SportIQ
Related Servers
Alternatives to SportIQ
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceAn MCP server that enables LLMs to act as World Cup forecasting agents, pulling live odds, getting model forecasts, and simulating tournament brackets via natural language.MIT
- AlicenseAqualityCmaintenanceMCP server for the FootballStack football data API. Gives LLM clients and AI agents (Claude, Cursor) access to real football data — fixtures, standings, lineups, live events, shot-level xG with pitch coordinates, and model-derived fair odds.12MIT
- AlicenseAqualityCmaintenanceMCP server for FIFA World Cup 2026 data: matches, teams, venues, city guides, fan zones, visa info, injuries, odds, standings, bracket, and historical matchups. 18 tools, zero external API dependencies.18227 npm34MIT

Parlayofficial
AlicenseNot gradedqualityBmaintenanceRemote MCP server for prediction markets — search and compare live odds across Polymarket, Kalshi, and Limitless from Claude, ChatGPT, or Gemini. Six read-only tools, free tier available.6MIT- AlicenseAqualityAmaintenance17 decision intelligence algorithms as MCP tools for AI agents. Bandits (UCB1, Thompson), LP/MIP solver (HiGHS), Monte Carlo simulation, Bayesian inference, graph analytics (PageRank, Louvain), genetic algorithms, CMA-ES, anomaly detection, time series forecasting, and more. All under 25ms, deterministic, zero LLM cost.1713MIT
- AlicenseAqualityCmaintenance29 MCP tools for IPL 2026, IPL historical (18 seasons), and Major League Cricket. Free, no API key.5767 npm2MIT
TDQS
Scored across 46 tools
Most tools are scoped by sport and action, but several clusters overlap meaningfully: f1_head_to_head_pace vs f1_race_pace_compare both compare two drivers' pace (the latter adding degradation, which f1_tyre_degradation also models), and football_xg_model vs football_match_predictor both output W/D/L probabilities. The four football tournament-simulation tools (simulate_group, simulate_bracket, knockout_path, what_if) are distinguishable but require careful reading of descriptions, and the fantasy trio (dream11_team, captain_recommendation, differential_picks) shares nearly identical inputs.
A predictable sport-prefixed convention runs throughout (football_*, cricket_*, f1_*, cross_sport_*), usually verb_noun like football_get_fixtures or cricket_build_dream11_team. A few outliers use noun-only forms (football_xg_model, f1_undercut_window, cricket_player_form_index, f1_weather_strategy_impact), but the prefixing keeps the set readable.
46 tools across four domains is heavy; per-sport sub-surfaces are reasonable, but the combined set is well past the 15-tool guideline. Low-value or stub entries (cricket_find_value_bets that always returns [], football_get_odds that is normally empty) inflate the count without adding capability.
Core lifecycle coverage exists per sport (fixtures, standings, squads, scorecards, sessions, results, simulations, odds). However several tools are effectively dead ends — cricket_find_value_bets always returns no bets, football_get_squad serves an empty seed without a key, and football_get_match_stats demands an opaque numeric team id with no key fallback — leaving agents without paths for real cricket betting edges or offline football stats.