Open Food Facts MCP
MCP server for Open Food Facts product, ingredient, allergen, and nutrition lookup.
Description from the maker (original English)
MCP server that exposes Formula 1 data from the OpenF1 API.
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We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
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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-sportPaste 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.
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.
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Runs on your computer; the tool list appears in your AI app after you install it.
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
The registry name was verified with the GitHub account andrequeiroz2, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
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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).
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 2 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Open Food Facts MCP, Modrinth MCP, Lichess MCP
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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP server for Open Food Facts product, ingredient, allergen, and nutrition lookup.
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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.
| Layer | Technology |
|---|---|
| Language | Python 3.13+ |
| MCP framework | FastMCP 4.x |
| Validation | Pydantic v2 |
| Data | OpenF1 API (REST, free for historical data 2023+) |
| Project management | uv + pyproject.toml |
| Transport | stdio |
# Clone and install dependencies
git clone https://github.com/andrequeiroz2/mcp-sport.git mcp-sport cd mcp-sport uv sync
.venv/bin/python src/mcp_sport/server.py
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.
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.
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.
| Domain | Tool | Description |
|---|---|---|
| Navigation | get_sessions | Sessions (practice, qualifying, sprint, race) |
get_meetings | Grand Prix and testing weekends | |
| Registry | get_drivers | Drivers by session/meeting |
| Results | get_session_results | Final classification of a session |
get_starting_grid | Starting grid | |
get_positions | Position history throughout a session | |
| Race | get_laps | Lap times, sectors and speeds |
get_pit_stops | Pit stops | |
get_stints | Stints and tyre compounds | |
get_intervals | Real-time gaps (leader and car ahead) | |
get_race_control | Flags, safety car, incidents | |
| Context | get_weather | Track weather (per-minute samples) |
get_overtakes | Overtakes | |
get_team_radio | Team radio excerpts (MP3) | |
| Telemetry | get_car_data | Speed, RPM, gear, throttle, brake, DRS (~3.7 Hz) |
get_location | Approximate car position on the circuit (~3.7 Hz) | |
| Championships | get_drivers_championship | Drivers standings (beta) |
get_teams_championship | Teams standings (beta) |
"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.
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
| Document | Contents |
|---|---|
docs/Technical_Reference.md | Stack, versions and official links (source of truth) |
docs/Architectural_Design.md | Implementation patterns and procedure for new endpoints |
docs/Logging_Strategy.md | Logging strategy (stderr + per-request telemetry) |
tasks/ | History of planned and executed tasks |
| Variable | Default | Description |
|---|---|---|
MCP_SPORT_LOG_LEVEL | INFO | Log level on stderr (DEBUG, INFO, WARNING, ERROR) |
session_result and starting_grid return HTTP 404 until official results are publishedcar_data, location) returns 18–24k samples per session/driver. Narrow the call with range filters such as speed_min and date_from/date_toMIT. OpenF1 is an unofficial project, not associated in any way with the Formula 1 companies.