get_qualifying_sessions
Retrieve Formula 1 qualifying session data by splitting Q1, Q2, and Q3 segments with lap details for specific Grand Prix events and seasons.
Instructions
Split qualifying into Q1, Q2, Q3 segments with lap data for each.
Args: year: Season year (2018+) gp: Grand Prix name or round segment: 'Q1', 'Q2', 'Q3', or 'all' (default: 'all' returns all segments)
Returns: dict with Q1/Q2/Q3 keys containing LapsResponse for each segment
Example: get_qualifying_sessions(2024, "Monaco") → All Q1/Q2/Q3 segments get_qualifying_sessions(2024, "Monaco", "Q3") → Q3 only
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | ||
| gp | Yes | ||
| segment | No | all |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- tools/session/qualifying.py:47-112 (handler)The core handler function for the 'get_qualifying_sessions' tool. Fetches qualifying session using FastF1Client, loads laps data, splits into Q1/Q2/Q3 using laps.split_qualifying_sessions(), converts to LapData models via helper, and packages into LapsResponse objects grouped by segment.
def get_qualifying_sessions( year: int, gp: Union[str, int], segment: str = "all" ) -> dict[str, LapsResponse]: """ Split qualifying into Q1, Q2, Q3 segments with lap data for each. Args: year: Season year (2018+) gp: Grand Prix name or round segment: 'Q1', 'Q2', 'Q3', or 'all' (default: 'all' returns all segments) Returns: dict with Q1/Q2/Q3 keys containing LapsResponse for each segment Example: get_qualifying_sessions(2024, "Monaco") → All Q1/Q2/Q3 segments get_qualifying_sessions(2024, "Monaco", "Q3") → Q3 only """ session_obj = fastf1_client.get_session(year, gp, 'Q') session_obj.load(laps=True, telemetry=False, weather=False, messages=False) event = session_obj.event laps = session_obj.laps # Split qualifying into segments q1_laps, q2_laps, q3_laps = laps.split_qualifying_sessions() results = {} # Convert each segment to LapsResponse if segment in ["all", "Q1"] and q1_laps is not None and len(q1_laps) > 0: q1_list = [_row_to_lap_data(row) for idx, row in q1_laps.iterrows()] results["Q1"] = LapsResponse( session_name="Q1", event_name=event['EventName'], driver=None, lap_type="all", laps=q1_list, total_laps=len(q1_list) ) if segment in ["all", "Q2"] and q2_laps is not None and len(q2_laps) > 0: q2_list = [_row_to_lap_data(row) for idx, row in q2_laps.iterrows()] results["Q2"] = LapsResponse( session_name="Q2", event_name=event['EventName'], driver=None, lap_type="all", laps=q2_list, total_laps=len(q2_list) ) if segment in ["all", "Q3"] and q3_laps is not None and len(q3_laps) > 0: q3_list = [_row_to_lap_data(row) for idx, row in q3_laps.iterrows()] results["Q3"] = LapsResponse( session_name="Q3", event_name=event['EventName'], driver=None, lap_type="all", laps=q3_list, total_laps=len(q3_list) ) return results - tools/session/qualifying.py:10-44 (helper)Supporting helper that maps pandas DataFrame row (from FastF1 laps) to the LapData Pydantic model used in the response.
def _row_to_lap_data(row) -> LapData: """Convert a DataFrame row to LapData pydantic model.""" return LapData( time=str(row['Time']) if pd.notna(row.get('Time')) else None, driver=str(row['Driver']) if pd.notna(row.get('Driver')) else "", driver_number=str(row['DriverNumber']) if pd.notna(row.get('DriverNumber')) else "", lap_time=str(row['LapTime']) if pd.notna(row.get('LapTime')) else None, lap_number=int(row['LapNumber']) if pd.notna(row.get('LapNumber')) else 0, stint=int(row['Stint']) if pd.notna(row.get('Stint')) else None, pit_out_time=str(row['PitOutTime']) if pd.notna(row.get('PitOutTime')) else None, pit_in_time=str(row['PitInTime']) if pd.notna(row.get('PitInTime')) else None, sector_1_time=str(row['Sector1Time']) if pd.notna(row.get('Sector1Time')) else None, sector_2_time=str(row['Sector2Time']) if pd.notna(row.get('Sector2Time')) else None, sector_3_time=str(row['Sector3Time']) if pd.notna(row.get('Sector3Time')) else None, sector_1_session_time=str(row['Sector1SessionTime']) if pd.notna(row.get('Sector1SessionTime')) else None, sector_2_session_time=str(row['Sector2SessionTime']) if pd.notna(row.get('Sector2SessionTime')) else None, sector_3_session_time=str(row['Sector3SessionTime']) if pd.notna(row.get('Sector3SessionTime')) else None, speed_i1=float(row['SpeedI1']) if pd.notna(row.get('SpeedI1')) else None, speed_i2=float(row['SpeedI2']) if pd.notna(row.get('SpeedI2')) else None, speed_fl=float(row['SpeedFL']) if pd.notna(row.get('SpeedFL')) else None, speed_st=float(row['SpeedST']) if pd.notna(row.get('SpeedST')) else None, is_personal_best=bool(row['IsPersonalBest']) if pd.notna(row.get('IsPersonalBest')) else None, compound=str(row['Compound']) if pd.notna(row.get('Compound')) else None, tyre_life=float(row['TyreLife']) if pd.notna(row.get('TyreLife')) else None, fresh_tyre=bool(row['FreshTyre']) if pd.notna(row.get('FreshTyre')) else None, team=str(row['Team']) if pd.notna(row.get('Team')) else None, lap_start_time=str(row['LapStartTime']) if pd.notna(row.get('LapStartTime')) else None, lap_start_date=str(row['LapStartDate']) if pd.notna(row.get('LapStartDate')) else None, track_status=str(row['TrackStatus']) if pd.notna(row.get('TrackStatus')) else None, position=float(row['Position']) if pd.notna(row.get('Position')) else None, deleted=bool(row['Deleted']) if pd.notna(row.get('Deleted')) else None, deleted_reason=str(row['DeletedReason']) if pd.notna(row.get('DeletedReason')) else None, fast_f1_generated=bool(row['FastF1Generated']) if pd.notna(row.get('FastF1Generated')) else None, is_accurate=bool(row['IsAccurate']) if pd.notna(row.get('IsAccurate')) else None, ) - server.py:154-154 (registration)MCP tool registration decorator applied to the get_qualifying_sessions handler function.
mcp.tool()(get_qualifying_sessions) - tools/__init__.py:8-8 (registration)Import of get_qualifying_sessions into the main tools namespace, enabling server.py import.
get_qualifying_sessions, - tools/session/__init__.py:6-6 (registration)Import of the handler from its implementation file into the session tools submodule.
from .qualifying import get_qualifying_sessions