sportiq-mcp
MCP tools for FIFA World Cup 2026 football, Formula 1, and IPL cricket — sims, strategy, fantasy.
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (3)
- LOWTool 'football_knockout_path' description lacks action verbin football_knockout_path
- LOWTool 'f1_get_standings' description lacks action verbin f1_get_standings
- LOWTool 'cricket_get_points_table' description lacks action verbin cricket_get_points_table
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"sportiq-mcp": {
"command": "uvx",
"args": [
"sportiq-mcp"
]
}
}
}Ausführbare Pakete
0.3.2stdioRemote-Endpunkte
https://sportiq.utkarshgupta.org/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (44)
🟢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`.
Eingabe-Schema
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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(limit, offset)
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).
Eingabe-Schema
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"type": "object",
"properties": {
"limit": {
"default": 50,
"title": "Limit",
"type": "integer",
"description": "Max fixtures to return, 1..200 (default 50)."
},
"offset": {
"default": 0,
"title": "Offset",
"type": "integer",
"description": "Number of fixtures to skip for paging (default 0)."
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},
"title": "football_get_fixturesArguments"
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}🟢football_get_standings(limit, offset)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"limit": {
"default": 50,
"title": "Limit",
"type": "integer",
"description": "Max standing rows to return, 1..200 (default 50)."
},
"offset": {
"default": 0,
"title": "Offset",
"type": "integer",
"description": "Number of rows to skip for paging (default 0)."
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},
"title": "football_get_standingsArguments"
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"title": "Envelope"
}🟢football_get_squad(team)
Return a national team's World Cup squad. Args: team: Team code or name (e.g. "ARG"). Without an API-Football key, the static seed serves an empty-but-valid squad (rosters are a follow-up). Returns: data.squad: list of {name, number, position, age}. meta.source: adapter that served the data.
Eingabe-Schema
{
"type": "object",
"properties": {
"team": {
"title": "Team",
"type": "string",
"description": "Team code or name (e.g. \"ARG\"). Without an API-Football key, the static seed serves an empty-but-valid squad (rosters are a follow-up)."
}
},
"required": [
"team"
],
"title": "football_get_squadArguments"
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}🟢football_get_match_stats(team)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"team": {
"title": "Team",
"type": "integer",
"description": "API-Football numeric team id (not a country code)."
}
},
"required": [
"team"
],
"title": "football_get_match_statsArguments"
}Ausgabe-Schema
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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.
Eingabe-Schema
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}🟢football_get_odds(team)
Return live market head-to-head odds for upcoming World Cup 2026 matches. 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 WC event. Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, draw, away}]} with decimal 1X2 prices per bookmaker. meta.source: adapter that served the data (theodds / cache:stale).
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}🟢football_xg_model(home_team, away_team, neutral)
Estimate a match's expected goals and win/draw/loss probabilities. Args: home_team: First team code (e.g. "ARG"). away_team: Second team code (e.g. "BRA"). 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.
Eingabe-Schema
{
"type": "object",
"properties": {
"home_team": {
"title": "Home Team",
"type": "string",
"description": "First team code (e.g. \"ARG\")."
},
"away_team": {
"title": "Away Team",
"type": "string",
"description": "Second team code (e.g. \"BRA\")."
},
"neutral": {
"default": true,
"title": "Neutral",
"type": "boolean",
"description": "True for a neutral venue (no home advantage). World Cup default."
}
},
"required": [
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"away_team"
],
"title": "football_xg_modelArguments"
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}🟢football_match_predictor(home_team, away_team, neutral)
Predict a single match: most likely scoreline + outcome probabilities. Args: home_team: First team code. away_team: Second team code. neutral: True for a neutral venue (World Cup default). Returns: data: {most_likely_score, home_win, draw, away_win, predicted_winner}. meta.estimated: true.
Eingabe-Schema
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"title": "Home Team",
"type": "string",
"description": "First team code."
},
"away_team": {
"title": "Away Team",
"type": "string",
"description": "Second team code."
},
"neutral": {
"default": true,
"title": "Neutral",
"type": "boolean",
"description": "True for a neutral venue (World Cup default)."
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},
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"title": "football_match_predictorArguments"
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}🟢football_simulate_group(group, iterations)
Monte Carlo one group within the full 12-group qualification context. Args: group: Group letter A-L. 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.
Eingabe-Schema
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"group": {
"title": "Group",
"type": "string",
"description": "Group letter A-L."
},
"iterations": {
"default": 5000,
"title": "Iterations",
"type": "integer",
"description": "Number of simulations (clamped to 100..20000)."
}
},
"required": [
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],
"title": "football_simulate_groupArguments"
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"title": "Envelope"
}🟢football_simulate_bracket(iterations, seed)
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)
Eingabe-Schema
{
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"properties": {
"iterations": {
"default": 10000,
"title": "Iterations",
"type": "integer",
"description": "Number of tournament simulations (clamped to 100..20000; ~10000 gives stable ±2% probabilities)."
},
"seed": {
"anyOf": [
{
"type": "integer"
},
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],
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"description": "Optional RNG seed for reproducible output."
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},
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}🟢football_knockout_path(team, iterations, seed)
Round-by-round survival probabilities for one team in the full sim. Args: team: Team code (e.g. "FRA"). 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.
Eingabe-Schema
{
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"team": {
"title": "Team",
"type": "string",
"description": "Team code (e.g. \"FRA\")."
},
"iterations": {
"default": 10000,
"title": "Iterations",
"type": "integer",
"description": "Number of tournament simulations (clamped to 100..20000)."
},
"seed": {
"anyOf": [
{
"type": "integer"
},
{
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}
],
"default": null,
"title": "Seed",
"description": "Optional RNG seed."
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},
"required": [
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"title": "football_knockout_pathArguments"
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}🟢football_find_value_bets(team, min_edge)
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. Where the model probability exceeds the de-vigged market probability by at least ``min_edge``, the outcome is flagged with its edge and the model's fair odds. Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). 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, bookmaker}, sorted by edge descending. data.events_analysed: events with both teams rated (model-comparable). meta.estimated: true. meta.is_stale reflects the odds freshness.
Eingabe-Schema
{
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"properties": {
"team": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Team",
"description": "Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to scan every WC 2026 odds event."
},
"min_edge": {
"default": 0.05,
"title": "Min Edge",
"type": "number",
"description": "Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05 (5 percentage points)."
}
},
"title": "football_find_value_betsArguments"
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}🟢football_form_trends(team)
Return rolling form, goal record, and xG trend for a football team. Args: team: Team name (e.g. "Brazil", "Argentina"). 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.
Eingabe-Schema
{
"type": "object",
"properties": {
"team": {
"title": "Team",
"type": "string",
"description": "Team name (e.g. \"Brazil\", \"Argentina\")."
}
},
"required": [
"team"
],
"title": "football_form_trendsArguments"
}Ausgabe-Schema
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"title": "Envelope"
}🟢football_build_accumulator(legs, min_edge)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"legs": {
"default": 3,
"title": "Legs",
"type": "integer",
"description": "Number of legs (2-8). Default 3."
},
"min_edge": {
"default": 0.05,
"title": "Min Edge",
"type": "number",
"description": "Minimum edge threshold per leg. Default 0.05."
}
},
"title": "football_build_accumulatorArguments"
}Ausgabe-Schema
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"title": "Envelope"
}🟢f1_get_sessions(year, country)
Return F1 sessions for a given year, optionally filtered by country. Args: year: Championship year (e.g. 2025). country: Optional country name to filter (e.g. "Monaco"). Returns: data.sessions: list of session objects with session_key, session_type, date. meta.source: adapter that served the data.
Eingabe-Schema
{
"type": "object",
"properties": {
"year": {
"title": "Year",
"type": "integer",
"description": "Championship year (e.g. 2025)."
},
"country": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Country",
"description": "Optional country name to filter (e.g. \"Monaco\")."
}
},
"required": [
"year"
],
"title": "f1_get_sessionsArguments"
}Ausgabe-Schema
{
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},
"title": "Envelope"
}🟢f1_get_drivers(session_key)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
}
},
"required": [
"session_key"
],
"title": "f1_get_driversArguments"
}Ausgabe-Schema
{
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}🟢f1_get_lap_times(session_key, driver_number, limit, offset)
Return lap times for a driver in a specific F1 session. Args: session_key: OpenF1 session identifier. driver_number: Driver's race number (e.g. 1 for Verstappen). 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.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
},
"driver_number": {
"title": "Driver Number",
"type": "integer",
"description": "Driver's race number (e.g. 1 for Verstappen)."
},
"limit": {
"default": 100,
"title": "Limit",
"type": "integer",
"description": "Max laps to return, 1..200 (default 100 — covers most full races)."
},
"offset": {
"default": 0,
"title": "Offset",
"type": "integer",
"description": "Number of laps to skip for paging (default 0)."
}
},
"required": [
"session_key",
"driver_number"
],
"title": "f1_get_lap_timesArguments"
}Ausgabe-Schema
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"title": "Envelope"
}🟢f1_get_standings(year)
Return F1 driver and constructor championship standings for a year. Use this for "who is leading / who will win the F1 championship this year". There is no F1 title Monte Carlo — current points and position are the answer. This is not a cricket or football tool. Args: year: Championship year (e.g. 2026). Returns: data.driver_standings: driver championship positions and points. data.constructor_standings: constructor championship positions and points. meta.source: adapter that served the data.
Eingabe-Schema
{
"type": "object",
"properties": {
"year": {
"title": "Year",
"type": "integer",
"description": "Championship year (e.g. 2026)."
}
},
"required": [
"year"
],
"title": "f1_get_standingsArguments"
}Ausgabe-Schema
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"title": "Envelope"
}🟢f1_get_race_results(year, round)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"year": {
"title": "Year",
"type": "integer",
"description": "Championship year (e.g. 2025)."
},
"round": {
"title": "Round",
"type": "integer",
"description": "Round number within the season (1-based; e.g. 1 for the opener)."
}
},
"required": [
"year",
"round"
],
"title": "f1_get_race_resultsArguments"
}Ausgabe-Schema
{
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}🟢f1_get_weather(session_key)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
}
},
"required": [
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],
"title": "f1_get_weatherArguments"
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},
"title": "Envelope"
}🟢f1_tyre_degradation(session_key, driver_number, compound)
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). Returns: data: {intercept, slope, residual_std, sample_count}. meta.estimated: true — model output, not telemetry oracle.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
},
"driver_number": {
"title": "Driver Number",
"type": "integer",
"description": "Driver's race number."
},
"compound": {
"title": "Compound",
"type": "string",
"description": "Tyre compound (SOFT, MEDIUM, HARD, INTER, WET)."
}
},
"required": [
"session_key",
"driver_number",
"compound"
],
"title": "f1_tyre_degradationArguments"
}Ausgabe-Schema
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}🟢f1_undercut_window(session_key, attacker_number, target_number, current_lap)
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.estimated: true.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
},
"attacker_number": {
"title": "Attacker Number",
"type": "integer",
"description": "Attacking driver's race number."
},
"target_number": {
"title": "Target Number",
"type": "integer",
"description": "Target driver's race number."
},
"current_lap": {
"title": "Current Lap",
"type": "integer",
"description": "Current lap number in the race."
}
},
"required": [
"session_key",
"attacker_number",
"target_number",
"current_lap"
],
"title": "f1_undercut_windowArguments"
}Ausgabe-Schema
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}🟢f1_head_to_head_pace(session_key, driver_a, driver_b)
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}. meta.estimated: true.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
},
"driver_a": {
"title": "Driver A",
"type": "integer",
"description": "First driver's race number."
},
"driver_b": {
"title": "Driver B",
"type": "integer",
"description": "Second driver's race number."
}
},
"required": [
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"driver_a",
"driver_b"
],
"title": "f1_head_to_head_paceArguments"
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}🟢f1_weather_strategy_impact(session_key)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
}
},
"required": [
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],
"title": "f1_weather_strategy_impactArguments"
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}🟢f1_predict_pit_strategy(session_key, driver_number, current_lap, total_laps)
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 (e.g. 1 for Verstappen). current_lap: Current lap to project from (default 1 = full race ahead). total_laps: Total race laps. If omitted, inferred from the highest observed lap_number in the fetched laps (correct for Monaco 78 / Spa 44), falling back to 57 when no laps are available. 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. meta.total_laps: race length used (explicit arg, else inferred from laps). meta.estimated: true. Example: f1_predict_pit_strategy(session_key=9158, driver_number=1) f1_predict_pit_strategy(session_key=9158, driver_number=16, current_lap=20, total_laps=78)
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier for a recorded race."
},
"driver_number": {
"title": "Driver Number",
"type": "integer",
"description": "Driver's race number (e.g. 1 for Verstappen)."
},
"current_lap": {
"default": 1,
"title": "Current Lap",
"type": "integer",
"description": "Current lap to project from (default 1 = full race ahead)."
},
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{
"type": "integer"
},
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}
],
"default": null,
"title": "Total Laps",
"description": "Total race laps. If omitted, inferred from the highest observed lap_number in the fetched laps (correct for Monaco 78 / Spa 44), falling back to 57 when no laps are available. An explicit value always wins."
}
},
"required": [
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"driver_number"
],
"title": "f1_predict_pit_strategyArguments"
}Ausgabe-Schema
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}🟢f1_qualifying_analysis(session_key)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier for a Qualifying session."
}
},
"required": [
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],
"title": "f1_qualifying_analysisArguments"
}Ausgabe-Schema
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}🟢f1_race_pace_compare(session_key, driver_a, driver_b)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
},
"driver_a": {
"title": "Driver A",
"type": "integer",
"description": "First driver's race number."
},
"driver_b": {
"title": "Driver B",
"type": "integer",
"description": "Second driver's race number."
}
},
"required": [
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"driver_a",
"driver_b"
],
"title": "f1_race_pace_compareArguments"
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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.
Eingabe-Schema
{
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"title": "cricket_get_live_matchesArguments"
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}🟢cricket_get_scorecard(match_id)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"match_id": {
"title": "Match Id",
"type": "string",
"description": "The match identifier (e.g. from cricket_get_live_matches)."
}
},
"required": [
"match_id"
],
"title": "cricket_get_scorecardArguments"
}Ausgabe-Schema
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}🟢cricket_get_points_table(series_id)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"series_id": {
"title": "Series Id",
"type": "string",
"description": "The series identifier (e.g. IPL 2026 series ID from CricAPI)."
}
},
"required": [
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"title": "cricket_get_points_tableArguments"
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}🟢cricket_get_schedule(series_id, limit, offset)
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 upcoming matches with teams, date, venue. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
Eingabe-Schema
{
"type": "object",
"properties": {
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"anyOf": [
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"type": "string"
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"default": null,
"title": "Series Id",
"description": "Optional. Filter to a specific series. If omitted, returns all upcoming fixtures across all active series."
},
"limit": {
"default": 50,
"title": "Limit",
"type": "integer",
"description": "Max matches to return, 1..200 (default 50)."
},
"offset": {
"default": 0,
"title": "Offset",
"type": "integer",
"description": "Number of matches to skip for paging (default 0)."
}
},
"title": "cricket_get_scheduleArguments"
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}🟢cricket_get_squad(team, series_id)
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).
Eingabe-Schema
{
"type": "object",
"properties": {
"team": {
"title": "Team",
"type": "string",
"description": "Team code or name (e.g. \"MI\", \"CSK\", \"IND\", \"AUS\")."
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"title": "Series Id",
"description": "Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data."
}
},
"required": [
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"title": "cricket_get_squadArguments"
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}🟢cricket_get_live_odds(team)
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).
Eingabe-Schema
{
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"type": "string"
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}
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"default": null,
"title": "Team",
"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."
}
},
"title": "cricket_get_live_oddsArguments"
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}🟢cricket_build_dream11_team(match_id, team_a, team_b, venue, strategy)
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. Example: cricket_build_dream11_team(team_a="MI", team_b="CSK", venue="wankhede") cricket_build_dream11_team(match_id="abc123")
Eingabe-Schema
{
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"properties": {
"match_id": {
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"title": "Match Id",
"description": "CricAPI match identifier; resolves team_a/team_b/venue automatically."
},
"team_a": {
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"type": "string"
},
{
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}
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{
"type": "string"
},
{
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}
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"title": "Venue",
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},
"strategy": {
"default": "balanced",
"title": "Strategy",
"type": "string",
"description": "``\"balanced\"`` only in Phase 2; future variants reserved."
}
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"title": "cricket_build_dream11_teamArguments"
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}🟢cricket_captain_recommendation(match_id, team_a, team_b, venue)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"match_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Match Id",
"description": "CricAPI match identifier; resolves team_a/team_b/venue automatically."
},
"team_a": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Team A",
"description": "First team code/name. Required if match_id is absent."
},
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{
"type": "string"
},
{
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"title": "Team B",
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"title": "Venue",
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}
},
"title": "cricket_captain_recommendationArguments"
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}🟢cricket_differential_picks(match_id, team_a, team_b, venue, ownership_threshold)
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}. meta.source: model:captain_score (filtered). meta.estimated: true.
Eingabe-Schema
{
"type": "object",
"properties": {
"match_id": {
"anyOf": [
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"type": "string"
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],
"default": null,
"title": "Match Id",
"description": "CricAPI match identifier; resolves team_a/team_b/venue automatically."
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"team_a": {
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"type": "string"
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"title": "Team A",
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"title": "Venue",
"description": "Venue key/name. Required if match_id is absent."
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"ownership_threshold": {
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"title": "Ownership Threshold",
"type": "integer",
"description": "percent ownership cap; affects estimated label."
}
},
"title": "cricket_differential_picksArguments"
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}🟢cricket_player_form_index(player_id)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"player_id": {
"title": "Player Id",
"type": "string",
"description": "Upstream player identifier (CricAPI/Cricbuzz id)."
}
},
"required": [
"player_id"
],
"title": "cricket_player_form_indexArguments"
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}🟢cricket_get_pitch_report(venue)
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.
Eingabe-Schema
{
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"properties": {
"venue": {
"title": "Venue",
"type": "string",
"description": "Venue key (e.g. ``wankhede``), official name, or city."
}
},
"required": [
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],
"title": "cricket_get_pitch_reportArguments"
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}🟢cricket_find_value_bets(team, min_edge)
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). 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 events screened (both teams present). data.model: ``"neutral_baseline"``. data.note: why no bets are emitted. meta.estimated: true.
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{
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"default": null,
"title": "Team",
"description": "Optional team name to filter events (case-insensitive substring). Omit to scan every IPL odds event."
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"min_edge": {
"default": 0.05,
"title": "Min Edge",
"type": "number",
"description": "Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05. Currently informational only (no bets emitted)."
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"title": "cricket_find_value_betsArguments"
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}🟢cricket_head_to_head(team_a, team_b)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"team_a": {
"title": "Team A",
"type": "string",
"description": "First team code or name (e.g. \"MI\", \"India\")."
},
"team_b": {
"title": "Team B",
"type": "string",
"description": "Second team code or name (e.g. \"CSK\", \"Australia\")."
}
},
"required": [
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"team_b"
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"title": "cricket_head_to_headArguments"
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}🟢cricket_player_matchup(player_a, player_b)
Analyse the head-to-head matchup between two cricket players based on role and career stats. Args: player_a: Player ID or name for the first player. player_b: Player ID or name for the second player. Returns: data: {matchup_type, edge_holder, edge_reason, signals, role_a, role_b}. meta.estimated: true — heuristic model, not ball-by-ball H2H data.
Eingabe-Schema
{
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"type": "string",
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"title": "Player B",
"type": "string",
"description": "Player ID or name for the second player."
}
},
"required": [
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"title": "cricket_player_matchupArguments"
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}🟢cross_sport_build_accumulator(legs, min_edge)
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.
Eingabe-Schema
{
"type": "object",
"properties": {
"legs": {
"default": 3,
"title": "Legs",
"type": "integer",
"description": "Total legs across both sports (2-8). Default 3."
},
"min_edge": {
"default": 0.05,
"title": "Min Edge",
"type": "number",
"description": "Minimum edge per leg. Default 0.05."
}
},
"title": "cross_sport_build_accumulatorArguments"
}Ausgabe-Schema
{
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"properties": {
"data": {
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"type": "object"
},
{
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"default": null,
"title": "Data"
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"title": "Envelope"
}Community
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