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notsedano

Formula One MCP Server

by notsedano

get_driver_info

Retrieve Formula One driver details including performance data and statistics for specific seasons, events, and sessions.

Instructions

Get information about a specific Formula One driver

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesSeason year (e.g., 2023)
event_identifierYesEvent name or round number (e.g., 'Monaco' or '7')
session_nameYesSession name (e.g., 'Race', 'Qualifying', 'Sprint', 'FP1', 'FP2', 'FP3')
driver_identifierYesDriver identifier (number, code, or name; e.g., '44', 'HAM', 'Hamilton')

Implementation Reference

  • Core handler function that implements the get_driver_info tool logic. Loads F1 session data using fastf1 library and retrieves specific driver information.
    def get_driver_info(
        year: Any, event_identifier: str, session_name: str, driver_identifier: str
    ) -> dict[str, Any]:
        """
        Get information about a specific Formula One driver.
    
        Args:
            year (int or str): The year of the F1 season
            event_identifier (str): Event name or round number
            session_name (str): Session type (Race, Qualifying, Sprint, etc.)
            driver_identifier (str): Driver number, code, or name
    
        Returns:
            dict: Status and driver information or error information
        """
        try:
            # Validate year
            year_int = validate_year(year)
    
            logger.debug(
                f"Fetching driver info for {year_int}, "
                f"event: {event_identifier}, session: {session_name}, "
                f"driver: {driver_identifier}"
            )
            session = fastf1.get_session(year_int, event_identifier, session_name)
            # Load session without telemetry for faster results
            session.load(telemetry=False)
    
            driver_info = session.get_driver(driver_identifier)
    
            # Convert to JSON serializable format
            driver_dict = driver_info.to_dict()
            clean_dict = {k: json_serial(v) for k, v in driver_dict.items()}
    
            logger.info(f"Successfully retrieved driver info for {driver_identifier}")
            return {"status": "success", "data": clean_dict}
        except Exception as e:
            logger.error(f"Error retrieving driver info: {str(e)}", exc_info=True)
            return {
                "status": "error",
                "message": f"Failed to retrieve driver information: {str(e)}",
            }
  • MCP tool registration in list_tools() including name, description, and JSON schema for input validation.
    types.Tool(
        name="get_driver_info",
        description=("Get information about a specific Formula One driver"),
        inputSchema={
            "type": "object",
            "properties": {
                "year": {
                    "type": "number",
                    "description": "Season year (e.g., 2023)",
                },
                "event_identifier": {
                    "type": "string",
                    "description": (
                        "Event name or round number (e.g., 'Monaco' or '7')"
                    ),
                },
                "session_name": {
                    "type": "string",
                    "description": (
                        "Session name (e.g., 'Race', 'Qualifying', "
                        "'Sprint', 'FP1', 'FP2', 'FP3')"
                    ),
                },
                "driver_identifier": {
                    "type": "string",
                    "description": (
                        "Driver identifier (number, code, or name; "
                        "e.g., '44', 'HAM', 'Hamilton')"
                    ),
                },
            },
            "required": [
                "year",
                "event_identifier",
                "session_name",
                "driver_identifier",
            ],
        },
    ),
  • Dispatch logic in the MCP call_tool handler that invokes the get_driver_info function with validated arguments.
    result = get_driver_info(
        sanitized_args["year"],
        str(arguments["event_identifier"]),
        str(arguments["session_name"]),
        str(arguments["driver_identifier"]),
    )
  • Tool mapping/registration in the MCP bridge application for direct function calls.
    'get_driver_info': get_driver_info,

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?

With no annotations, the description carries the full burden of behavioral disclosure. It only states the basic purpose, offering no details about side effects (none expected), authentication needs, rate limits, or data freshness. The agent is left guessing about 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?

A single sentence that is concise and front-loaded with the key action and resource. No unnecessary words or redundancy.

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?

Despite 4 required parameters and no output schema, the description fails to explain what information is returned, any constraints (e.g., driver must be participating in the session), or possible error conditions. It is insufficiently complete for a data retrieval tool.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description adds no extra semantic information beyond what the schema already provides; it just paraphrases the tool's purpose.

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 action ('Get') and resource ('information about a specific Formula One driver'), distinguishing it from sibling tools like 'get_telemetry' or 'get_session_results'. However, it could more precisely indicate that it returns session-level information, as implied by the required parameters.

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 is provided on when to use this tool versus alternatives like 'analyze_driver_performance' or 'compare_drivers'. There is no mention of prerequisites, exclusions, or use cases, leaving the agent to infer from names alone.

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