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Machine-To-Machine

Formula One MCP Server (Python)

get_championship_standings

Retrieve Formula One championship standings for a season year, with optional round number to get standings at that point in the season.

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

  • Handler function that fetches F1 championship standings (drivers and constructors) using the FastF1 Ergast API. Accepts a year (required) and optional round_num. Returns JSON-serializable driver and constructor standings data.
    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 analyzing driver performance: {str(e)}", exc_info=True)
            return {
                "status": "error",
                "message": f"Failed to analyze driver performance: {str(e)}",
            }
  • Schema definition for get_championship_standings tool registration. Defines input parameters: year (required, number) and round_num (optional, number).
    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"],
        },
  • Tool registration via list_tools() function returning a types.Tool object with name 'get_championship_standings'.
    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"],
        },
    ),
  • Call handler routing in f1_tool() - validates round_num argument then dispatches to get_championship_standings handler 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)
  • Import of get_championship_standings from f1_data module into server.py.
    from .f1_data import (
        analyze_driver_performance,
        compare_drivers,
        get_championship_standings,

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It only states the basic purpose and does not disclose any behavioral traits such as data ordering, pagination, or whether historical data is included.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, complete sentence with no wasted words. It is appropriately front-loaded but lacks any structural elements like sections or examples.

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 retrieval tool with fully documented parameters and no output schema, the description is adequate. However, it could be improved by mentioning the return format or scope (e.g., drivers and/or constructors).

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?

Both parameters (year, round_num) are documented in the input schema with descriptions. The tool description adds no additional semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 retrieves Formula One championship standings, which is a specific verb+resource. It distinguishes itself from sibling tools that focus on drivers, events, or sessions, though it could be more explicit about whether standings include drivers, constructors, or both.

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. There is no mention of prerequisites, filtering options, or scenarios where other tools might be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.