sportiq-mcp
MCP tools for FIFA World Cup 2026 football, Formula 1, and IPL cricket — sims, strategy, fantasy.
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Calidad y seguridad
Hallazgos (3)
- LOWTool 'football_knockout_path' description lacks action verben football_knockout_path
- LOWTool 'f1_get_standings' description lacks action verben f1_get_standings
- LOWTool 'cricket_get_points_table' description lacks action verben cricket_get_points_table
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"sportiq-mcp": {
"command": "uvx",
"args": [
"sportiq-mcp"
]
}
}
}Paquetes ejecutables
0.3.2stdioPuntos de conexión remotos
https://sportiq.utkarshgupta.org/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (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`.
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "sportiq_healthArguments"
}Esquema de salida
{
"type": "object",
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},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "football_get_groupsArguments"
}Esquema de salida
{
"type": "object",
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"data": {
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}
},
"title": "Envelope"
}🟢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).
Esquema de entrada
{
"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)."
}
},
"title": "football_get_fixturesArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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)."
}
},
"title": "football_get_standingsArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
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],
"default": null,
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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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
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],
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "football_get_top_scorersArguments"
}Esquema de salida
{
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},
"title": "Envelope"
}🟢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).
Esquema de entrada
{
"type": "object",
"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 return every WC event."
}
},
"title": "football_get_oddsArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
{
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},
{
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],
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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": [
"home_team",
"away_team"
],
"title": "football_xg_modelArguments"
}Esquema de salida
{
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"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"home_team": {
"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)."
}
},
"required": [
"home_team",
"away_team"
],
"title": "football_match_predictorArguments"
}Esquema de salida
{
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},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"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": [
"group"
],
"title": "football_simulate_groupArguments"
}Esquema de salida
{
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"properties": {
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],
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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)
Esquema de entrada
{
"type": "object",
"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"
},
{
"type": "null"
}
],
"default": null,
"title": "Seed",
"description": "Optional RNG seed for reproducible output."
}
},
"title": "football_simulate_bracketArguments"
}Esquema de salida
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"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"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"
},
{
"type": "null"
}
],
"default": null,
"title": "Seed",
"description": "Optional RNG seed."
}
},
"required": [
"team"
],
"title": "football_knockout_pathArguments"
}Esquema de salida
{
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},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"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"
}Esquema de salida
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},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"team": {
"title": "Team",
"type": "string",
"description": "Team name (e.g. \"Brazil\", \"Argentina\")."
}
},
"required": [
"team"
],
"title": "football_form_trendsArguments"
}Esquema de salida
{
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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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
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],
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"title": "Error"
}
},
"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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
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"type": "object"
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],
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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.
Esquema de entrada
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
}
},
"required": [
"session_key"
],
"title": "f1_get_driversArguments"
}Esquema de salida
{
"type": "object",
"properties": {
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
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"title": "Error"
}
},
"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.
Esquema de entrada
{
"type": "object",
"properties": {
"year": {
"title": "Year",
"type": "integer",
"description": "Championship year (e.g. 2026)."
}
},
"required": [
"year"
],
"title": "f1_get_standingsArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
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"title": "Error"
}
},
"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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
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"type": "object"
},
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],
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"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
}
},
"required": [
"session_key"
],
"title": "f1_get_weatherArguments"
}Esquema de salida
{
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],
"default": null,
"title": "Error"
}
},
"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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
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"type": "object"
},
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],
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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"
}Esquema de salida
{
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],
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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": [
"session_key",
"driver_a",
"driver_b"
],
"title": "f1_head_to_head_paceArguments"
}Esquema de salida
{
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"properties": {
"data": {
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],
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier."
}
},
"required": [
"session_key"
],
"title": "f1_weather_strategy_impactArguments"
}Esquema de salida
{
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}
},
"title": "Envelope"
}🟢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)
Esquema de entrada
{
"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)."
},
"total_laps": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"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": [
"session_key",
"driver_number"
],
"title": "f1_predict_pit_strategyArguments"
}Esquema de salida
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},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"session_key": {
"title": "Session Key",
"type": "integer",
"description": "OpenF1 session identifier for a Qualifying session."
}
},
"required": [
"session_key"
],
"title": "f1_qualifying_analysisArguments"
}Esquema de salida
{
"type": "object",
"properties": {
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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": [
"session_key",
"driver_a",
"driver_b"
],
"title": "f1_race_pace_compareArguments"
}Esquema de salida
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {},
"title": "cricket_get_live_matchesArguments"
}Esquema de salida
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"properties": {
"data": {
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"error": {
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],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
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"type": "object"
},
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],
"default": null,
"title": "Data"
},
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"error": {
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}
],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"series_id": {
"title": "Series Id",
"type": "string",
"description": "The series identifier (e.g. IPL 2026 series ID from CricAPI)."
}
},
"required": [
"series_id"
],
"title": "cricket_get_points_tableArguments"
}Esquema de salida
{
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"properties": {
"data": {
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],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"series_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"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"
}Esquema de salida
{
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"properties": {
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],
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"title": "Error"
}
},
"title": "Envelope"
}🟢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).
Esquema de entrada
{
"type": "object",
"properties": {
"team": {
"title": "Team",
"type": "string",
"description": "Team code or name (e.g. \"MI\", \"CSK\", \"IND\", \"AUS\")."
},
"series_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Series Id",
"description": "Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data."
}
},
"required": [
"team"
],
"title": "cricket_get_squadArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
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"type": "object"
},
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],
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],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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).
Esquema de entrada
{
"type": "object",
"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 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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
{
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"type": "object"
},
{
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],
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},
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],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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")
Esquema de entrada
{
"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 (e.g. ``MI``). Required if match_id is absent."
},
"team_b": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Team B",
"description": "Second team code/name (e.g. ``CSK``). Required if match_id is absent."
},
"venue": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Venue",
"description": "Venue key/name (e.g. ``wankhede``). Required if match_id is absent."
},
"strategy": {
"default": "balanced",
"title": "Strategy",
"type": "string",
"description": "``\"balanced\"`` only in Phase 2; future variants reserved."
}
},
"title": "cricket_build_dream11_teamArguments"
}Esquema de salida
{
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"data": {
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],
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"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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."
},
"team_b": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Team B",
"description": "Second team code/name. Required if match_id is absent."
},
"venue": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Venue",
"description": "Venue key/name (IPL ground, e.g. ``wankhede``). Required if match_id is absent."
}
},
"title": "cricket_captain_recommendationArguments"
}Esquema de salida
{
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"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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."
},
"team_b": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Team B",
"description": "Second team code/name. Required if match_id is absent."
},
"venue": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Venue",
"description": "Venue key/name. Required if match_id is absent."
},
"ownership_threshold": {
"default": 20,
"title": "Ownership Threshold",
"type": "integer",
"description": "percent ownership cap; affects estimated label."
}
},
"title": "cricket_differential_picksArguments"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
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],
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}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
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"type": "object"
},
{
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],
"default": null,
"title": "Data"
},
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],
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},
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},
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}
],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"venue": {
"title": "Venue",
"type": "string",
"description": "Venue key (e.g. ``wankhede``), official name, or city."
}
},
"required": [
"venue"
],
"title": "cricket_get_pitch_reportArguments"
}Esquema de salida
{
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],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"team": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Team",
"description": "Optional team name to filter events (case-insensitive substring). Omit to scan every IPL 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. Currently informational only (no bets emitted)."
}
},
"title": "cricket_find_value_betsArguments"
}Esquema de salida
{
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"properties": {
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},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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": [
"team_a",
"team_b"
],
"title": "cricket_head_to_headArguments"
}Esquema de salida
{
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"properties": {
"data": {
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},
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],
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"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"type": "object",
"properties": {
"player_a": {
"title": "Player A",
"type": "string",
"description": "Player ID or name for the first player."
},
"player_b": {
"title": "Player B",
"type": "string",
"description": "Player ID or name for the second player."
}
},
"required": [
"player_a",
"player_b"
],
"title": "cricket_player_matchupArguments"
}Esquema de salida
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},
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"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Meta"
},
"error": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}🟢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.
Esquema de entrada
{
"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"
}Esquema de salida
{
"type": "object",
"properties": {
"data": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Data"
},
"meta": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Meta"
},
"error": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Error"
}
},
"title": "Envelope"
}Comunidad
Evidencia