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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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    Enables users to interact with the Griptonite indoor climbing app, allowing them to log climbs, browse routes and grades, and retrieve activity feeds and rankings.
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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 triathlon coaches and athletes to interact with Garmin Connect, including retrieving health/activity data, building and uploading structured workouts (cycling, running, swimming, brick), and accessing coaching analytics like readiness, load, and performance trends.
    MIT
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    Enables AI models to manage and query fantasy sports leagues through the Sleeper API, supporting tasks like player lookups, league activity, and draft management.
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    MIT
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    Provides MCP servers for Claude to access fitness data from Strava and intervals.icu, enabling natural language queries for activity analysis, advanced training metrics, and wellness tracking.
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    MCP server for Bosch eBike Flow (Smart System / BES3). Read-only access to battery health and charge-cycle trends, components and firmware, live state-of-charge, and per-ride activity data (distance, elevation, power, assist-mode, CO2), signing in with your own Bosch account via PKCE.
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    GPL 3.0
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    Provides access to Fantasy NBA Israel League statistics including team rankings, player stats, roster details, and shooting analytics for a specific private fantasy basketball league.
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    A lightweight Garmin Model Context Protocol server with 12 curated endpoints for endurance athletes, enabling natural-language queries of activity, training, health, device, and goal data from Garmin Connect.
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    MIT
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    Python MCP server that provides comprehensive access to MLB statistics and baseball data through a FastAPI-based interface. Acts as a bridge between AI applications and MLB data sources, enabling seamless integration of baseball statistics, game information, player data, and more.
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    Provides AI agents with access to football match predictions for Premier League and Champions League, including half-time/full-time scores, goal scorers, corner counts, and key player insights.
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    MIT