F1verse

Formula 1 race data with the domain rules applied: stints, tyre wear, publish-safety. Zero deps.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

f1verse

Documentation · MCP server · llms.txt · Changelog · Contributing · Security · License

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.

Terminal

pip install f1verse

server.ts

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

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

config.json

{"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:

python

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:

python

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

python

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?

python

race.quality_report()
# {'state': 'final',            # provisional → settled → final, or corrected
#  'coverage': {'overall': 0.9955, 'sectors': 0.9824, 'compound': 1.0},
#  'missing': ['STR.lap_46.lap_duration', ...],
#  'source_age_seconds': 312,
#  'revisions': [],             # source rewrites this install has observed
#  'crosscheck': {...},
#  'publishable': True}

crosscheck answers do independent sources agree. quality_report adds the three things that verdict is silent about: how complete the data is, how old the copy is, and whether the classification is still provisional.

The chequered flag is not the final classification — scrutineering disqualifications and penalties land hours later and rewrite rows in place. So the rows the stewards can change are not cached forever until the session is final, and any change that is seen is recorded:

python

before = race.snapshot()          # hashed, comparable, JSON — you persist it
...
f1verse.diff(before, race.refresh().snapshot())
# {'changed': True,
#  'changes': [{'abbr': 'HAM', 'field': 'position', 'before': 4, 'after': None},
#              {'abbr': 'HAM', 'field': 'gap', 'before': '+8.1s', 'after': 'DSQ'}]}

f1verse.revisions()               # every source rewrite observed, with the
f1verse.vintage(rec)              # superseded body when it was small enough

There is deliberately no as_of= time travel: f1verse can tell you what it sees now and when it saw a value change, not reconstruct a value nobody here ever fetched.

How the race actually unfolded

python

race = f1verse.load(2025, 24)

race.running_order()[30]
# ['PIA', 'VER', 'NOR', 'LEC', 'RUS', ...]   who was where, lap by lap

race.position_changes()[1]
# {'lap': 2, 'moves': 18, 'biggest': {'abbr': 'PIA', 'gained': 17,
#                                     'from': 19, 'to': 2}}

race.battles()[0]
# {'ahead': 'HAD', 'behind': 'OCO', 'from': 2, 'to': 14, 'laps': 13,
#  'closest': 0.529}      <- a thirteen-lap fight the results table hides

battles finds pairs that held consecutive positions within 1.5 s for at least three laps. A scrap for eighth that ran a third of the race never shows up in a classification; it is often the best part of the afternoon.

Races from before the live feeds

python

old = f1verse.load_archive(2008, 18)     # Brazil, the last-corner title

old.coverage
# {'lap_times': True, 'pit_stops': False, 'stints': False,
#  'note': 'lap times only'}

old.leader_runs()
# [{'abbr': 'MAS', 'from': 1, 'to': 9}, {'abbr': 'TRU', 'from': 10, 'to': 11},
#  {'abbr': 'MAS', 'from': 12, 'to': 38}, ...]

old.laps_led()          # {'MAS': 64, 'TRU': 2, 'ALO': 2, 'RAI': 3}

The coverage block is not decoration. 2008 has no stint data anywhere, so ArchiveRace has no stints() — rather than returning an empty dict that reads like "no pit stops happened". What the era recorded, you get; what it did not, it says.

Seasons against each other

python

f1verse.season_shape(2025)
# {'rounds': 24, 'final_margin': 2.0,
#  'lead_changes': [{'round': 5, 'from': 'NOR', 'to': 'PIA'},
#                   {'round': 20, 'from': 'PIA', 'to': 'NOR'}], ...}

f1verse.title_margins(top=5)
# 2025  NOR 423.0 vs VER 421.0   margin 2.0   (0.08 of a win)
# 2008  HAM  98.0 vs MAS  97.0   margin 1.0   (0.10 of a win)
# 2012  VET 281.0 vs ALO 278.0   margin 3.0   (0.12 of a win)

Points systems changed repeatedly, so a raw margin cannot compare eras — one point in 1958 was most of a win. Every row also carries the gap measured in wins, which is the comparison that survives the rule changes.

Where the passing happened

python

f1verse.overtake_hotspots(race)[0]
# {'from_s': 3510.0, 'to_s': 3540.0, 'signals': 69, 'drivers': [...]}

The timing feed publishes an OvertakeState per car that almost never changes — about a hundred transitions in nineteen thousand records. That sparsity is the value: the transitions are a free index of the moments worth looking at, from the same feed that times the race. Read it as "something happened here", then confirm against running_order.

Beyond a single race

python

f1verse.career("max_verstappen")
# {'starts': 245, 'wins': 71, 'podiums': 131, 'poles': 48, ...}  1950-present

f1verse.milestones("max_verstappen")
# [{'stat': 'poles', 'current': 48, 'target': 50, 'remaining': 2}]

f1verse.circuit_profile(2026, 13)
# corners, marshal sectors, track outline, and pit loss split by track state
# {'normal': 25.43, 'sc': 16.11, 'vsc': 18.4}  <- what an undercut costs here
# plus historic record: 75 races held, pole-to-win rate 0.30

# Geometry is also interpreted, but never overclaimed: every corner carries
# its lap position, preceding-run share and local heading deflection; mini-
# and marshal-sector boundaries carry lap percentages.
profile = f1verse.circuit_profile(2026, 13)
profile["layout"]["corners"][0]
# {'number': 1, 'progress_pct': 8.412, 'local_deflection_deg': 72.6, ...}

# What a map cannot say, the cars can. This measures the circuit from the
# session's own telemetry: the height profile, where overtakes actually
# happen, how much of the lap is flat out and where it is braked, and every
# numbered corner as the car experienced it.
f1verse.circuit_survey(2026, 13)["corners"]["corners"][0]
# {'corner': 1, 'apex_speed_kph': 103, 'radius_m': 40.0,
#  'lateral_load_g': 2.09, 'gear_at_apex': 2, 'severity': 'medium'}

# Published specifications are stored rather than derived, and the
# measurement audits them rather than replacing them.
f1verse.circuit_profile(2026, 13, measure=True)["audit"]
# {'verdict': 'agrees', 'checked_age_days': 0,
#  'checks': [{'field': 'length_m', 'published': 4259, 'measured': 4274.4,
#              'off_by_percent': 0.36, 'state': 'agrees'}, ...]}

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about F1verse

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "f1verse": { "command": "npx", "args": ["-y", "f1verse"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

36Quality signal: Fair · 36/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to F1verse →Install in Claude Desktop Install in Cursor Install in VS Code

F1verse

Formula 1 race data with the domain rules applied: stints, tyre wear, publish-safety. Zero deps.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

f1verse

Documentation · MCP server · llms.txt · Changelog · Contributing · Security · License

Terminal

pip install f1verse

server.ts

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

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

config.json

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

For any other LLM pipeline, the library describes itself:

python

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:

python

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

python

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}

Is this data safe to publish?

python

race.quality_report()
# {'state': 'final',            # provisional → settled → final, or corrected
#  'coverage': {'overall': 0.9955, 'sectors': 0.9824, 'compound': 1.0},
#  'missing': ['STR.lap_46.lap_duration', ...],
#  'source_age_seconds': 312,
#  'revisions': [],             # source rewrites this install has observed
#  'crosscheck': {...},
#  'publishable': True}

python

before = race.snapshot()          # hashed, comparable, JSON — you persist it
...
f1verse.diff(before, race.refresh().snapshot())
# {'changed': True,
#  'changes': [{'abbr': 'HAM', 'field': 'position', 'before': 4, 'after': None},
#              {'abbr': 'HAM', 'field': 'gap', 'before': '+8.1s', 'after': 'DSQ'}]}

f1verse.revisions()               # every source rewrite observed, with the
f1verse.vintage(rec)              # superseded body when it was small enough

There is deliberately no as_of= time travel: f1verse can tell you what it sees now and when it saw a value change, not reconstruct a value nobody here ever fetched.

How the race actually unfolded

python

race = f1verse.load(2025, 24)

race.running_order()[30]
# ['PIA', 'VER', 'NOR', 'LEC', 'RUS', ...]   who was where, lap by lap

race.position_changes()[1]
# {'lap': 2, 'moves': 18, 'biggest': {'abbr': 'PIA', 'gained': 17,
#                                     'from': 19, 'to': 2}}

race.battles()[0]
# {'ahead': 'HAD', 'behind': 'OCO', 'from': 2, 'to': 14, 'laps': 13,
#  'closest': 0.529}      <- a thirteen-lap fight the results table hides

Races from before the live feeds

python

old = f1verse.load_archive(2008, 18)     # Brazil, the last-corner title

old.coverage
# {'lap_times': True, 'pit_stops': False, 'stints': False,
#  'note': 'lap times only'}

old.leader_runs()
# [{'abbr': 'MAS', 'from': 1, 'to': 9}, {'abbr': 'TRU', 'from': 10, 'to': 11},
#  {'abbr': 'MAS', 'from': 12, 'to': 38}, ...]

old.laps_led()          # {'MAS': 64, 'TRU': 2, 'ALO': 2, 'RAI': 3}

Seasons against each other

python

f1verse.season_shape(2025)
# {'rounds': 24, 'final_margin': 2.0,
#  'lead_changes': [{'round': 5, 'from': 'NOR', 'to': 'PIA'},
#                   {'round': 20, 'from': 'PIA', 'to': 'NOR'}], ...}

f1verse.title_margins(top=5)
# 2025  NOR 423.0 vs VER 421.0   margin 2.0   (0.08 of a win)
# 2008  HAM  98.0 vs MAS  97.0   margin 1.0   (0.10 of a win)
# 2012  VET 281.0 vs ALO 278.0   margin 3.0   (0.12 of a win)

Where the passing happened

python

f1verse.overtake_hotspots(race)[0]
# {'from_s': 3510.0, 'to_s': 3540.0, 'signals': 69, 'drivers': [...]}

Beyond a single race

python

f1verse.career("max_verstappen")
# {'starts': 245, 'wins': 71, 'podiums': 131, 'poles': 48, ...}  1950-present

f1verse.milestones("max_verstappen")
# [{'stat': 'poles', 'current': 48, 'target': 50, 'remaining': 2}]

f1verse.circuit_profile(2026, 13)
# corners, marshal sectors, track outline, and pit loss split by track state
# {'normal': 25.43, 'sc': 16.11, 'vsc': 18.4}  <- what an undercut costs here
# plus historic record: 75 races held, pole-to-win rate 0.30

# Geometry is also interpreted, but never overclaimed: every corner carries
# its lap position, preceding-run share and local heading deflection; mini-
# and marshal-sector boundaries carry lap percentages.
profile = f1verse.circuit_profile(2026, 13)
profile["layout"]["corners"][0]
# {'number': 1, 'progress_pct': 8.412, 'local_deflection_deg': 72.6, ...}

# What a map cannot say, the cars can. This measures the circuit from the
# session's own telemetry: the height profile, where overtakes actually
# happen, how much of the lap is flat out and where it is braked, and every
# numbered corner as the car experienced it.
f1verse.circuit_survey(2026, 13)["corners"]["corners"][0]
# {'corner': 1, 'apex_speed_kph': 103, 'radius_m': 40.0,
#  'lateral_load_g': 2.09, 'gear_at_apex': 2, 'severity': 'medium'}

# Published specifications are stored rather than derived, and the
# measurement audits them rather than replacing them.
f1verse.circuit_profile(2026, 13, measure=True)["audit"]
# {'verdict': 'agrees', 'checked_age_days': 0,
#  'checks': [{'field': 'length_m', 'published': 4259, 'measured': 4274.4,
#              'off_by_percent': 0.36, 'state': 'agrees'}, ...]}

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about F1verse

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "f1verse": { "command": "npx", "args": ["-y", "f1verse"] } }

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HTML Embed

<a href="https://allmcps.com/mcp/f1verse"><img src="https://allmcps.com/api/badge/f1verse?style=directory" alt="F1verse on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

36Quality signal: Fair · 36/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity1/15

Community engagement0/10

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to F1verse →Install in Claude Desktop Install in Cursor Install in VS Code