SportIQ MCP
An open-source MCP server providing over 25 AI-callable tools for sports analysis, including Dream11 team building for IPL, F1 pit strategy modeling, and FIFA World Cup 2026 bracket simulation. It supports multiple data sources with keyless fallbacks and works with Claude, Cursor, and Zed via hosted or local installation.
Endpoint and access
No sign-in to connect and list tools. Individual tools may still need a vendor account, credentials, or permissions; check the documented prerequisites.
https://sportiq.utkarshgupta.org/mcpWhat its tools do
46 tools, grouped by the hints the server advertises. Hints are self-declared, not verified behavior.
Read-only (46)
sportiq_healthfootball_get_groupsfootball_get_fixturesfootball_get_standingsfootball_get_squadfootball_get_match_statsfootball_get_top_scorersfootball_get_oddsfootball_xg_modelfootball_match_predictorfootball_simulate_groupfootball_simulate_bracketfootball_knockout_pathfootball_what_iffootball_find_value_betsfootball_form_trendsfootball_build_accumulatorf1_get_sessionsf1_get_driversf1_get_lap_timesf1_get_standingsf1_get_race_resultsf1_get_weatherf1_tyre_degradationf1_undercut_windowf1_head_to_head_pacef1_weather_strategy_impactf1_predict_pit_strategyf1_simulate_championshipf1_qualifying_analysisf1_race_pace_comparecricket_get_live_matchescricket_get_scorecardcricket_get_points_tablecricket_get_schedulecricket_get_squadcricket_get_live_oddscricket_build_dream11_teamcricket_captain_recommendationcricket_differential_pickscricket_player_form_indexcricket_get_pitch_reportcricket_find_value_betscricket_head_to_headcricket_player_matchupcross_sport_build_accumulator
Checks and signals
Each line is separate evidence, not a trust score.
- Passed Endpoint answered the live check ·
- Availability 1 of 1 checks passed in 30 days
What you can do with it
- Build Dream11 fantasy cricket teams with captain recommendations
- Analyze F1 race pace and predict pit strategy
- Simulate FIFA World Cup 2026 brackets using Monte Carlo methods
- Retrieve live cricket scores and football standings
Source and maintenance
- Maintenance checked
Protocol and capabilities
- Server
- sportiq 0.4.0
- MCP protocol negotiated in check
- 2025-11-25
- Tools
- 46 advertised
- Resources
- 7 advertised
- Resource templates
- 0 advertised
- Prompts
- 10 advertised
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46 advertised
sportiq_health
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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}football_get_groups
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 served the data.
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}football_get_fixtures
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}. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data (static_seed = group schedule only).
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}football_get_standings
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, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
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}football_get_squad
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} (empty from the seed). meta.source: adapter that served the data. meta.note: present when the seed served it — how to get live squads.
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}football_get_match_stats
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 id (not a country code). Returns: data.team_stats: {team, played, wins, goals_for, goals_against}. meta.source: adapter that served the data.
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}football_get_top_scorers
Return the World Cup 2026 top scorers. Returns: data.scorers: list of {name, team, goals, assists}. meta.source: adapter that served the data.
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}football_get_odds
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 empty. Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every WC event. Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, draw, away, last_update}]} with decimal 1X2 prices per bookmaker. meta.source: adapter that served the data (theodds / cache:stale).
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}football_xg_model
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. Returns: data: {expected_home_goals, expected_away_goals, home_win, draw, away_win}. meta.estimated: true.
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"title": "Away Team",
"description": "Second team, FIFA code or name (e.g. \"BRA\" or \"Brazil\")."
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}football_match_predictor
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_win, draw, away_win, predicted_winner}. meta.estimated: true.
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"type": "string",
"title": "Away Team",
"description": "Second team, FIFA code or name."
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"type": "string",
"title": "Home Team",
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}football_simulate_group
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, truthful combined p_advance, and avg_points. data.iterations: iterations actually run. meta.estimated: true. meta.conditioned_matches: completed matches locked in.
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}football_simulate_bracket
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; ~10000 gives stable ±2% probabilities). seed: Optional RNG seed for reproducible output. Returns: data.teams: {code: {reach_r32, reach_r16, reach_qf, reach_sf, reach_final, win}} sorted by win probability descending. data.champion: most likely winner. data.iterations: iterations run. meta.estimated: true. meta.conditioned_matches: completed matches locked in (played group results fixed, decided knockout ties locked). Example: football_simulate_bracket() football_simulate_bracket(iterations=20000, seed=42)
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}football_knockout_path
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, reach_final, win}. meta.estimated: true.
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}football_what_if
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: replay_from: Stage to re-simulate from: "group" (whole tournament, no real results kept), "r32", "r16", "qf", "sf" or "final" (keeps every result before the final and simulates only the final). flip_winner_of: Optional tie to flip, as two FIFA codes or names joined by a hyphen in any order (e.g. "ESP-FRA"). It must be a decided knockout tie before ``replay_from``. iterations: Number of tournament simulations (clamped to 100..20000). seed: Optional RNG seed for reproducible output. Returns: data.teams: {code: {reach_r32, reach_r16, reach_qf, reach_sf, reach_final, win}} sorted by win probability descending. data.champion: most likely winner. data.iterations: iterations run. meta.mode: "what_if". meta.replay_from: the stage. meta.flipped: {tie: [code_a, code_b], winner: code} or null. meta.estimated: true. Example: football_what_if(replay_from="qf") football_what_if(replay_from="final", flip_winner_of="ESP-FRA", seed=7)
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"title": "Replay From",
"default": "group",
"description": "Stage to re-simulate from: \"group\" (whole tournament, no real results kept), \"r32\", \"r16\", \"qf\", \"sf\" or \"final\" (keeps every result before the final and simulates only the final)."
},
"flip_winner_of": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Flip Winner Of",
"default": null,
"description": "Optional tie to flip, as two FIFA codes or names joined by a hyphen in any order (e.g. \"ESP-FRA\"). It must be a decided knockout tie before ``replay_from``."
}
}
}{
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}
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"idempotentHint": true,
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}football_find_value_bets
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. Flags outcomes where the model beats the de-vigged market by ``min_edge`` **and** the offered price has positive expected value, with the edge, the expected value and the model's fair odds. Events that have already started are skipped. Args: team: Optional FIFA code or team name to filter events (matched against both sides; an unrecognised value falls back to a case-insensitive substring match). Omit to scan every WC 2026 odds event. min_edge: Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05 (5 percentage points). Returns: data.value_bets: list of {event_id, home, away, outcome, model_prob, fair_odds, market_odds, edge, ev_pct, bookmaker, price_updated_at}, sorted by edge descending. data.events_analysed: not-yet-started events with both teams in the draw. meta.estimated: true. meta.is_stale reflects the odds freshness.
{
"type": "object",
"title": "football_find_value_betsArguments",
"properties": {
"team": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team",
"default": null,
"description": "Optional FIFA code or team name to filter events (matched against both sides; an unrecognised value falls back to a case-insensitive substring match). Omit to scan every WC 2026 odds event."
},
"min_edge": {
"type": "number",
"title": "Min Edge",
"default": 0.05,
"description": "Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05 (5 percentage points)."
}
}
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}football_form_trends
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}. meta.estimated: true — derived from available fixture data.
{
"type": "object",
"title": "football_form_trendsArguments",
"required": [
"team"
],
"properties": {
"team": {
"type": "string",
"title": "Team",
"description": "FIFA code (e.g. ARG) or name (e.g. \"Argentina\", \"South Korea\")."
}
}
}{
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}
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"idempotentHint": true,
"destructiveHint": false
}football_build_accumulator
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_edge: Minimum edge threshold per leg. Default 0.05. Returns: data: {legs, legs_used, combined_odds, combined_model_prob, combined_edge, risk_flag, independence_warning}. meta.estimated: true.
{
"type": "object",
"title": "football_build_accumulatorArguments",
"properties": {
"legs": {
"type": "integer",
"title": "Legs",
"default": 3,
"description": "Number of legs (2-8). Default 3."
},
"min_edge": {
"type": "number",
"title": "Min Edge",
"default": 0.05,
"description": "Minimum edge threshold per leg. Default 0.05."
}
}
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}{
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"openWorldHint": true,
"idempotentHint": true,
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}f1_get_sessions
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", "Sprint"). latest_completed: When true, return only the single session that ended most recently (greatest date_end before now, UTC) — e.g. with session_name="Race", the latest finished Grand Prix. Returns: data.sessions: list of session objects with session_key, session_type, date. meta.source: adapter that served the data.
{
"type": "object",
"title": "f1_get_sessionsArguments",
"required": [
"year"
],
"properties": {
"year": {
"type": "integer",
"title": "Year",
"description": "Championship year (e.g. 2026)."
},
"country": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Country",
"default": null,
"description": "Optional country name to filter (e.g. \"Netherlands\")."
},
"session_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Session Name",
"default": null,
"description": "Optional session name to keep, case-insensitive (e.g. \"Race\", \"Qualifying\", \"Sprint\")."
},
"latest_completed": {
"type": "boolean",
"title": "Latest Completed",
"default": false,
"description": "When true, return only the single session that ended most recently (greatest date_end before now, UTC) — e.g. with session_name=\"Race\", the latest finished Grand Prix."
}
}
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}f1_get_drivers
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.
{
"type": "object",
"title": "f1_get_driversArguments",
"required": [
"session_key"
],
"properties": {
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
}
}
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}
}{
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}f1_get_lap_times
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 of laps to skip for paging (default 0). Returns: data.laps: page of lap objects with lap_number and lap_duration. OpenF1 does not put compound/tyre_life here — those live on the stints endpoint. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
{
"type": "object",
"title": "f1_get_lap_timesArguments",
"required": [
"session_key",
"driver_number"
],
"properties": {
"limit": {
"type": "integer",
"title": "Limit",
"default": 100,
"description": "Max laps to return, 1..200 (default 100 — covers most full races)."
},
"offset": {
"type": "integer",
"title": "Offset",
"default": 0,
"description": "Number of laps to skip for paging (default 0)."
},
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
},
"driver_number": {
"type": "integer",
"title": "Driver Number",
"description": "Driver's race number (driver numbers change by season; use f1_get_drivers)."
}
}
}{
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}
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"readOnlyHint": true,
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"idempotentHint": true,
"destructiveHint": false
}f1_get_standings
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: data.season / data.round: season and the last round counted. data.drivers: [{position, driver_number, code, name, team, points, wins}] (points are season totals). data.constructors: [{position, team, points, wins}]. Same shape from every source (Jolpica primary; the FastF1 fallback sums per-race points and omits sprint points). meta.source: adapter that served the data.
{
"type": "object",
"title": "f1_get_standingsArguments",
"required": [
"year"
],
"properties": {
"year": {
"type": "integer",
"title": "Year",
"description": "Championship year (e.g. 2026)."
}
}
}{
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}
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}f1_get_race_results
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 positions, points, and fastest laps for the race. meta.source: adapter that served the data.
{
"type": "object",
"title": "f1_get_race_resultsArguments",
"required": [
"year",
"round"
],
"properties": {
"year": {
"type": "integer",
"title": "Year",
"description": "Championship year (e.g. 2025)."
},
"round": {
"type": "integer",
"title": "Round",
"description": "Round number within the season (1-based; e.g. 1 for the opener)."
}
}
}{
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}
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"idempotentHint": true,
"destructiveHint": false
}f1_get_weather
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.
{
"type": "object",
"title": "f1_get_weatherArguments",
"required": [
"session_key"
],
"properties": {
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
}
}
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}f1_tyre_degradation
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, sample_count}. meta.estimated: true — model output, not telemetry oracle.
{
"type": "object",
"title": "f1_tyre_degradationArguments",
"required": [
"session_key",
"driver_number",
"compound"
],
"properties": {
"compound": {
"type": "string",
"title": "Compound",
"description": "Tyre compound (SOFT, MEDIUM, HARD, INTER, WET; \"INTERMEDIATE\" = INTER)."
},
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
},
"driver_number": {
"type": "integer",
"title": "Driver Number",
"description": "Driver's race number."
}
}
}{
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}f1_undercut_window
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: {laps_to_clear, viable, marginal}. meta.gap_to_target_s: attacker-minus-target race time at current_lap. meta.gap_source: "cumulative_lap_time", or "default" (assumed 2.0 s, also in meta.assumed_gap_s) when either driver lacks a full lap history. meta.estimated: true.
{
"type": "object",
"title": "f1_undercut_windowArguments",
"required": [
"session_key",
"attacker_number",
"target_number",
"current_lap"
],
"properties": {
"current_lap": {
"type": "integer",
"title": "Current Lap",
"description": "Current lap number in the race."
},
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
},
"target_number": {
"type": "integer",
"title": "Target Number",
"description": "Target driver's race number."
},
"attacker_number": {
"type": "integer",
"title": "Attacker Number",
"description": "Attacking driver's race number."
}
}
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}
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}f1_head_to_head_pace
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" values are median lap times after dropping pit-out laps and slow outliers (safety-car / in-laps). meta.estimated: true.
{
"type": "object",
"title": "f1_head_to_head_paceArguments",
"required": [
"session_key",
"driver_a",
"driver_b"
],
"properties": {
"driver_a": {
"type": "integer",
"title": "Driver A",
"description": "First driver's race number."
},
"driver_b": {
"type": "integer",
"title": "Driver B",
"description": "Second driver's race number."
},
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
}
}
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}f1_weather_strategy_impact
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.
{
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"title": "f1_weather_strategy_impactArguments",
"required": [
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"properties": {
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
}
}
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}{
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}f1_predict_pit_strategy
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 = full race ahead). total_laps: Total race laps. If omitted, estimated from the regulation minimum race distance (305 km; Monaco 260 km) over the circuit's lap length; INVALID_INPUT when the circuit is unknown. An explicit value always wins. Returns: data.stop_laps: recommended pit laps. data.compound_sequence: tyre compounds for each stint. data.expected_finish_position: currently always None (not modelled). data.confidence: 0.0-1.0 model confidence. data.net_saving_s: stay-out time minus the best one-stop time (seconds). data.current_tyre_age: laps on the current tyre set at current_lap. meta.total_laps: race length used. meta.total_laps_source: "explicit" or "regulation_estimate". meta.estimated: true. Example: f1_predict_pit_strategy(session_key=11353, driver_number=1, total_laps=72) f1_predict_pit_strategy(session_key=11353, driver_number=1, current_lap=20)
{
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"properties": {
"total_laps": {
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"type": "integer"
},
{
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}
],
"title": "Total Laps",
"default": null,
"description": "Total race laps. If omitted, estimated from the regulation minimum race distance (305 km; Monaco 260 km) over the circuit's lap length; INVALID_INPUT when the circuit is unknown. An explicit value always wins."
},
"current_lap": {
"type": "integer",
"title": "Current Lap",
"default": 1,
"description": "Current lap to project from (default 1 = full race ahead)."
},
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier for a recorded race."
},
"driver_number": {
"type": "integer",
"title": "Driver Number",
"description": "Driver's race number (driver numbers change by season; use f1_get_drivers)."
}
}
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}f1_simulate_championship
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 winning every remaining race and sprint has title_prob 0.0. This is not a cricket or football tool. Args: year: Championship season (2018-2030, default 2026). iterations: Monte Carlo iterations, clamped to 1000-20000 (default 10000). seed: Optional RNG seed for reproducible probabilities. Returns: data.drivers: [{code, points_now, title_prob, expected_points, eliminated, max_possible}] sorted by title_prob. data.max_remaining_points: most points one driver can still score. data.rounds_remaining: races left after the last completed round. meta.estimated: true. meta.model: "plackett-luce-points-rate". meta.is_stale: true if standings or schedule came from stale cache.
{
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"title": "f1_simulate_championshipArguments",
"properties": {
"seed": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"title": "Seed",
"default": null,
"description": "Optional RNG seed for reproducible probabilities."
},
"year": {
"type": "integer",
"title": "Year",
"default": 2026,
"description": "Championship season (2018-2030, default 2026)."
},
"iterations": {
"type": "integer",
"title": "Iterations",
"default": 10000,
"description": "Monte Carlo iterations, clamped to 1000-20000 (default 10000)."
}
}
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}f1_qualifying_analysis
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. data.drivers_analysed: count of drivers with valid laps. meta.estimated: true — grid derived from session laps, not official timing.
{
"type": "object",
"title": "f1_qualifying_analysisArguments",
"required": [
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],
"properties": {
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier for a Qualifying session."
}
}
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}f1_race_pace_compare
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.estimated: true — degradation model fit, not official timing.
{
"type": "object",
"title": "f1_race_pace_compareArguments",
"required": [
"session_key",
"driver_a",
"driver_b"
],
"properties": {
"driver_a": {
"type": "integer",
"title": "Driver A",
"description": "First driver's race number."
},
"driver_b": {
"type": "integer",
"title": "Driver B",
"description": "Second driver's race number."
},
"session_key": {
"type": "integer",
"title": "Session Key",
"description": "OpenF1 session identifier."
}
}
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}cricket_get_live_matches
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.
{
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}cricket_get_scorecard
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.
{
"type": "object",
"title": "cricket_get_scorecardArguments",
"required": [
"match_id"
],
"properties": {
"match_id": {
"type": "string",
"title": "Match Id",
"description": "The match identifier (e.g. from cricket_get_live_matches)."
}
}
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}cricket_get_points_table
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.
{
"type": "object",
"title": "cricket_get_points_tableArguments",
"required": [
"series_id"
],
"properties": {
"series_id": {
"type": "string",
"title": "Series Id",
"description": "The series identifier (e.g. IPL 2026 series ID from CricAPI)."
}
}
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}cricket_get_schedule
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 skip for paging (default 0). Returns: data.matches: page of matches dated today (UTC) or later, with teams, date, venue; filtered to ``series_id`` when the source labels series. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
{
"type": "object",
"title": "cricket_get_scheduleArguments",
"properties": {
"limit": {
"type": "integer",
"title": "Limit",
"default": 50,
"description": "Max matches to return, 1..200 (default 50)."
},
"offset": {
"type": "integer",
"title": "Offset",
"default": 0,
"description": "Number of matches to skip for paging (default 0)."
},
"series_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Series Id",
"default": null,
"description": "Optional. Filter to a specific series. If omitted, returns all upcoming fixtures across all active series."
}
}
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}cricket_get_squad
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.players: list of players with name, role, and credits. meta.source: adapter that served the data (cricapi / static_seed).
{
"type": "object",
"title": "cricket_get_squadArguments",
"required": [
"team"
],
"properties": {
"team": {
"type": "string",
"title": "Team",
"description": "Team code or name (e.g. \"MI\", \"CSK\", \"IND\", \"AUS\")."
},
"series_id": {
"anyOf": [
{
"type": "string"
},
{
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}
],
"title": "Series Id",
"default": null,
"description": "Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data."
}
}
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}cricket_get_live_odds
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 The Odds API (requires THEODDS_KEY). Without a key the call returns a clean ALL_SOURCES_FAILED envelope rather than crashing. Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every IPL event. The Odds API uses its own opaque event ids, so a CricAPI match_id cannot be resolved to an event yet — filtering is by team name. Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, away}]} with decimal h2h prices per bookmaker. Empty when no IPL events are listed (typical off-season). meta.source: adapter that served the data (theodds / cache:stale).
{
"type": "object",
"title": "cricket_get_live_oddsArguments",
"properties": {
"team": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team",
"default": null,
"description": "Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every IPL event. The Odds API uses its own opaque event ids, so a CricAPI match_id cannot be resolved to an event yet — filtering is by team name."
}
}
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}cricket_build_dream11_team
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``). Required if match_id is absent. venue: Venue key/name (e.g. ``wankhede``). Required if match_id is absent. strategy: ``"balanced"`` only in Phase 2; future variants reserved. Returns: data.players: 11 picked players with name/role/credits/team/projected_points. data.captain: name of the chosen captain. data.vice_captain: name of the chosen VC. data.total_credits: sum of credits used (<= 100). data.total_projected_points: fantasy points including C x2 and VC x1.5 boosts. meta.estimated: true — projections are model output, not a fantasy oracle. meta.form_source: "default_neutral" (role-based projections; see meta.note). meta.scoring_profile: the Dream11 T20 points profile id. meta.excluded_missing_credits: players dropped for having no contest credits. Example: cricket_build_dream11_team(team_a="MI", team_b="CSK", venue="wankhede") cricket_build_dream11_team(match_id="abc123")
{
"type": "object",
"title": "cricket_build_dream11_teamArguments",
"properties": {
"venue": {
"anyOf": [
{
"type": "string"
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{
"type": "null"
}
],
"title": "Venue",
"default": null,
"description": "Venue key/name (e.g. ``wankhede``). Required if match_id is absent."
},
"team_a": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team A",
"default": null,
"description": "First team code/name (e.g. ``MI``). Required if match_id is absent."
},
"team_b": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team B",
"default": null,
"description": "Second team code/name (e.g. ``CSK``). Required if match_id is absent."
},
"match_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Match Id",
"default": null,
"description": "CricAPI match identifier; resolves team_a/team_b/venue automatically."
},
"strategy": {
"type": "string",
"title": "Strategy",
"default": "balanced",
"description": "``\"balanced\"`` only in Phase 2; future variants reserved."
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}
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}cricket_captain_recommendation
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 history. Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name (IPL ground, e.g. ``wankhede``). Required if match_id is absent. Returns: data.candidates: list of 3 dicts with name/role/team/projected_points. meta.source: model:captain_score. meta.estimated: true. meta.form_source: "default_neutral".
{
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"properties": {
"venue": {
"anyOf": [
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"type": "string"
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}
],
"title": "Venue",
"default": null,
"description": "Venue key/name (IPL ground, e.g. ``wankhede``). Required if match_id is absent."
},
"team_a": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team A",
"default": null,
"description": "First team code/name. Required if match_id is absent."
},
"team_b": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team B",
"default": null,
"description": "Second team code/name. Required if match_id is absent."
},
"match_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Match Id",
"default": null,
"description": "CricAPI match identifier; resolves team_a/team_b/venue automatically."
}
}
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}cricket_differential_picks
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/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name. Required if match_id is absent. ownership_threshold: percent ownership cap; affects estimated label. Returns: data.picks: list of {name, role, team, credits, projected_points, estimated_ownership_pct, ownership_pct_estimated}. meta.source: model:captain_score (filtered). meta.estimated: true. meta.ownership_source: "heuristic_from_credits". meta.form_source: "default_neutral".
{
"type": "object",
"title": "cricket_differential_picksArguments",
"properties": {
"venue": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Venue",
"default": null,
"description": "Venue key/name. Required if match_id is absent."
},
"team_a": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team A",
"default": null,
"description": "First team code/name. Required if match_id is absent."
},
"team_b": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team B",
"default": null,
"description": "Second team code/name. Required if match_id is absent."
},
"match_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Match Id",
"default": null,
"description": "CricAPI match identifier; resolves team_a/team_b/venue automatically."
},
"ownership_threshold": {
"type": "integer",
"title": "Ownership Threshold",
"default": 20,
"description": "percent ownership cap; affects estimated label."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}cricket_player_form_index
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. meta.source: which adapter served the underlying stats. meta.estimated: true.
{
"type": "object",
"title": "cricket_player_form_indexArguments",
"required": [
"player_id"
],
"properties": {
"player_id": {
"type": "string",
"title": "Player Id",
"description": "Upstream player identifier (CricAPI/Cricbuzz id)."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}cricket_get_pitch_report
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.
{
"type": "object",
"title": "cricket_get_pitch_reportArguments",
"required": [
"venue"
],
"properties": {
"venue": {
"type": "string",
"title": "Venue",
"description": "Venue key (e.g. ``wankhede``), official name, or city."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}cricket_find_value_bets
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 flag every market underdog, which would be misleading. It still reports how many events were screened so callers know odds were available. For raw de-vigged prices use ``cricket_get_live_odds``. Real edge detection lands when a cricket win model is wired (see cricket_head_to_head); it will flag outcomes where the model beats the de-vigged market by ``min_edge`` **and** the offered price has positive expected value. Args: team: Optional team name to filter events (case-insensitive substring). Omit to scan every IPL odds event. min_edge: Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05. Currently informational only (no bets emitted). Returns: data.value_bets: always ``[]`` until a cricket model is wired. data.events_analysed: count of not-yet-started events screened (both teams present). data.model: ``"neutral_baseline"``. data.note: why no bets are emitted. meta.estimated: true.
{
"type": "object",
"title": "cricket_find_value_betsArguments",
"properties": {
"team": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Team",
"default": null,
"description": "Optional team name to filter events (case-insensitive substring). Omit to scan every IPL odds event."
},
"min_edge": {
"type": "number",
"title": "Min Edge",
"default": 0.05,
"description": "Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05. Currently informational only (no bets emitted)."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}cricket_head_to_head
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_a, key_players_b, h2h_win_rate_a, h2h_win_rate_b, win_prob_a, win_prob_b}. meta.estimated: true. meta.estimate_quality: "uninformative" when no player stats were available (both sides at default form), else "player_stats".
{
"type": "object",
"title": "cricket_head_to_headArguments",
"required": [
"team_a",
"team_b"
],
"properties": {
"team_a": {
"type": "string",
"title": "Team A",
"description": "First team code or name (e.g. \"MI\", \"India\")."
},
"team_b": {
"type": "string",
"title": "Team B",
"description": "Second team code or name (e.g. \"CSK\", \"Australia\")."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}cricket_player_matchup
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_holder, edge_reason, signals, role_a, role_b}. meta.estimated: true — heuristic model, not ball-by-ball H2H data.
{
"type": "object",
"title": "cricket_player_matchupArguments",
"required": [
"player_a",
"player_b"
],
"properties": {
"player_a": {
"type": "string",
"title": "Player A",
"description": "CricAPI player ID for the first player (names are not resolved)."
},
"player_b": {
"type": "string",
"title": "Player B",
"description": "CricAPI player ID for the second player (names are not resolved)."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}cross_sport_build_accumulator
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: true.
{
"type": "object",
"title": "cross_sport_build_accumulatorArguments",
"properties": {
"legs": {
"type": "integer",
"title": "Legs",
"default": 3,
"description": "Total legs across both sports (2-8). Default 3."
},
"min_edge": {
"type": "number",
"title": "Min Edge",
"default": 0.05,
"description": "Minimum edge per leg. Default 0.05."
}
}
}{
"type": "object",
"title": "Envelope",
"properties": {
"data": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Data",
"default": null
},
"meta": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Meta",
"default": null
},
"error": {
"anyOf": [
{
"type": "object",
"additionalProperties": true
},
{
"type": "null"
}
],
"title": "Error",
"default": null
}
}
}{
"readOnlyHint": true,
"openWorldHint": true,
"idempotentHint": true,
"destructiveHint": false
}Prompts
10 advertised
dream11_team_builder — Fantasy XI Builder
Build an optimal fantasy XI for an IPL match.
- team_a
- No description (required)
- team_b
- No description (required)
- venue
- No description (required)
f1_race_strategy — F1 Pit Strategy
Predict pit strategy for a driver at a specific Grand Prix.
- year
- No description (required)
- country
- No description (required)
- driver_number
- No description (required)
world_cup_2026_retrospective — World Cup 2026 Retrospective
Review the completed FIFA World Cup 2026 against the model's simulation.
cricket_value_bets — Cricket Probability Edge
Compare model probabilities against the market across IPL matches.
f1_driver_comparison — F1 Driver Comparison
Compare two F1 drivers' pace at a Grand Prix.
- year
- No description (required)
- country
- No description (required)
- driver_a
- No description (required)
- driver_b
- No description (required)
cricket_captain_pick — IPL Captain Pick
Recommend a captain for tonight's live IPL match.
predict_match — Predict a Match
Predict the scoreline and outcome of one World Cup 2026 match.
- home_team
- No description (required)
- away_team
- No description (required)
build_accumulator — Multi-Match Model
Model the joint probability of multiple outcomes across sports.
- legs
- No description
server_health — Server Health
Report cache backend, adapter status, and remaining API quota.
wc_group_situation — World Cup Group Situation
Show the World Cup 2026 group draw and advancement format.
Resources
7 advertised
sportiq-instructions — SportIQ AI Operating Guide
Read once at session start: tool catalogue, min-call recipes, envelope format, staleness rules, and error recovery for all 44 tools.
sportiq://instructionsfootball-wc2026-draw — FIFA World Cup 2026 draw
WC 2026 format and the 12 groups of team codes.
sportiq://football/wc2026/drawfootball-team-codes — Football team codes
WC 2026 team code → name and accepted name aliases.
sportiq://football/team-codesf1-circuits — F1 circuit profiles
Measured circuit profiles (pit loss, lap length) keyed by circuit key.
sportiq://f1/circuitscricket-venues — Cricket venues
Venue keys accepted by the cricket tools, with names and cities.
sportiq://cricket/venuescricket-teams — IPL team codes
IPL franchise code → official name.
sportiq://cricket/teamscricket-scoring-profile — Dream11 T20 scoring profile
The versioned Dream11 T20 points table the fantasy tools score with.
sportiq://cricket/scoring-profile90-day uptime
A lightweight connection check about every six hours, sampled on hosts with many listings. It shows the server answered, not that its tools work.
- 2026-11-07: 1 of 1 checks answered
30-day availability
Aggregated from read-only remote checks; this is not a tool-execution or security guarantee.
- Successful checks
- 1 of 1 (100%)
- Average latency
- 2482 ms
- 95th percentile latency
- 2482 ms
Maintenance history
Public check and source-refresh events. Editorial and ownership records remain private.
- · probe · passed · 2482 ms · metadata changed · 46 tool(s), 7 resource(s), 0 resource template(s), 10 prompt(s)
Connect from your client
Each snippet registers the same endpoint; pick the client you use.
Claude Code
claude mcp add --transport http sportiq-mcp-utkarshgupta https://sportiq.utkarshgupta.org/mcpCodex
codex mcp add sportiq-mcp-utkarshgupta --url https://sportiq.utkarshgupta.org/mcpCursor
{
"mcpServers": {
"sportiq-mcp-utkarshgupta": {
"url": "https://sportiq.utkarshgupta.org/mcp"
}
}
}Cline
{
"mcpServers": {
"sportiq-mcp-utkarshgupta": {
"type": "streamableHttp",
"url": "https://sportiq.utkarshgupta.org/mcp",
"disabled": false
}
}
}VS Code
{
"servers": {
"sportiq-mcp-utkarshgupta": {
"type": "http",
"url": "https://sportiq.utkarshgupta.org/mcp"
}
}
}Gemini CLI
gemini mcp add --transport http sportiq-mcp-utkarshgupta https://sportiq.utkarshgupta.org/mcpClaude Desktop
https://sportiq.utkarshgupta.org/mcpWindsurf
{
"mcpServers": {
"sportiq-mcp-utkarshgupta": {
"serverUrl": "https://sportiq.utkarshgupta.org/mcp"
}
}
}Let your agent find its tools
Find servers like SportIQ MCP from your agent. Add Koskamo once to search the index for endpoints, access requirements, and client setup. Public discovery needs no key.