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MCP Sport

by andrequeiroz2version 0.1.0i★ –iRuns on your computer
Registry nameio.github.andrequeiroz2/mcp-sport

Install

Overview

Description from the maker (original English)

MCP server that exposes Formula 1 data from the OpenF1 API.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Games, sports, food & learning

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

claude mcp add sport -- uvx mcp-sport

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "sport": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-sport"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account andrequeiroz2, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Games, sports, food & learningi

  • More stars than 0% of the other 1308 servers in this category
  • Published to the registry more recently than 35% of the other 1308 servers in this category
  • Basic checks equal to or better than 100% of the other 1308 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MCP Sport safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Open Food Facts MCP, Modrinth MCP, Lichess MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Open Food Facts MCP

MCP server for Open Food Facts product, ingredient, allergen, and nutrition lookup.

by mrfentmenVery new↓ 43/wkUpdated today

Runs on your computer

Modrinth MCP

Search Minecraft mods on Modrinth, filter by loader/game version, browse versions.

by mrfentmenVery new↓ 89/wkUpdated today

Runs on your computer

Lichess MCP

MCP server for Lichess, chess puzzles by rating, player stats, top players.

by mrfentmenVery new↓ 52/wkUpdated today

Runs on your computer

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

MCP Sport — F1 Telemetry MCP 🏎️

An MCP (Model Context Protocol) server that exposes Formula 1 data from the OpenF1 API as tools for AI assistants (Claude Desktop, Cursor, MCP Inspector, etc.).

Full coverage: 18 data tools matching the 18 documented OpenF1 endpoints — sessions, meetings, drivers, results, laps, pit stops, stints, telemetry, weather, championships and more. Two MCP App views sit on top of that data: a drivers standings board and an animated race replay. Hosts that render MCP Apps show the HTML. Cursor and Claude Desktop do not: they return the same payload as JSON.

Stack

LayerTechnology
LanguagePython 3.13+
MCP frameworkFastMCP 4.x
ValidationPydantic v2
DataOpenF1 API (REST, free for historical data 2023+)
Project managementuv + pyproject.toml
Transportstdio

Installation

# Clone and install dependencies

git clone https://github.com/andrequeiroz2/mcp-sport.git mcp-sport cd mcp-sport uv sync

Usage

Run the server (stdio)
.venv/bin/python src/mcp_sport/server.py
MCP Inspector (web UI to test the tools)
npx @modelcontextprotocol/inspector@latest .venv/bin/python src/mcp_sport/server.py

In the Inspector UI: transport STDIO, command .venv/bin/python, args src/mcp_sport/server.py → Connect.

Claude Desktop / Cursor

Add to the client's MCP configuration:

{

"mcpServers": { "f1-telemetry": { "command": "/absolute/path/mcp-sport/.venv/bin/python", "args": ["/absolute/path/mcp-sport/src/mcp_sport/server.py"] } } }

The 18 data tools work in both clients. The views do not render there.

Views (MCP Apps)

get_drivers_championship_view and get_race_replay_view return interactive HTML. Cursor and Claude Desktop are incompatible with MCP Apps: they ignore the UI and show the JSON payload. The MCP Inspector also treats the result as text.

The views were validated in the official basic-host from modelcontextprotocol/ext-apps. The server must be HTTP, with CORS exposing the MCP session headers. Otherwise the browser cannot complete the Streamable HTTP handshake.

Terminal 1 — MCP server on port 8765:

uv run python -c "

import uvicorn from starlette.middleware import Middleware from starlette.middleware.cors import CORSMiddleware from mcp_sport.server import mcp

app = mcp.http_app(middleware=[Middleware( CORSMiddleware, allow_origins=['*'], allow_methods=['*'], allow_headers=['*'], expose_headers=['mcp-session-id', 'mcp-protocol-version'], )]) uvicorn.run(app, host='127.0.0.1', port=8765) "

Terminal 2 — basic-host (needs Node.js; npm start requires bun, so use tsx):

git clone --depth 1 https://github.com/modelcontextprotocol/ext-apps.git

cd ext-apps/examples/basic-host npm install npm run build SERVERS='["http://127.0.0.1:8765/mcp"]' npx tsx serve.ts

Open http://localhost:8080 (sandbox on :8081) and call get_drivers_championship_view or get_race_replay_view. After a change to the view HTML, hard-refresh the page (Ctrl+Shift+R) before running the tool again. The host caches the ui:// resource.

Available tools (18)

DomainToolDescription
Navigationget_sessionsSessions (practice, qualifying, sprint, race)
get_meetingsGrand Prix and testing weekends
Registryget_driversDrivers by session/meeting
Resultsget_session_resultsFinal classification of a session
get_starting_gridStarting grid
get_positionsPosition history throughout a session
Raceget_lapsLap times, sectors and speeds
get_pit_stopsPit stops
get_stintsStints and tyre compounds
get_intervalsReal-time gaps (leader and car ahead)
get_race_controlFlags, safety car, incidents
Contextget_weatherTrack weather (per-minute samples)
get_overtakesOvertakes
get_team_radioTeam radio excerpts (MP3)
Telemetryget_car_dataSpeed, RPM, gear, throttle, brake, DRS (~3.7 Hz)
get_locationApproximate car position on the circuit (~3.7 Hz)
Championshipsget_drivers_championshipDrivers standings (beta)
get_teams_championshipTeams standings (beta)
Example conversation with the AI

"How many points did Norris score in the last two races?"

The AI orchestrates: get_sessions(session_type="Race") to discover recent sessions → get_session_results(session_key=..., driver_number=4) on each one.

Project structure

src/mcp_sport/

├── server.py # Entrypoint: FastMCP instance + tool registration ├── exceptions.py # Domain exceptions ├── logging_config.py # Logging to stderr (stdout is the protocol channel) ├── clients/openf1.py # Single OpenF1 HTTP client ├── schemas/ # Pydantic: input (BaseInput) and output per endpoint ├── validators/ # Business validations per endpoint ├── services/ # Orchestration per endpoint ├── tools/ # MCP tools (thin layer) per endpoint └── apps/ # MCP App views (Custom HTML, ui:// resource) ├── championship_view.py # Drivers standings board └── race_replay_view.py # Animated race replay

Canonical documentation

DocumentContents
docs/Technical_Reference.mdStack, versions and official links (source of truth)
docs/Architectural_Design.mdImplementation patterns and procedure for new endpoints
docs/Logging_Strategy.mdLogging strategy (stderr + per-request telemetry)
tasks/History of planned and executed tasks

Configuration

VariableDefaultDescription
MCP_SPORT_LOG_LEVELINFOLog level on stderr (DEBUG, INFO, WARNING, ERROR)

Known limitations

  • Historical data from 2023 onwards; real-time data requires a paid OpenF1 subscription
  • session_result and starting_grid return HTTP 404 until official results are published
  • Telemetry (car_data, location) returns 18–24k samples per session/driver. Narrow the call with range filters such as speed_min and date_from/date_to
  • Championship endpoints are in beta on OpenF1

License

MIT. OpenF1 is an unofficial project, not associated in any way with the Formula 1 companies.

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