MCP server

Footballcharts

io.github.ddevetak/footballcharts-mcp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Tables, results, fixtures, goal timing, season projections: 93 football leagues incl. lower tiers

Working at last checkAbout 2,527 tokens per request once connected10 tools

Website Source

https://mcp.football-charts.com/mcp

At a glance

Type: MCP server
Publisher: Not verified
Status: Working at last check
Tools: 10
Token cost: About 2,527 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http footballcharts-mcp https://mcp.football-charts.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.footballcharts-mcp]
url = "https://mcp.football-charts.com/mcp"

Gemini CLI

{"mcpServers":{"footballcharts-mcp":{"httpUrl":"https://mcp.football-charts.com/mcp"}}}

Cursor

{"mcpServers":{"footballcharts-mcp":{"url":"https://mcp.football-charts.com/mcp"}}}

VS Code

{"servers":{"footballcharts-mcp":{"type":"http","url":"https://mcp.football-charts.com/mcp"}}}

Antigravity

agy mcp add footballcharts-mcp https://mcp.football-charts.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"footballcharts-mcp","server_url":"https://mcp.football-charts.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"footballcharts-mcp","server_url":"https://mcp.football-charts.com/mcp"}

Tools

about_football_charts ~133 tokens
Returns a summary of the football-charts.com source: the leagues and data it covers (93 leagues, including lower divisions), what it does not hold (live scores, player data, bookmaker odds), the league-key and season-string formats, how the model probabilities are produced, and the free-tier limits.
get_fixtures ~234 tokens
Returns the upcoming matches of one league, earliest first: kick-off date and time, teams, a match slug, and the model's calibrated probabilities (home and away win, over/under goal lines, both teams to score, half-time lines; the draw probability is 1 - home - away) with team attack and defence rat
get_goal_timing ~314 tokens
Returns goals per 15-minute period (0-15 to 90+) for every team in one league season, with each team's peak period or periods (ties are listed together), share of late goals and share of first-half goals, plus league totals and the league's most active period. team limits the result to teams whose n
get_league_table ~349 tokens
Returns the standings of one league season: one row per team with position, played, wins, draws, losses, goals, points and last-five form, plus expected points and a luck category (how far results run ahead of or behind the underlying performance). view='luck' orders the same rows by over- or under-
get_match ~191 tokens
Returns one match in full, identified by its slug ('country/league-slug/YYYY-MM-DD-home-vs-away', as listed in the slug field of upcoming fixtures): the complete model probability block (calibrated and raw), team ratings, first-goal-time histograms for both sides, recent form and, once played, the s
get_results ~351 tokens
Returns the finished matches of one league season in chronological order: date, teams, full-time and half-time score, first-goal minute and a goalless flag. team keeps the matches where either side's name contains the given text (case-insensitive); last=N keeps only the N most recent matches. Contai
get_season_projection ~242 tokens
Returns how one league's current season is projected to finish, from 10,000 Monte Carlo simulations refreshed daily: for each team the probabilities of winning the title, finishing in the top four and finishing in the bottom three, current points, mean final points, a 10th-90th percentile points ran
get_team ~358 tokens
Returns one team's season in one league: its table row (with expected points and luck category), the full match log (date, opponent, home or away, score, half-time score, first-goal minute, outcome), goals per 15-minute period, the first-goal distribution, and season averages of match statistics (sh
get_track_record ~221 tokens
Returns the public calibration record of the model: every probability published before kick-off and scored after the result, with count, hit rate, Brier score and reliability buckets (predicted probability against observed frequency), overall and by question type (match result, total goals over 2.5
list_leagues ~134 tokens
Returns every league covered (93 across 42 countries) with its league key, display name, country and the seasons available, newest first. Other tools take these league keys and season strings. Season format depends on the competition: '2026-2027' for winter-calendar leagues, '2026' for summer-calend

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