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Formula One MCP Server

get_championship_standings

Retrieve Formula One championship standings for a specific season, optionally filtered by round number. Use this tool to access driver and constructor rankings throughout the racing calendar.

Instructions

Get Formula One championship standings

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesSeason year (e.g., 2023)
round_numNoRound number (optional, gets latest standings if not provided)

Implementation Reference

  • Core handler function that retrieves driver and constructor championship standings using FastF1's Ergast API, processes the data into JSON-serializable format, and handles errors.
    def get_championship_standings(year, round_num=None):
        """
        Get championship standings for drivers and constructors.
    
        Args:
            year (int or str): The year of the F1 season
            round_num (int, optional): Specific round number or None for latest
    
        Returns:
            dict: Status and championship standings or error information
        """
        try:
            year = int(year)
    
            # Create Ergast API client
            ergast = fastf1.ergast.Ergast()
    
            # Get Ergast API data
            if round_num:
                round_num = int(round_num)  # Ensure proper type conversion
                drivers_standings = ergast.get_driver_standings(
                    season=year, round=round_num
                ).content[0]
                constructor_standings = ergast.get_constructor_standings(
                    season=year, round=round_num
                ).content[0]
            else:
                drivers_standings = ergast.get_driver_standings(season=year).content[0]
                constructor_standings = ergast.get_constructor_standings(
                    season=year
                ).content[0]
    
            # Convert driver standings to JSON serializable format
            drivers_list = []
            for _, row in drivers_standings.iterrows():
                driver_dict = row.to_dict()
                clean_dict = {k: json_serial(v) for k, v in driver_dict.items()}
                drivers_list.append(clean_dict)
    
            # Convert constructor standings to JSON serializable format
            constructors_list = []
            for _, row in constructor_standings.iterrows():
                constructor_dict = row.to_dict()
                clean_dict = {k: json_serial(v) for k, v in constructor_dict.items()}
                constructors_list.append(clean_dict)
    
            return {
                "status": "success",
                "data": {
                    "drivers": drivers_list,
                    "constructors": constructors_list,
                },
            }
        except Exception as e:
            logger.error(f"Error getting championship standings: {str(e)}", exc_info=True)
            return {
                "status": "error",
                "message": f"Failed to get championship standings: {str(e)}",
            }
  • MCP tool schema definition including input parameters (year required, round_num optional) and description.
    types.Tool(
        name="get_championship_standings",
        description="Get Formula One championship standings",
        inputSchema={
            "type": "object",
            "properties": {
                "year": {
                    "type": "number",
                    "description": "Season year (e.g., 2023)",
                },
                "round_num": {
                    "type": "number",
                    "description": (
                        "Round number (optional, gets latest "
                        "standings if not provided)"
                    ),
                },
            },
            "required": ["year"],
        },
    ),
  • Dispatch logic in the MCP call_tool handler that validates arguments and calls the get_championship_standings function.
    elif name == "get_championship_standings":
        round_num = arguments.get("round_num")
        if round_num is not None:
            try:
                round_num = int(round_num)
                if round_num <= 0:
                    raise ValueError("Round number must be positive")
            except (ValueError, TypeError) as e:
                raise ValueError(f"Invalid round number: {round_num}") from e
    
        result = get_championship_standings(sanitized_args["year"], round_num)
    else:
  • Import of the get_championship_standings handler function into the MCP server module.
    get_championship_standings,
Behavior2/5

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 but offers minimal information. It states what the tool does but doesn't describe response format, error conditions, rate limits, or whether it's a read-only operation (though 'Get' implies reading). This leaves significant gaps for agent understanding.

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 a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple tool and front-loads the essential information without unnecessary elaboration.

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

Completeness2/5

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

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the standings include (e.g., driver/constructor rankings, points), how results are formatted, or any behavioral aspects like pagination or data freshness. This leaves the agent with insufficient context for reliable use.

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?

The input schema has 100% description coverage, thoroughly documenting both parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline score of 3 where the schema does the heavy lifting without compensating value.

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 tool's purpose with a specific verb ('Get') and resource ('Formula One championship standings'), making it immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'get_driver_info' or 'get_session_results' beyond the resource type, which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or how it relates to sibling tools like 'get_session_results' or 'compare_drivers', leaving the agent to infer appropriate usage scenarios.

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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