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Related Servers

Alternatives to SportIQ

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
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      maintenance
      MCP 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.
      12
      MIT
    • A
      license
      A
      quality
      C
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      MCP 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.
      18
      227 npm
      34
      MIT
    • A
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      Not graded
      quality
      B
      maintenance
      Remote 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.
      6
      MIT
    • A
      license
      A
      quality
      A
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      17 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.
      17
      13
      MIT

    TDQS

    A3.5/5.0

    Scored across 46 tools

    Disambiguation3/5

    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.

    Naming Consistency4/5

    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.

    Tool Count2/5

    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.

    Completeness3/5

    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.

    Maintenance

    ActivityActive
    ResponsivenessResponsive