Vibe Coding
Describe what you want to build. Let the AI write the code. Ship it.
This page gives you everything you need to build with the Sportmonks Football API using any AI coding tool - the right context, and ready-to-run prompts for common football data builds.
Give your AI the right context
For the AI to write accurate Sportmonks integration code, it needs to know the API. Pick one of the options below and give it to your AI tool before you start building.
llms.txt - full documentation index
Sportmonks publishes a machine-readable index of all its APIs at:
https://docs.sportmonks.com/llms.txtPaste this URL into Claude, ChatGPT, or Cursor and ask it to fetch the content. This gives the AI a broad picture of every Sportmonks API before you start.
Context block - Football API quick reference
For faster setup, paste this directly into your session:
Sportmonks Football API v3
Base URL: https://api.sportmonks.com/v3/football
Auth: ?api_token=YOUR_TOKEN (query parameter on every request)
Key endpoints:
GET /players/search/{query}
GET /teams/search/{query}
GET /leagues/search/{query}
GET /fixtures/{id}
GET /fixtures/date/{date}
GET /livescores/inplay
GET /squads/teams/{team_id}
GET /standings/live/leagues/{league_id}
GET /topscorers/seasons/{season_id}
Includes: semicolon-separated (e.g. include=participants;scores;league;state)
Filters: key:value syntax (e.g. filters=fixtureLeagues:8)
Pagination: ?page=1&per_page=25
Docs: https://docs.sportmonks.com/footballFor a more complete reference with all endpoints, includes, filters, and common IDs, see the full context block.
Cursor Rules or Copilot instructions
If you use Cursor or GitHub Copilot, you can make the API knowledge persistent across your whole project - no need to paste context every session.
Cursor Rules
GitHub Copilot instructions
Build prompts
Each prompt below is ready to paste into your AI tool. They're written to produce working code against the real API - not mock data.
Note: These prompts guide the AI to write code that calls the Sportmonks API directly from your application. This is different from the MCP server, which is a companion tool for exploring data conversationally - not for generating application code.
Live score dashboard
A real-time dashboard that polls for in-play matches and updates every 30 seconds.
Pre-match briefing card
Given a fixture ID, generate a briefing with team details, head-to-head history, and current league positions.
Squad viewer
Search for any team by name and browse their current squad grouped by position.
Top scorers leaderboard
A season leaderboard for goals, assists, or yellow cards with a toggle to switch between them.
Discord match alert bot
A Node.js script that posts to Discord when a match kicks off or ends.
Tips
Fetch real data first. Every prompt above asks the AI to print the raw API response before building UI. This prevents the AI from guessing the wrong response shape.
Search before you hardcode. Ask the AI to use the search endpoints to resolve names to IDs rather than hardcoding them.
Iterate in plain English. Once there's a working base, describe changes conversationally - "group players by position", "add a loading skeleton", "make the cards darker".
Use the full context block for complex builds. For anything beyond a single endpoint, paste the full context so the AI knows the complete include and filter syntax upfront.
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