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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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    Provides read-only access to the Sleeper Fantasy Sports API for league info, rosters, matchups, drafts, transactions, and player data.
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    The Blue Alliance MCP server using its API for FIRST Robotics Competition data. Retrieve comprehensive FRC team, event, and match information.
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    Provides access to Formula 1 data including driver and constructor championship standings with support for current and historical seasons. Enables users to query F1 championship information through natural language with plans for expanded race data and telemetry.
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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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    Enables AI assistants to access World Cube Association speedcubing data including world records, competitor profiles, competition information, and championship results. Supports queries about rankings, competition schedules, and detailed speedcubing statistics through natural language.
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    An MCP server that connects Claude Desktop to Hudl, enabling live access to team stats, player stats, and game results through natural language queries.
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    Read/write MCP server for Intervals.icu — 51 tools spanning activities, streams, wellness, calendar, gear, and sport zones, plus structured workout generation with a built-in syntax reference for cycling, running, and swimming.
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    Gives your MCP host (Claude Desktop, Cursor, Continue, Zed) access to live scores, match details, standings, top scorers, knockout brackets and player stats across football, basketball, cricket and tennis. Backed by the free public SportScore API — no key, no signup, CORS-open.
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    Enables AI assistants to access sports betting odds data from 265+ bookmakers across 34 sports, including events, odds, historical data, arbitrage, and value bets.
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    Connects Claude to TeamSnap accounts to access teams, rosters, events, and availability data. Supports OAuth 2.0 authentication with optional AWS serverless deployment for permanent HTTPS callback URLs.
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    A Model Context Protocol server that provides comprehensive Formula One racing data, enabling access to event schedules, driver information, telemetry data, race results, and performance analytics through natural language queries.
    Last updated
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    Connects Claude to the SportRadar MLB API to access real-time baseball data including game schedules, live scores, player statistics, team standings, injury reports, and play-by-play information through natural language queries.
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    Provides Formula One data and statistics through a Model Context Protocol interface, allowing users to access race calendars, session results, driver statistics, telemetry data, and championship standings.
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    Enables MCP clients to access live football data from FotMob, including match stats, team form, injuries, and player workload, without making predictions.
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    Provides access to live NHL game data, player and team statistics, standings, schedules, playoff brackets, and historical comparisons through natural language queries using the official NHL API.
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    Enables access to MLB Stats API data including game schedules, results, team information, and player lookups with fuzzy matching support through the Model Context Protocol.
    Last updated
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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.
    Last updated
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    Provides comprehensive Formula 1 data access including race schedules, session results, lap times, telemetry data, driver/constructor standings, and circuit information. Enables users to retrieve and analyze F1 racing data through natural language queries using the FastF1 Python package.
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    MIT