F1verse MCP server

f1verse

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The story layer for Formula 1 data. Data libraries fetch and tidy — f1verse tells you what happened: lead changes, laps led, event timelines, stint strategy, race pace — and what happens next: title probabilities from a season simulation that ships with its own backtest.

Zero dependencies. Standard library only. Full live-timing coverage from 2023; lap-by-lap racing back to 1996, pit stops to 2011, and results, qualifying and standings to 1950 — each answer stating which era it came from and what that era does not hold.

pip install f1verse

import f1verse

race = f1verse.load(2026, 12)        # year, round — no other library needed

race.laps_led()      # {'ANT': 32, 'NOR': 31, 'HAM': 9}
race.leader_runs()   # [{'abbr': 'NOR', 'from': 1, 'to': 4}, ...]
race.results()[7]    # {'abbr': 'HUL', 'gap': '+1 LAP', ...}
race.race_pace()     # median pace — pit/SC/VSC laps excluded by default
race.story()         # one call, whole story, plain JSON

race.championship_prediction()   # per-lap "if it ended now" title projection
race.team_radio()                # timestamped clip URLs (nothing downloaded)

f1verse.championship_projection(2026)   # who wins the title, 20,000 seasons
f1verse.title_scenarios(2026)           # and who is mathematically out

Give it to an AI agent

<!-- mcp-name: io.github.jinsim/f1verse -->

f1verse ships its own MCP server. No install step, no dependencies:

{"mcpServers": {"f1verse": {"command": "uvx", "args": ["--from", "f1verse", "f1verse-mcp"]}}}

That is the whole setup — the server is standard library only, so it starts and answers tools/list in about 140 ms instead of unpacking a scientific stack into a throwaway environment first. Eight tools, not eighty: a model picks the right one.

For any other LLM pipeline, the library describes itself:

f1verse.tools()                  # MCP-dialect JSON schemas
f1verse.tools("openai")          # function-calling dialect
f1verse.call_tool("f1_race_story", {"year": 2026, "round": 12})

Errors are written for the caller that has to fix them without reading this page:

f1verse.call_tool("f1_race_summary", {})
# LookupError: unknown tool 'f1_race_summary' — available: f1_race_story, ...
f1verse.load_session(2026, 12, "Qualy")
# LookupError: ... listing the sessions that weekend actually had

The whole weekend, not just the race

f1verse.sessions(2026, 12)
# Practice 1 · Sprint Qualifying · Sprint · Qualifying · Race

q = f1verse.load_session(2026, 12, "Qualifying")
q.results()[0]
# {'abbr': 'NOR', 'q1': 72.695, 'q1_gap': 0.085, 'q3': 71.163, 'q3_gap': 0.0,
#  'best': 71.163, 'eliminated_in': None, ...}

q.segments()["q1"]
# {'fastest': 'PIA', 'advanced': [...16 codes...], 'eliminated': [...],
#  'cut_margin': 0.022}

Each kind gets the classification it actually has. Qualifying gaps are to the fastest lap of that segment — the pole-sitter above was 0.085 s off in Q1 — because a single "gap to leader" column would misreport the session. A sprint loads as a Race; practice is a best-lap table.

Is this data safe to publish?

Shortened. The full README is on GitHub.