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get_meeting_info

Retrieve Formula 1 race weekend schedules with session times, circuit details, and session keys from OpenF1 data for specified seasons and countries.

Instructions

Get meeting and session schedule information from OpenF1.

Provides precise start times, session keys, and circuit details.

Args: year: Season year (2023+, OpenF1 data availability) country: Country name (e.g., "Monaco", "Italy", "United States")

Returns: MeetingResponse with meeting info and all sessions

Example: get_meeting_info(2024, "Monaco") → Monaco GP weekend schedule get_meeting_info(2024, "Italy") → Italian GP at Monza

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
countryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
meetingNoMeeting information
sessionsYesList of sessions in meeting
total_sessionsYesTotal number of sessions

Implementation Reference

  • Main tool handler: Fetches meeting info and sessions using OpenF1 API, converts to Pydantic models.
    def get_meeting_info(
        year: int,
        country: str
    ) -> MeetingResponse:
        """
        Get meeting and session schedule information from OpenF1.
    
        Provides precise start times, session keys, and circuit details.
    
        Args:
            year: Season year (2023+, OpenF1 data availability)
            country: Country name (e.g., "Monaco", "Italy", "United States")
    
        Returns:
            MeetingResponse with meeting info and all sessions
    
        Example:
            get_meeting_info(2024, "Monaco") → Monaco GP weekend schedule
            get_meeting_info(2024, "Italy") → Italian GP at Monza
        """
        # Get meeting info
        meetings = openf1_client.get_meetings(year=year, country_name=country)
        if not meetings:
            return MeetingResponse(
                meeting=None,
                sessions=[],
                total_sessions=0
            )
    
        meeting_data = meetings[0]
    
        # Convert to Pydantic model
        meeting = MeetingInfo(
            meeting_key=meeting_data['meeting_key'],
            meeting_name=meeting_data['meeting_name'],
            meeting_official_name=meeting_data['meeting_official_name'],
            location=meeting_data['location'],
            country_name=meeting_data['country_name'],
            circuit_key=meeting_data['circuit_key'],
            circuit_short_name=meeting_data['circuit_short_name'],
            date_start=meeting_data['date_start'],
            year=meeting_data['year']
        )
    
        # Get sessions for this meeting
        sessions_data = openf1_client.get_sessions(year=year, country_name=country)
    
        # Convert sessions to Pydantic models
        sessions = [
            SessionInfo(
                session_key=session['session_key'],
                session_name=session['session_name'],
                session_type=session['session_type'],
                date_start=session['date_start'],
                date_end=session['date_end'],
                gmt_offset=session['gmt_offset'],
                circuit_key=session['circuit_key'],
                circuit_short_name=session['circuit_short_name']
            )
            for session in sessions_data
        ]
    
        return MeetingResponse(
            meeting=meeting,
            sessions=sessions,
            total_sessions=len(sessions)
        )
  • Pydantic schemas: SessionInfo, MeetingInfo, MeetingResponse for structured input/output validation.
    class SessionInfo(BaseModel):
        """Session information."""
        session_key: int = Field(..., description="Unique session identifier")
        session_name: str = Field(..., description="Session name (e.g., 'Race', 'Qualifying')")
        session_type: str = Field(..., description="Session type")
        date_start: str = Field(..., description="Session start datetime")
        date_end: str = Field(..., description="Session end datetime")
        gmt_offset: str = Field(..., description="GMT offset")
        circuit_key: int = Field(..., description="Circuit identifier")
        circuit_short_name: str = Field(..., description="Circuit short name")
    
    
    class MeetingInfo(BaseModel):
        """Meeting (race weekend) information."""
        meeting_key: int = Field(..., description="Unique meeting identifier")
        meeting_name: str = Field(..., description="Official meeting name")
        meeting_official_name: str = Field(..., description="Full official name")
        location: str = Field(..., description="Location/city")
        country_name: str = Field(..., description="Country name")
        circuit_key: int = Field(..., description="Circuit identifier")
        circuit_short_name: str = Field(..., description="Circuit short name")
        date_start: str = Field(..., description="Meeting start date")
        year: int = Field(..., description="Year")
    
    
    class MeetingResponse(BaseModel):
        """Response for meeting and session information."""
        meeting: Optional[MeetingInfo] = Field(None, description="Meeting information")
        sessions: list[SessionInfo] = Field(..., description="List of sessions in meeting")
        total_sessions: int = Field(..., description="Total number of sessions")
  • server.py:171-171 (registration)
    MCP server tool registration.
    mcp.tool()(get_meeting_info)
  • Package-level import registration in tools/live.
    from .meetings import get_meeting_info
  • Root tools package import registration.
    from .live import get_driver_radio, get_live_pit_stops, get_live_intervals, get_meeting_info, get_stints_live
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's function and return data (MeetingResponse with meeting info and all sessions), but lacks details on rate limits, error handling, or authentication requirements that would enhance transparency for an AI agent.

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 well-structured and front-loaded, starting with the core purpose, followed by key details, parameters, returns, and examples. Every sentence adds value without redundancy, making it efficient for an AI agent to parse and understand.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 annotations, but with an output schema), the description is largely complete. It covers purpose, parameters, returns, and examples, but could improve by addressing potential edge cases or clarifying the relationship with sibling tools like get_schedule for better contextual integration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaningful context for both parameters: 'year' is explained with data availability constraints (2023+), and 'country' is clarified with examples (e.g., 'Monaco', 'Italy', 'United States'), though it could specify format expectations like exact country names versus circuit locations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get meeting and session schedule information') and resources ('from OpenF1'), distinguishing it from siblings like get_schedule or get_session_details by focusing on comprehensive meeting-level data including sessions and circuit details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage by specifying the data source (OpenF1) and parameter constraints (year 2023+), but does not explicitly state when to use this tool versus alternatives like get_schedule or get_session_details, which might offer overlapping functionality.

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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