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Formula One MCP Server

get_event_schedule

Retrieve the Formula One race calendar for a specific season to view event schedules and dates.

Instructions

Get Formula One race calendar for a specific season

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesSeason year (e.g., 2023)

Implementation Reference

  • Core handler function that implements the get_event_schedule tool logic: validates year, fetches schedule from fastf1, serializes data to JSON-compatible format, and returns structured response.
    def get_event_schedule(year: Any) -> dict[str, Any]:
        """
        Get the event schedule for a specified Formula One season.
    
        Args:
            year (int or str): The year of the F1 season
    
        Returns:
            dict: Status and schedule data or error information
        """
        try:
            # Validate year
            year_int = validate_year(year)
    
            logger.debug(f"Fetching event schedule for {year_int}")
            schedule = fastf1.get_event_schedule(year_int)
    
            # Convert DataFrame to JSON serializable format
            result = []
            for _, row in schedule.iterrows():
                event_dict = row.to_dict()
                # Clean and convert non-serializable values
                clean_dict = {k: json_serial(v) for k, v in event_dict.items()}
                result.append(clean_dict)
    
            logger.info(f"Successfully retrieved {len(result)} events for {year_int}")
            return {"status": "success", "data": result}
        except Exception as e:
            logger.error(f"Error retrieving event schedule: {str(e)}", exc_info=True)
            return {
                "status": "error",
                "message": f"Failed to retrieve event schedule: {str(e)}",
            }
  • MCP tool registration in the server's list_tools() method, including name, description, and input schema.
    types.Tool(
        name="get_event_schedule",
        description=("Get Formula One race calendar for a specific season"),
        inputSchema={
            "type": "object",
            "properties": {
                "year": {
                    "type": "number",
                    "description": "Season year (e.g., 2023)",
                },
            },
            "required": ["year"],
        },
  • Input schema definition for the get_event_schedule tool, specifying required 'year' parameter as number.
        "type": "object",
        "properties": {
            "year": {
                "type": "number",
                "description": "Season year (e.g., 2023)",
            },
        },
        "required": ["year"],
    },
  • Dispatch handler in the MCP server's call_tool method that routes to get_event_schedule with sanitized arguments.
    if name == "get_event_schedule":
        if "year" not in sanitized_args:
            sanitized_args["year"] = int(arguments["year"])
        result = get_event_schedule(sanitized_args["year"])
  • Helper function used by get_event_schedule to validate and normalize the year input parameter.
    def validate_year(year: Any) -> int:
        """
        Validate that the provided year is valid for F1 data.
    
        Args:
            year: Year value to validate
    
        Returns:
            Valid year as integer
    
        Raises:
            ValueError: If year is invalid
        """
        try:
            year_int = int(year)
            # F1 started in 1950 and we don't want future years far ahead
            current_year = datetime.now().year
            if year_int < 1950 or year_int > current_year + 1:
                raise ValueError(f"Year must be between 1950 and {current_year + 1}")
            return year_int
        except (ValueError, TypeError) as e:
            raise ValueError(f"Invalid year format: {year}") from e
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states it 'gets' data (implying read-only), but doesn't disclose behavioral traits like whether it returns past/future seasons, error handling for invalid years, rate limits, or data freshness. For a read tool with zero annotation coverage, this is a significant gap.

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?

Single sentence, zero waste, front-loaded with the core action. Every word earns its place by specifying the domain (Formula One), resource (race calendar), and scope (specific season).

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with one parameter and no output schema, the description is minimally adequate. It covers the purpose but lacks behavioral context (no annotations) and usage guidelines. Given the low complexity, it's complete enough to understand what it does, but not how to use it effectively.

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

Parameters3/5

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

Schema description coverage is 100%, with the single parameter 'year' documented as 'Season year (e.g., 2023)'. The description adds no additional parameter semantics beyond what the schema provides (e.g., valid year ranges, format constraints). Baseline 3 is appropriate when schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('Formula One race calendar'), specifying it's for a specific season. It distinguishes from siblings like get_event_info (single event) or get_session_results (session-level data), but doesn't explicitly contrast them. The purpose is unambiguous though not maximally differentiated.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like get_event_info (for single events) or get_session_results (for session data). The description implies usage for season-wide calendar retrieval but doesn't provide explicit when/when-not rules or mention sibling tools as alternatives.

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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