          params.append(f"{_ensure_date(to_date)}T23:59:59")
      if workout_type:
          filters.append("workout_type = ?")
          params.append(workout_type)
      if tag:
          filters.append("tags LIKE ?")
          params.append(f'%"{tag}"%')
  
      base = "SELECT * FROM workouts"
      if filters:
          base += " WHERE " + " AND ".join(filters)
      base += " ORDER BY date_time DESC LIMIT ? OFFSET ?"
      params.extend([limit, offset])
  
      cursor.execute(base, params)
      rows = cursor.fetchall()
  
      workouts = [_hydrate_workout(conn, _row_to_dict(row)) for row in rows]
      conn.close()
      return {"workouts": workouts}