"A method to allow vision models to process and understand image files" matching MCP servers:

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.

Provides AI-powered sports analytics for Daily Fantasy Sports (DFS) with real-time player projections, lineup optimization, live odds aggregation from multiple sportsbooks, and SHAP-based explainability to understand recommendation reasoning.

A physics-aware inline skating combo generator that models moves as state transitions to ensure sequences are executable and flow naturally. It allows users to generate custom skating routines and browse a catalog of wizard-style tricks.

AI-powered golf swing analysis. Submit a swing image URL and receive scored metrics, flaws, and personalised drill recommendations

Enables AI models to manage and query fantasy sports leagues through the Sleeper API, supporting tasks like player lookups, league activity, and draft management.

Classifies Bowman Prospects baseball cards using image recognition, provides player statistics and market pricing data to determine card value.

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.

MCP server that gives Claude programmatic access to a local En Croissant chess opening trainer, enabling conversational opening prep by reading its SQLite database, PGN files, and Lichess API. It supports repertoire review, due cards, and opponent statistics directly from chat.

Provides a tool to retrieve today's 2026 FIFA World Cup matches, including teams, time, venue, and scores.

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.

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.

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.

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.

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.

Provides structured access to a World Cup SQLite database via MCP tools, enabling queries for tournaments, team histories, head-to-head matches, and player records.

FastMCP server for Dota 2 match analysis using replay files and OpenDota API, with built-in coaching instructions for LLMs.

A unified MCP server that combines The Odds API and SportMonks Football API v3 to provide 28 tools for sports data like odds, scores, fixtures, standings, and predictions, enabling Claude to interact with a wide range of sports information.

Provides natural language access to IPL cricket match data, allowing users to query player statistics, team performances, and match results. It utilizes a SQLite backend and Cricsheet data to deliver detailed cricket analytics through the Model Context Protocol.

A Model Context Protocol server for the Sportmonks Football API v3, providing over 150 tools to access football data including fixtures, teams, players, odds, predictions, and more.

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