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Josh-Mantel

F1 MCP Server

by Josh-Mantel

get_constructor_standings

Retrieve Formula 1 constructor championship standings for any season, optionally after specific race rounds, to track team performance and rankings.

Instructions

Get constructor championship standings for a season

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesSeason year (e.g., 2024)
round_numberNoRound number (optional, gets standings after this round)

Implementation Reference

  • The handler function that executes the logic to fetch and compute constructor standings using FastF1.
    async def get_constructor_standings(arguments: Dict[str, Any]) -> List[TextContent]:
        """Get constructor championship standings."""
        year = arguments["year"]
        round_number = arguments.get("round_number")
    
        try:
            # Get the schedule to determine which round to use
            schedule = fastf1.get_event_schedule(year)
    
            if round_number is None:
                # Get latest completed round
                current_date = datetime.now()
                completed_rounds = schedule[schedule["EventDate"] <= current_date]
                if completed_rounds.empty:
                    round_number = 1
                else:
                    round_number = int(completed_rounds["RoundNumber"].max())
    
            # Get race session for standings calculation
            session = fastf1.get_session(year, round_number, "R")
            session.load()
    
            results = session.results
    
            # Group by team and sum points
            team_points = {}
            for _, driver in results.iterrows():
                team = driver["TeamName"]
                points = float(driver["Points"]) if pd.notna(driver.get("Points", 0)) else 0
    
                if team not in team_points:
                    team_points[team] = {"points": 0, "drivers": []}
    
                team_points[team]["points"] += points
                team_points[team]["drivers"].append(
                    {
                        "name": driver["FullName"],
                        "abbreviation": driver["Abbreviation"],
                        "points": points,
                    }
                )
  • The tool registration for 'get_constructor_standings' in the MCP server.
    name="get_constructor_standings",
    description="Get constructor championship standings for a season",
    inputSchema={
        "type": "object",
        "properties": {
            "year": {
                "type": "integer",
                "description": "Season year (e.g., 2024)",
            },
            "round_number": {
                "type": "integer",
                "description": "Round number (optional, gets standings after this round)",
  • The dispatch logic in the main tool handler that routes requests to the get_constructor_standings function.
    elif name == "get_constructor_standings":
        return await get_constructor_standings(arguments)
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. It states what the tool does but doesn't describe how it behaves—such as whether it returns real-time or historical data, potential rate limits, error conditions for invalid inputs, or the format of the returned standings. This leaves significant gaps in understanding the tool's operational characteristics.

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 without any unnecessary words or fluff. It is front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

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?

Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex data like standings. It doesn't explain what the output includes (e.g., team names, points, positions) or any behavioral aspects like data freshness or error handling. For a tool with two parameters and no structured output documentation, this leaves too much unspecified.

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 schema description coverage is 100%, so the input schema already documents both parameters ('year' and 'round_number') with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining the relationship between season and round or providing examples beyond the schema's details. This meets the baseline for high schema coverage.

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 ('constructor championship standings for a season'), making it immediately understandable. However, it doesn't explicitly distinguish itself from sibling tools like 'get_driver_standings' beyond the resource name difference, which is why it doesn't reach a score of 5.

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 like 'get_driver_standings' or other sibling tools. It lacks context about prerequisites, such as data availability for specific years or rounds, and doesn't mention any exclusions or complementary tools.

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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