"Understanding Sequential Thinking in Server Systems" matching MCP servers:

Enables LLMs to accurately convert climbing grades across different rating systems using a data-driven approach.

An MCP server that lets Claude plan cycling training in intervals.icu, upload workouts to the calendar, and review planned versus actual outcomes, fitness trends, and power curves.

An MCP server that lets an LLM query a DuckDB warehouse of EuroLeague and EuroCup basketball data in natural language, offering tools for player and team stats, shot charts, boxscores, and fantasy draft analysis without contacting upstream APIs.

Provides Claude Code and Claude Desktop with safe, read-only access to your local Garmin training data, readiness decisions, run details, and trends so you can get coaching advice in natural language.

Four utilities in one server: PDF text, ruled tables and OCR from a URL as pages, documents or RAG chunks; YouTube thumbnail URLs and files with per-size availability; sports scores, schedules and standings across 33 leagues; and ATP/WTA tennis with per-set scores and draws.

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.

A local MCP server that gives Claude (or any MCP-compatible AI client) access to Formula 1 race data. Load any session from 2018 onwards, ask questions in natural language, and get answers backed by real telemetry, timing, and strategy data.

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.

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.

Allows Claude to access French Chess Federation public data in conversation, including player search, department tournament listings, tournament details, rankings, and round-by-round player results.

NFL fantasy football data over MCP. Compare any two players head to head, pull position rankings in PPR, half-PPR or standard, check positional scarcity and bye-week conflicts.

Enables AI assistants to read your SisRUN training plan with your own credentials and answer questions about prescribed workouts, like weekly coach plans, in a structured format.

Enables AI assistants to query MyFantasyLeague rosters, standings, trades, free agents, and draft picks across private, dynasty, and IDP leagues in read-only mode. It supports multiple leagues and current or previous seasons without making changes in MFL.

Enables AI assistants to monitor a Sleeper fantasy football draft in real time by retrieving draft picks, available players, current turn, and roster needs through Sleeper's public keyless API.

Enables AI agents to query Matrix / Johnson Fitness ride history, exposing per-10-second power, cadence, resistance and heart-rate samples plus cross-ride views like power curves, training volume, and ride comparisons. Runs over a cached history downloaded without credentials, or signs in once to refresh it.

MCP server enabling AI agents to play fantasy football on Solana through Fantopy Arena, with tools for contests, lineups, and payouts.

Enables querying composite NFL questions by requiring explicit definitions for ambiguous terms, rejecting unresolved ones and grounding answers in chosen definitions.

Enables Claude, ChatGPT and other MCP clients to look up members, their memberships and credentials, events and event bookings, and clubs and other organisations in a sports membership platform, and, when writes are enabled, to change the dates of a member's membership.

Gives Claude Desktop read access to an ESPN Fantasy Football league, exposing standings, rosters, matchups, free agents, trades, and schedules so users can ask about their league in natural language. Adds purpose-built tools for trade targeting, roster-improvement analysis, and projection-based start/sit advice.

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.