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    This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.
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    MIT
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    An MCP server that integrates with the Playtomic API to find nearby padel courts, check availability, compare prices, and set alerts for preferred slots.
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    Enables Claude to access and analyze Suunto sports watch data, allowing users to ask natural-language questions about their workouts, training metrics, and history.
    MIT
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    A server for querying surf spot data and searching spots, with tools to list regions, spots, get spot info, find spots by criteria, and get mock forecasts.
    MIT
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    An HTTP broker that exposes Strava data and operations (activities, routes, segments, etc.) as MCP tools, allowing users to interact with their own Strava account via natural language using per-user OAuth2 authentication.
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    An MCP server that answers football questions with interactive widgets, centered on the 2026 FIFA World Cup using SofaScore API data.
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    Provides surf forecast data including swell height, period, direction, and wind conditions for any location worldwide using the Open-Meteo Marine API. It also includes tools to find the best day to surf and integrates with MCP clients via a Python server.
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    Provides access to real-time data from the French Basketball Federation (FFBB), including live scores, standings, and competition details. It allows users to search for clubs, matches, and venues using natural language without requiring an API key.
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    Apache 2.0
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    Searchable football data provider documentation for AI coding agents. Enables agents to look up verified docs on event types, qualifier IDs, coordinate systems, and more across 15 providers.
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    MIT
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    Tells you when your favourite football teams play and where to watch in Denmark, using data from TheSportsDB.com and built-in Danish broadcast hints.
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    MIT
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    Enables AI agents to query live sports betting data including odds, edges, arbitrage opportunities, player/team stats, and reference data using the SportWizzard API.
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    MIT
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    Enables users to query MLB Statcast, FanGraphs, and Baseball Reference data using natural language through an AI assistant. It provides comprehensive tools for analyzing player performance, pitch-level data, season leaderboards, and team standings.
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    MIT
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    MCP server for Parks on the Air (POTA) providing live activator spots, park details, activator/hunter stats, and scheduled activations through any MCP-compatible AI assistant.
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    GPL 3.0
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    Provides access to KenPom basketball analytics through MCP tools. Enables querying ratings, efficiency, player stats, and more using natural language.
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    MIT
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    Provides AI-powered sports betting intelligence including live odds, injury reports, and documented picks for NBA, NHL, and NCAAB. It enables AI agents to analyze line movements, win rates, and betting edges using real-time data from sportsbettingaianalyzer.com.
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    MIT
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    Provides comprehensive running performance calculations including VDOT, training paces, race time predictions, velocity markers, and heart rate zones using Jack Daniels, Greg McMillan, and Riegel methodologies.
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    MIT
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    Provides soccer match predictions and league statistics using xG data and Poisson distribution models. It enables users to forecast outcomes, analyze team performance, and view league tables across major European football leagues.
    3
    GPL 2.0