F1 MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | The Node.js environment mode | production |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_session_dataC | Get detailed F1 session data including telemetry, timing, and weather |
| analyze_tire_performanceC | Advanced tire performance analysis with degradation modeling and strategy insights |
| analyze_lap_timesC | Sophisticated lap time analysis with fuel correction and mini-sector breakdown |
| predict_weather_impactC | Weather impact analysis and prediction for race strategy |
| simulate_race_strategyC | Monte Carlo race strategy simulation with probabilistic outcomes |
| analyze_driver_performanceC | Driver performance extraction separating skill from car performance |
| get_real_time_telemetryC | Real-time telemetry data processing with advanced signal analysis |
| analyze_sector_performanceC | 25 mini-sector analysis with track curvature correlation |
| get_current_season_infoC | Get current F1 season information including live data availability |
| get_race_scheduleB | Get F1 race schedule for any year including 2025 |
| connect_live_timingB | Connect to F1 Official Live Timing API for real-time data |
| get_live_timing_statusB | Get status of F1 Live Timing API connection |
| get_api_statusB | Get status of all F1 data APIs (Ergast, OpenF1, Live Timing) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 13 tools
Every tool has a clearly distinct purpose with no overlap; for example, analyze_driver_performance focuses on driver skill separation, analyze_lap_times on lap breakdowns, and get_race_schedule on scheduling, making misselection unlikely.
All tool names follow a consistent verb_noun pattern using snake_case, such as analyze_driver_performance, get_race_schedule, and predict_weather_impact, with no deviations in style or convention.
With 13 tools, the count is well-scoped for an F1 data server, covering analysis, real-time data, scheduling, and simulations without being overwhelming or insufficient for the domain.
The toolset provides complete coverage for F1 data analysis, including real-time telemetry, historical data, performance insights, strategy simulation, and API status checks, with no apparent gaps in the domain.