/acquire

name: nutmeg-acquire
description: "Fetch, scrape, or download football data from any source. Also handles API key setup and credential management. Use when the user wants to get data from StatsBomb, Opta, FBref, Understat, SportMonks, Wyscout, Kaggle, or any football data source. Also use when they ask about API keys, authentication, setting up access to a provider, or what data is available free vs paid."
argument-hint: "[what data to get]"
allowed-tools: ["Read", "Write", "Bash", "Glob", "Grep", "Agent", "AskUserQuestion", "mcp__football-docs__search_docs", "mcp__football-docs__resolve_entity"]

Acquire

Help the user get football data from any source into their local environment. This includes setting up credentials for providers that require them.

Accuracy

Read and follow `docs/accuracy-guardrail.md` before answering any question about provider-specific facts (IDs, endpoints, schemas, coordinates, rate limits). Always use `search_docs` — never guess from training data.

First: check profile

Read `.nutmeg.user.md`. If it doesn't exist, tell the user to run `/nutmeg` first. Use their profile to determine preferred language and available providers.

Credentials

If the user needs to set up API keys or asks "what can I access for free?", handle it here.

**Key management rules:**

• Keys go in `.env` (gitignored), environment variables, or `.nutmeg.credentials.local` (gitignored)
• Never commit keys to git. Verify `.gitignore` includes `.env` and `*.local`
• Test the key works with a minimal API call
• Never print or log API keys

**Provider access reference:**

| Source | Access | Free? | Env var | |--------|--------|-------|---------| | StatsBomb open data | GitHub / statsbombpy | Yes | — | | FBref | Web scraping (soccerdata) | Yes | — | | Understat | Web scraping (soccerdata) | Yes | — | | ClubElo | HTTP API | Yes | — | | football-data.co.uk | CSV download | Yes | — | | Transfermarkt | Web scraping | Yes (fragile) | — | | SportMonks | REST API | Free tier | `SPORTMONKS_API_TOKEN` | | Football-data.org | REST API | Free tier | `FOOTBALL_DATA_API_KEY` | | FPL | Unofficial API | Yes | — | | Opta/Perform | Feed | No | `OPTA_FEED_TOKEN` | | StatsBomb API | REST API | No | `STATSBOMB_API_KEY`, `STATSBOMB_API_PASSWORD` | | Wyscout | REST API | No | `WYSCOUT_API_KEY` | | Kaggle | Download | Yes | — | | GitHub datasets | Download | Yes | — |

Decision tree

When the user asks for data, determine the best source:

1. What data do they need?

| Need | Best free source | Best paid source | |------|-----------------|-----------------| | Match events (pass-by-pass) | StatsBomb open data | Opta, StatsBomb API, Wyscout | | Season stats (aggregates) | FBref | SportMonks | | xG / shot data | Understat, StatsBomb open | Opta (matchexpectedgoals), StatsBomb API | | Tracking data (player positions) | None free | Second Spectrum, SkillCorner, Tracab | | Historical results | football-data.co.uk | SportMonks | | Elo ratings | ClubElo (free API) | - | | Player valuations | Transfermarkt (scraping) | - | | Cross-provider entity IDs | Reep Register (free CSV + API) | - |

2. Write acquisition code

Adapt to the user's language preference from `.nutmeg.user.md`.

**Python patterns:**

# StatsBomb open data
from statsbombpy import sb
events = sb.events(match_id=3788741)

# FBref via soccerdata
import soccerdata as sd
fbref = sd.FBref('ENG-Premier League', '2024')
stats = fbref.read_team_season_stats()

# Understat via soccerdata
understat = sd.Understat('ENG-Premier League', '2024')
shots = understat.read_shot_events()

**R patterns:**

# StatsBomb
library(StatsBombR)
events <- get.matchFree(Matches) %>% allclean()

# FBref
library(worldfootballR)
stats <- fb_season_team_stats("ENG", "M", 2024, "standard")

**JavaScript/TypeScript:**

// StatsBomb open data (direct from GitHub)
const resp = await fetch('https://raw.githubusercontent.com/statsbomb/open-data/master/data/events/{match_id}.json');
const events = await resp.json();

3. Data validation

After acquiring data, always:

• Check row/event counts are sensible (PL match should have ~1500-2000 events)
• Verify key fields are present (coordinates, player IDs, timestamps)
• Check for missing data (some providers have gaps for certain competitions)
• Warn about coordinate system differences if combining sources

Entity ID resolution

When joining data from different providers (e.g. FBref stats with Transfermarkt valuations), use the **Reep Register** to map entity IDs across providers.

Use the `resolve_entity` MCP tool (from football-docs) to look up any player, team, or coach:

resolve_entity(name: "Cole Palmer")                          # search by name
resolve_entity(provider: "transfermarkt", id: "568177")      # resolve provider ID
resolve_entity(qid: "Q99760796")                             # Wikidata QID lookup

For entity-resolution tasks that go beyond a one-off lookup, read `docs/entity-resolution-routing.md`:

• check provider identity surfaces and quirks in `football-docs` before writing

matching logic;

• use `reep-scripts` for reusable public matching/candidate code and schemas;
• mention the matching logic pack only if the user has access to that private

partner material;

• do not define new Reep doctrine inside Nutmeg.

Returns IDs for Transfermarkt, FBref, Sofascore, Opta, Soccerway, 11v11, and more.

For bulk/offline use, download the CSV register:

• GitHub: https://github.com/withqwerty/reep
• `data/people.csv` (430K players+coaches), `data/teams.csv` (45K teams)

Self-discovery

If the user asks for data from an unfamiliar source: 1. Search the football-docs index: `search_docs(query="[source name]")` 2. If not found, search the web for "[source] football data API" or "[source] football dataset" 3. Evaluate: is it free? What format? What coverage? Any rate limits? 4. Guide the user through access

Caching

Always recommend caching fetched data loc