For the complete documentation index, see llms.txt. This page is also available as Markdown.

Example Builds

Concrete things you can build with AI and Sportmonks data. Each of these is achievable in an afternoon using any AI coding tool with the MCP server or a context block.

Live score dashboard

A real-time dashboard that polls for in-play matches and displays scores, match state, and elapsed minutes.

What you need:

  • GET /livescores/inplay?include=participants;scores;state;periods

  • A polling loop (every 30-60 seconds)

  • A simple UI framework (React, Vue, or plain HTML)

Ask your AI:

Build a live score dashboard that polls the Sportmonks inplay livescores endpoint every 30 seconds and renders a card for each match showing home team, away team, score, and current minute. Use React and Tailwind.

Pre-match briefing generator

Paste a fixture ID and get a formatted pre-match briefing with team info, recent form, and head-to-head history.

What you need:

  • GET /fixtures/{id}?include=participants

  • GET /fixtures/head-to-head/{team1}/{team2}?per_page=5&order=desc

  • GET /standings/live/leagues/{league_id}?include=participant;details

Ask your AI:

Build a pre-match briefing generator. Given a fixture ID, fetch the two teams, their last 5 head-to-head results, and their current league positions. Format everything as a clean markdown report.

Odds comparison table

A side-by-side odds comparison across bookmakers for a given fixture and market.

What you need:

  • GET /odds/pre-match/fixtures/{fixture_id}?include=bookmaker;market

  • Filter by market (e.g. 1X2) and sort by bookmaker

Ask your AI:

Build an odds comparison function that takes a fixture ID and returns a table showing the home win, draw, and away win odds from every available bookmaker, sorted by best home win odds.

Transfer tracker

A feed of the latest transfer activity across subscribed leagues, updated on demand.

What you need:

  • GET /transfers/latest?include=player;fromTeam;toTeam

  • Optional date range filtering

Ask your AI:

Build a transfer tracker that fetches the 20 most recent transfers and displays them as a feed with player name, from club, to club, and transfer date. Add a filter for specific leagues.

League season explorer

A tool that lets users pick a league, browse its historical seasons, and jump to standings or topscorers for any of them.

What you need:

  • GET /leagues/search/{query}

  • GET /leagues/{id}?include=seasons

  • GET /standings/seasons/{season_id}?include=participant;details

  • GET /topscorers/seasons/{season_id}?filters=seasonTopscorerTypes:208

Ask your AI:

Build a league explorer. The user types a league name, picks from the search results, then sees a list of all historical seasons. Clicking a season shows the final standings table and top 10 scorers.

Tips for building with AI

  • Start with search - ask the AI to use the search endpoint to find IDs before writing the rest of the code. It avoids hardcoded IDs that break.

  • Use the context block - paste the full context at the start of your session so the AI knows the endpoint shapes upfront.

  • Ask for real data first - before writing UI code, ask the AI to fetch a live sample response and print it. It avoids building against the wrong shape.

  • Install the MCP server - if you're in Claude or Cursor, the AI can fetch real data mid-conversation to test its own code as it writes it.

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