MCP server

Sportiq

io.github.Ninjabeam20/sportiq-mcp

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

MCP tools for FIFA World Cup 2026 football, Formula 1, and IPL cricket — sims, strategy, fantasy.

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

46

Token cost

About 13,510 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 CursorAdd to VS CodeVS 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 sportiq-mcp https://sportiq.utkarshgupta.org/mcp

Claude Desktop

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

Codex (config.toml)

[mcp_servers.sportiq-mcp]
url = "https://sportiq.utkarshgupta.org/mcp"

Gemini CLI

{"mcpServers":{"sportiq-mcp":{"httpUrl":"https://sportiq.utkarshgupta.org/mcp"}}}

Cursor

{"mcpServers":{"sportiq-mcp":{"url":"https://sportiq.utkarshgupta.org/mcp"}}}

VS Code

{"servers":{"sportiq-mcp":{"type":"http","url":"https://sportiq.utkarshgupta.org/mcp"}}}

Antigravity

agy mcp add sportiq-mcp https://sportiq.utkarshgupta.org/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":"sportiq-mcp","server_url":"https://sportiq.utkarshgupta.org/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"sportiq-mcp","server_url":"https://sportiq.utkarshgupta.org/mcp"}

Tools

• cricket_build_dream11_team ~645 tokens
  Recommend an optimal fantasy XI + captain + vice-captain for one fixture. Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name (e.g. ``MI``). Required if match_id is absent. team_b: Second team code/name (e.g. ``CSK``). Requi
• cricket_captain_recommendation ~474 tokens
  Return the top-3 captain candidates ranked by projected points. IPL venues only (pitch seed is IPL grounds). Test/international matches and unknown venues fail rather than inventing a ranking. Same-role players often tie: projections use default form 55 and default opposition 0.5, not per-player hi
• cricket_differential_picks ~531 tokens
  Suggest low-ownership picks with positive projected upside. Ownership is *estimated* — proxied by credit weight (lower-credit players tend to have lower ownership), not real ownership data. Flagged ``estimated: true`` in the response. Args: match_id: CricAPI match identifier; resolves team_a/t
• cricket_find_value_bets ~520 tokens
  Compare model probabilities against market-implied IPL odds. Requires THEODDS_KEY. NOTE: cricket has no calibrated team-strength model wired yet (unlike the football Elo/Poisson path), so this tool currently returns an EMPTY ``value_bets`` list — scoring an edge against a neutral 50/50 prior would
• cricket_get_live_matches ~115 tokens
  Return all currently live cricket matches across all series. Returns: data.matches: list of live match objects (team names, score, status). meta.source: which adapter served the response. meta.is_stale: true if data is from stale cache.
• cricket_get_live_odds ~436 tokens
  Return live market head-to-head odds for upcoming/live IPL matches. IPL only (~March-May). An empty ``events`` list outside that window is a successful empty market, not an outage. Not international/Test/other T20 leagues. For World Cup 2026 football odds use ``football_get_odds``. Sourced from Th
• cricket_get_pitch_report ~163 tokens
  Summarise pitch characteristics for a venue. Args: venue: Venue key (e.g. ``wankhede``), official name, or city. Returns: data: {batting_friendly 0..1, expected_first_inn, recommendation, venue, pitch_type}. meta.source: which adapter served the venue record.
• cricket_get_points_table ~162 tokens
  Return the points table / standings for a cricket series. Args: series_id: The series identifier (e.g. IPL 2026 series ID from CricAPI). Returns: data: points table rows with team, P, W, L, NRR, Points. meta.source: adapter that served the data.
• cricket_get_schedule ~348 tokens
  Return the upcoming match schedule, optionally filtered by series. Args: series_id: Optional. Filter to a specific series. If omitted, returns all upcoming fixtures across all active series. limit: Max matches to return, 1..200 (default 50). offset: Number of matches to s
• cricket_get_scorecard ~157 tokens
  Return the full scorecard for a specific match. Args: match_id: The match identifier (e.g. from cricket_get_live_matches). Returns: data: full scorecard with innings, partnerships, bowling figures. meta.source: adapter that served the data.
• cricket_get_squad ~264 tokens
  Return the squad roster for a cricket team, optionally for a specific series. Args: team: Team code or name (e.g. "MI", "CSK", "IND", "AUS"). series_id: Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data. Returns: data.p
• cricket_head_to_head ~277 tokens
  Compare two cricket teams head-to-head using squad form and player stats. Args: team_a: First team code or name (e.g. "MI", "India"). team_b: Second team code or name (e.g. "CSK", "Australia"). Returns: data: {team_a, team_b, team_a_edge_count, team_b_edge_count, key_players
• cricket_player_form_index ~197 tokens
  Report a 0-100 form score for a player using the player_stats chain. Args: player_id: Upstream player identifier (CricAPI/Cricbuzz id). Returns: data.form_score: 0..100 indicator. data.trend: "rising" / "stable" / "falling". data.samples: how many recent innings were available.
• cricket_player_matchup ~246 tokens
  Analyse the head-to-head matchup between two cricket players based on role and career stats. Args: player_a: CricAPI player ID for the first player (names are not resolved). player_b: CricAPI player ID for the second player (names are not resolved). Returns: data: {matchup_type, edge_h
• cross_sport_build_accumulator ~201 tokens
  Model the joint probability of multiple outcomes across football and cricket. Args: legs: Total legs across both sports (2-8). Default 3. min_edge: Minimum edge per leg. Default 0.05. Returns: data: same shape as football_build_accumulator, with sport field per leg. meta.estimated:
• f1_get_drivers ~140 tokens
  Return driver list for a specific F1 session. Args: session_key: OpenF1 session identifier. Returns: data.drivers: list of driver objects with driver_number, full_name, team. meta.source: adapter that served the data.
• f1_get_lap_times ~378 tokens
  Return lap times for a driver in a specific F1 session. Args: session_key: OpenF1 session identifier. driver_number: Driver's race number (driver numbers change by season; use f1_get_drivers). limit: Max laps to return, 1..200 (default 100 — covers most full races). offset: Number o
• f1_get_race_results ~223 tokens
  Return the final classification for one F1 race, keyed by year and round. Args: year: Championship year (e.g. 2025). round: Round number within the season (1-based; e.g. 1 for the opener). Returns: data.results: Ergast/Jolpica RaceTable payload — finishing order, times, grid po
• f1_get_sessions ~444 tokens
  Return F1 sessions for a given year, optionally filtered by country or session name. Args: year: Championship year (e.g. 2026). country: Optional country name to filter (e.g. "Netherlands"). session_name: Optional session name to keep, case-insensitive (e.g. "Race", "Qualifying"
• f1_get_standings ~274 tokens
  Return F1 driver and constructor championship standings for a year. Use this for "who is leading / who will win the F1 championship this year". For title probabilities use f1_simulate_championship. This is not a cricket or football tool. Args: year: Championship year (e.g. 2026). Returns:
• f1_get_weather ~140 tokens
  Return weather data for a specific F1 session. Args: session_key: OpenF1 session identifier. Returns: data.weather: list of weather snapshots with temperature, rainfall, wind. meta.source: adapter that served the data.
• f1_head_to_head_pace ~262 tokens
  Compare lap-time pace distribution between two drivers in a session. Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number. Returns: data: {driver_a_avg_s, driver_b_avg_s, delta_s, faster_driver}. The "avg"
• f1_predict_pit_strategy ~627 tokens
  Predict the optimal pit-stop strategy for a driver in an F1 race session. Args: session_key: OpenF1 session identifier for a recorded race. driver_number: Driver's race number (driver numbers change by season; use f1_get_drivers). current_lap: Current lap to project from (default 1 = fu
• f1_qualifying_analysis ~212 tokens
  Analyse a qualifying session: best lap per driver, gap to pole, projected grid. Args: session_key: OpenF1 session identifier for a Qualifying session. Returns: data.grid: [{position, driver_number, full_name, team_name, best_lap_gap_s}]. data.pole_time_s: pole lap duration in seconds.
• f1_race_pace_compare ~239 tokens
  Compare race-pace and tyre degradation between two F1 drivers in a session. Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number. Returns: data: {by_compound, overall_faster, compounds_compared}. meta.estimate
• f1_simulate_championship ~458 tokens
  Estimate each driver's F1 drivers' title probability for the rest of a season. Monte Carlo over the remaining races and sprints (Plackett-Luce finishing orders weighted by each driver's season points rate), plus exact elimination maths: a driver who cannot catch the leader's current points even by
• f1_tyre_degradation ~260 tokens
  Fit a tyre degradation model for a driver + compound in a session. Args: session_key: OpenF1 session identifier. driver_number: Driver's race number. compound: Tyre compound (SOFT, MEDIUM, HARD, INTER, WET; "INTERMEDIATE" = INTER). Returns: data: {intercept, slope, residual_std, sa
• f1_undercut_window ~351 tokens
  Estimate whether an undercut is viable for the attacker against the target. Args: session_key: OpenF1 session identifier. attacker_number: Attacking driver's race number. target_number: Target driver's race number. current_lap: Current lap number in the race. Returns: data: {la
• f1_weather_strategy_impact ~149 tokens
  Analyse weather data and recommend compound or pit-window adjustments. Args: session_key: OpenF1 session identifier. Returns: data: {has_rain, avg_track_temp_c, compound_recommendation, recommendation}. meta.estimated: true.
• football_build_accumulator ~257 tokens
  Model the joint probability of several match outcomes from the top model-vs-market gaps. Calls ``football_find_value_bets`` internally to fetch live odds, then selects the strongest legs and combines them under the joint-probability model. Args: legs: Number of legs (2-8). Default 3. min_e
• football_find_value_bets ~527 tokens
  Surface the largest gaps between the model's win probability and the market. De-vigs each market's 1X2 decimal odds (removes the margin so implied probabilities sum to 1) and compares them to this server's own match-outcome probabilities — the same Elo/Poisson path ``football_match_predictor`` uses
• football_form_trends ~188 tokens
  Return rolling form, goal record, and xG trend for a football team. Args: team: FIFA code (e.g. ARG) or name (e.g. "Argentina", "South Korea"). Returns: data: {form_string, wins, draws, losses, goals_scored, goals_conceded, xg_for, xg_against, recent_trend, matches_analysed}.
• football_get_fixtures ~241 tokens
  Return World Cup 2026 fixtures (live providers, else the group schedule). Args: limit: Max fixtures to return, 1..200 (default 50). offset: Number of fixtures to skip for paging (default 0). Returns: data.fixtures: page of {home, away, date/group, status, home_goals, away_goals}. d
• football_get_groups ~130 tokens
  Return the FIFA World Cup 2026 group draw and advancement format. Returns: data.groups: {group_letter: [4 team codes]} for all 12 groups. data.format: 48-team / 12-group / top-2 + 8-best-thirds rule. data.teams: team-code -> {name, fifa_code} metadata. meta.source: adapter that serv
• football_get_match_stats ~210 tokens
  Return a team's aggregate World Cup tournament statistics. Network-only enrichment: requires a configured API-Football (or football-data.org) key. There is no offline static fallback, so without a key the call returns a clean ALL_SOURCES_FAILED envelope. Args: team: API-Football numeric team i
• football_get_odds ~306 tokens
  Return market head-to-head odds for any World Cup 2026 events the odds feed still lists. Sourced from The Odds API (requires THEODDS_KEY). Without a key the call returns a clean ALL_SOURCES_FAILED envelope rather than crashing. The tournament ended with the 2026-07-19 final, so the feed is normally
• football_get_squad ~234 tokens
  Return a national team's World Cup squad. Args: team: Team code or name (e.g. "ARG"). Live rosters need an API-Football key; without one the static seed serves an empty-but-valid squad (no rosters are bundled). Returns: data.squad: list of {name, number, position, age} (emp
• football_get_standings ~222 tokens
  Return current World Cup 2026 group standings. Args: limit: Max standing rows to return, 1..200 (default 50). offset: Number of rows to skip for paging (default 0). Returns: data.standings: page of {rank, team, group, points, played, goals_diff}. data.pagination: {total, count, off
• football_get_top_scorers ~87 tokens
  Return the World Cup 2026 top scorers. Returns: data.scorers: list of {name, team, goals, assists}. meta.source: adapter that served the data.
• football_knockout_path ~254 tokens
  Round-by-round survival probabilities for one team in the full sim. Args: team: FIFA code or name (e.g. "FRA" or "France"). iterations: Number of tournament simulations (clamped to 100..20000). seed: Optional RNG seed. Returns: data: {team, reach_r32, reach_r16, reach_qf, reach_sf,
• football_match_predictor ~260 tokens
  Predict a single match: most likely scoreline + outcome probabilities. Args: home_team: First team, FIFA code or name (e.g. "ESP" or "Spain"). away_team: Second team, FIFA code or name. neutral: True for a neutral venue (World Cup default). Returns: data: {most_likely_score, home_w
• football_simulate_bracket ~389 tokens
  Monte Carlo the full World Cup 2026 — per-team round + title probabilities. Simulates all 12 groups, advances the top 2 + 8 best third-placed teams to a 32-team knockout, and plays it to a champion, ``iterations`` times. Args: iterations: Number of tournament simulations (clamped to 100..20000
• football_simulate_group ~252 tokens
  Monte Carlo one group within the full 12-group qualification context. Args: group: Group letter A-L (also accepts "Group A"). iterations: Number of simulations (clamped to 100..20000). Returns: data.teams: Per-team position probabilities, p_auto_advance, p_best_third_advance, t
• football_what_if ~682 tokens
  Replay World Cup 2026 from a chosen stage, optionally flipping one real knockout result. Keeps every real result played before ``replay_from`` and Monte-Carlos the rest of the tournament from there. ``flip_winner_of`` makes one decided knockout tie in the kept results go the other way first. Args:
• football_xg_model ~286 tokens
  Estimate a match's expected goals and win/draw/loss probabilities. Args: home_team: First team, FIFA code or name (e.g. "ARG" or "Argentina"). away_team: Second team, FIFA code or name (e.g. "BRA" or "Brazil"). neutral: True for a neutral venue (no home advantage). World Cup default. R
• sportiq_health ~92 tokens
  Report cache backend, per-adapter healthcheck, and quota status. Returns: HealthReport-shaped dict with `cache_backend`, `cache_ok`, `adapters` (per-source ok/detail), and `quotas`.

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