io.github.darshjoshi/pitwall

<!-- mcp-name: io.github.darshjoshi/pitwall --> <h1 align="center">Pitwall</h1>

<p align="center"> <strong>Turn Claude into your F1 race engineer.</strong><br> Real telemetry. Real strategy data. Real-time during races. 75 years of history. </p>

<p align="center"> <a href="https://pypi.org/project/f1pitwall/"><img src="https://img.shields.io/pypi/v/f1pitwall?color=orange&label=PyPI" alt="PyPI"></a> <a href="https://pypi.org/project/f1pitwall/"><img src="https://img.shields.io/pypi/dm/f1pitwall?color=blue&label=Downloads" alt="Downloads"></a> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/Python-3.10+-blue.svg" alt="Python 3.10+"></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-Compatible-purple.svg" alt="MCP Compatible"></a> <a href="https://github.com/darshjoshi/pitwall/stargazers"><img src="https://img.shields.io/github/stars/darshjoshi/pitwall?style=social" alt="GitHub Stars"></a> </p>

<p align="center"> <img src="https://raw.githubusercontent.com/darshjoshi/pitwall/main/assets/ver_vs_nor_abu_dhabi_2024_quali.png" alt="Verstappen vs Norris — Abu Dhabi 2024 Qualifying Speed Trace" width="700"> </p>

---

Quick Start

pip install f1pitwall
claude mcp add pitwall -- f1pitwall

Then ask Claude: "Who won the 2025 Australian GP?"

Want visual plots and deep analysis? pip install "f1pitwall[full]"

<details> <summary><strong>Install from source instead</strong></summary>

git clone https://github.com/darshjoshi/pitwall.git && cd pitwall
pip install "mcp[cli]" requests        # lite
pip install -r requirements-full.txt   # full
claude mcp add pitwall -- python3 $(pwd)/pitwall.py

</details>

---

Why Pitwall?

Claude knows F1 from training data — but it can't look up last week's race. It can't show you Verstappen's throttle trace through Turn 1. It doesn't know who pitted first or when the safety car came out.

Pitwall connects Claude to live F1 data:

• Real data, not hallucinations — actual timing feeds from formula1.com
• Lap-level telemetry — speed, RPM, throttle, brake, gear, DRS at 4Hz per car
• Visual plots — speed trace comparisons, gear shift maps returned as images
• 75 years of history — every race result and championship since 1950
• Live during races — real-time positions, gaps, weather, and race control
• Zero API keys — all core data is free, no account needed

---

What You Can Ask

"Who won the 2025 Australian GP?"           → Race results and classification
"Verstappen's speed on lap 25 at Monaco"    → Lap telemetry at 4Hz
"Plot Hamilton vs Norris speed trace"       → Visual speed comparison chart
"Compare Ferrari's tyre strategy"           → Stint-by-stint breakdown
"Who won the 1994 championship?"            → 75 years of history
"When was the safety car at Silverstone?"   → Race control messages and flags

<details> <summary><strong>See all example questions</strong></summary>

Question	Tool Used
"Who won the Chinese GP?"	get_standings
"What was Verstappen's speed on lap 25?"	get_telemetry
"Compare Hamilton vs Leclerc"	get_driver_comparison
"What tyres did everyone use?"	get_tyre_strategy
"Fastest pit stop at Australia 2025?"	get_pit_stops
"When was the safety car?"	get_race_control
"Was it raining during the race?"	get_weather
"Top speeds at Monza 2024?"	get_speed_traps
"Norris's lap times in the race"	get_lap_times
"Who won the 2005 championship?"	get_championship_standings
"Plot Verstappen vs Hamilton speed trace"	plot_telemetry_comparison
"Show me the gear shift map at Monaco"	plot_gear_shifts
"Who gained the most positions?"	compare_grid_to_finish
"Overtakes in the race"	detect_overtakes
"Compare Verstappen lap 5 vs lap 50"	plot_multi_telemetry_comparison
"Ferrari head-to-head in qualifying"	team_head_to_head
"Deleted laps in qualifying"	get_deleted_laps
"Gap to leader throughout the race"	get_gap_to_leader

</details>

---

Features

67 tools across two modes. Pitwall auto-detects what's installed — no config changes needed.

Lite Mode (14 tools)

pip install "mcp[cli]" requests — no heavy dependencies.

Race results, lap times, telemetry, tyre strategy, pit stops, weather, race control, speed traps, driver comparison, and historical data back to 1950. Uses F1's free static archive and the Jolpica API.

Full Mode (67 tools)

pip install -r requirements-full.txt — adds FastF1.

Everything in Lite, plus:

Category	What You Get
Visual Plots	Speed trace comparisons, gear shift maps, multi-lap telemetry overlays
Deep Telemetry	Brake point analysis, RPM patterns, DRS usage, throttle traces
Advanced Strategy	Stint degradation, compound comparisons, tire age performance
Race Intelligence	Overtake detection, gap tracking, position changes, qualifying progression
Live Data	Real-time positions, lap times, sector times, weather during active sessions

<details> <summary><strong>Full tool list (67 tools)</strong></summary>

Lite Tools (always available)

Tool	Description
list_seasons	Available seasons (2018-present)
list_races	Full season calendar with dates
get_race_info	Session details and available data feeds
get_standings	Race classification — positions, gaps, best laps, pits
get_lap_times	Lap-by-lap times, filterable by driver and lap range
get_telemetry	Speed, RPM, throttle, brake, gear, DRS for a specific lap
get_tyre_strategy	Compound, stint length, new/used for every driver
get_pit_stops	All pit stops sorted by fastest
get_race_control	Flags, penalties, safety cars, investigations
get_weather	Air/track temp, rain, humidity, wind
get_speed_traps	Speed at 4 measurement points per driver
get_driver_comparison	Head-to-head: position, pace, strategy, pit stops
get_historical_results	Race results from 1950 to present
get_championship_standings	Driver/constructor championships from 1950+

FastF1 Tools (requires FastF1)

Category	Tools
Visual Plots	plot_telemetry_comparison, plot_gear_shifts, plot_multi_telemetry_comparison, plot_driver_telemetry_comparison
Telemetry Analysis	analyze_brake_points, analyze_rpm_data, analyze_drs_usage
Lap Analysis	get_lap_times_fastf1, get_deleted_laps, analyze_lap_consistency, get_fastest_sectors, get_personal_best_laps, compare_sector_times
Strategy	get_driver_tyre_detail, get_stint_analysis, compare_tire_compounds, compare_tire_age_performance, analyze_starting_tires, compare_strategies
Race Analysis	get_race_results, get_sprint_results, get_session_summary, get_fastest_lap_data, detect_overtakes, compare_grid_to_finish, get_qualifying_progression
Pit Stops	get_pit_stop_detail, get_fastest_pit_stops
Driver & Team	get_driver_info, get_driver_standings, get_constructor_standings, team_head_to_head, get_team_laps, analyze_long_run_pace
Track & Safety	get_circuit_info, get_track_status, get_track_record, get_race_control_messages, get_penalties, get_dnf_list
Speed & Position	get_speed_trap_comparison, get_position_changes, get_gap_to_leader
History	get_race_winners_history
Live Data	get_live_session_status, get_live_positions, get_live_lap_times, get_live_sector_times, get_live_telemetry, get_live_weather
Session	get_schedule, get_session_info, get_weather_data

</details>

---

Setup

<details> <summary><strong>macOS</strong></summary>

Claude Code:

claude mcp add pitwall -- python3 /absolute/path/to/pitwall.py

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "pitwall": {
      "command": "python3",
      "args": ["/absolute/path/to/pitwall.py"]
    }
  }
}

</details>

<details> <summary><strong>Windows</strong></summary>

1. Find your Python path:

where python

This will return something like C:\Users\YourName\AppData\Local\Programs\Python\Python313\python.exe or C:\Python313\python.exe.

2. Note where you cloned Pitwall: For example: C:\Users\YourName\Projects\pitwall\pitwall.py

Claude Code (PowerShell):

claude mcp add pitwall -- python C:\Users\YourName\Projects\pitwall\pitwall.py

Claude Desktop — add to %APPDATA%\Claude\claude_desktop_config.json:

To open this folder, press Win + R, type %APPDATA%\Claude, and hit Enter. If the Claude folder or claude_desktop_config.json doesn't exist, create them.

{
  "mcpServers": {
    "pitwall": {
      "command": "python",
      "args": ["C:\\Users\\YourName\\Projects\\pitwall\\pitwall.py"]
    }
  }
}

Note: Use double backslashes (\\) in the JSON path, or forward slashes (/) — both work. The command is python (not python3) on Windows. </details>

<details> <summary><strong>Linux</strong></summary>

Claude Code:

claude mcp add pitwall -- python3 /absolute/path/to/pitwall.py

Claude Desktop — add to ~/.config/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "pitwall": {
      "command": "python3",
      "args": ["/absolute/path/to/pitwall.py"]
    }
  }
}

</details>

Restart Claude Code or Claude Desktop after setup. Works with any MCP-compatible client.

Optional: Beginner-Friendly Skill (Claude Desktop)

Upload SKILL.md as a skill in Claude Desktop (Settings → Skills → Upload). Claude will explain F1 jargon inline — DRS, undercut, compound, safety car, etc.

---

Data Sources

All core data is free and requires no API keys.

Source	Coverage	What it provides
F1 Static Live Timing	2018-present	Telemetry, timing, strategy, pit stops, weather, race control
Jolpica-F1	1950-present	Historical results and championships
FastF1 (optional)	2018-present	Enhanced telemetry analysis and visual plots
F1 SignalR Core (optional)	Live only	Real-time race data during active sessions

---

How It Works

Pitwall reads from F1's publicly available static timing archive — the same data that powers the official F1 app. After each session ends (~30 minutes), F1 publishes 33 data feeds per session including full car telemetry (speed, RPM, throttle, brake, gear, DRS at ~4Hz per car), GPS positions, tyre data, pit stops, and race control messages.

The telemetry tool (get_telemetry) correlates the timing stream with the car data stream to extract telemetry for a specific driver on a specific lap — something no other F1 MCP server does.

Architecture

Claude ──MCP──> Pitwall ──HTTP──> livetiming.formula1.com/static/ (free)
                       ──HTTP──> api.jolpi.ca/ergast/f1/ (free)
                       ──lib──>  FastF1 (optional, local)
                       ──WS───> SignalR Core (optional, live races)

Running the Server

python3 pitwall.py              # MCP stdio (Claude Code / Claude Desktop)
python3 pitwall.py --http       # MCP HTTP (remote / self-hosted)
python3 pitwall.py --http --port 3000

---

Live Race Data

Pitwall includes a raw SignalR Core WebSocket client for real-time data during active F1 sessions. Most data is free — car telemetry and GPS require an F1 TV Pro or Premium subscription.

<details> <summary><strong>What's free vs what needs F1 TV</strong></summary>

Data	Free	F1 TV
Race positions, gaps, lap times	Yes	Yes
Race control, flags, penalties	Yes	Yes
Weather, track status	Yes	Yes
Tyre compounds, stint info	Yes	Yes
Team radio URLs	Yes	Yes
Car telemetry (speed, RPM, throttle, brake)	No	Yes
GPS positions (X/Y/Z coordinates)	No	Yes

All data (including telemetry and GPS) becomes free in the static archive ~30 minutes after a session ends.

</details>

<details> <summary><strong>Authentication setup</strong></summary>

python3 auth_setup.py

This opens a browser for F1 TV login. The token is saved locally:

• <project_dir>/.f1token
• ~/Library/Application Support/fastf1/f1auth.json (macOS)

Token expires every ~4 days. Re-run to refresh. Never uploaded anywhere.

</details>

<details> <summary><strong>Live client usage</strong></summary>

import asyncio
from signalr_client import F1LiveClient

async def main():
    # Free mode — timing, weather, race control (no auth needed)
    client = F1LiveClient(no_auth=True)

    @client.on("TimingData")
    def on_timing(data, timestamp):
        for num, info in data.get("Lines", {}).items():
            print(f"P{info.get('Position','?')} #{num} Gap: {info.get('GapToLeader','')}")

    @client.on("RaceControlMessages")
    def on_rc(data, timestamp):
        for msg in data.get("Messages", {}).values():
            print(f"[{msg.get('Flag', '')}] {msg.get('Message', '')}")

    await client.connect()

asyncio.run(main())

For full telemetry (speed, RPM, throttle, brake, GPS):

from auth_setup import load_token

client = F1LiveClient(no_auth=False, auth_token=load_token())

@client.on("CarData.z")
def on_telemetry(data, timestamp):
    # Speed, RPM, throttle, brake, gear, DRS at ~4Hz per car
    ...

@client.on("Position.z")
def on_position(data, timestamp):
    # GPS X/Y/Z coordinates at ~4Hz per car
    ...

</details>

---

Reference

Race Names

Race names are fuzzy-matched. All of these work:

"china", "chinese", "shanghai"           → Chinese Grand Prix
"australia", "melbourne", "aus"          → Australian Grand Prix
"monaco", "monte carlo"                  → Monaco Grand Prix
"silverstone", "great britain", "british" → British Grand Prix

Driver Codes

VER = Verstappen    HAM = Hamilton    NOR = Norris     LEC = Leclerc
ANT = Antonelli     RUS = Russell     PIA = Piastri    BEA = Bearman
GAS = Gasly         LAW = Lawson      HAD = Hadjar     SAI = Sainz
ALO = Alonso        STR = Stroll      OCO = Ocon       BOT = Bottas
ALB = Albon         HUL = Hulkenberg  COL = Colapinto  LIN = Lindblad

Project Files

File	Purpose
pitwall.py	MCP server — 67 tools, auto-degrades to 14 without FastF1
signalr_client.py	Raw SignalR Core WebSocket client for live race data
decompressor.py	Zlib decompression for CarData.z / Position.z
merger.py	Keyframe + delta state management for F1's incremental format
topics.py	All 20 SignalR topics with auth/compression metadata
auth_setup.py	F1 TV token setup — browser-based OAuth flow

---

Contributing

Found a bug? Want to add a tool? Contributions are welcome.

1. Fork the repo
2. Create a feature branch
3. Make your changes
4. Run the test suite: python3 tests/pitwall_tool_validation.py
5. Open a pull request

---

Credits

• FastF1 by @theOehrly — the gold standard F1 Python library
• Jolpica-F1 — the Ergast API successor
• drivenrajat/f1 — inspiration for FastF1 tool patterns

Built by

Darsh Joshi — AI Engineer

License

MIT