✨ RECENTLY ADDED
APIs, MCP servers, and directories added to the Developers Locker Room in the last 30 days.
Racquet & Net Sports
Python project for ATP tennis rankings data collection, analysis, visualization, and AI integration via MCP.
Multi-Sport
Official MCP server for BALLDONTLIE covering NBA, WNBA, NFL, MLB, NHL, EPL, NCAAF, NCAAB, MMA, CS2, League of Legends, Dota 2, FIFA World Cup 2026, La Liga, Serie A, Champions League, Bundesliga, and Ligue 1. Supports 200+ data endpoints across 15 leagues. Can be used as hosted server (mcp.balldontlie.io) or run locally. Requires BALLDONTLIE API key.
Venue Operations
FastMCP server for campus sports court reservation and cancellation automation, exposing reservation and cancellation tools for AI agents and MCP clients.
Ice Hockey (NHL)
MCP server that provides player and club statistics for EA Sports NHL CHEL (EASHL) by querying EA's Pro Clubs API.
Fan Engagement
MCP server that integrates with the official Fantasy Premier League API to retrieve player statistics, team information, fixture data, and league standings. Enables natural language queries against live FPL data for fantasy football analysis and transfer decisions. Built in Node.js/TypeScript, installable via npm.
Fan Engagement
Python-based MCP server providing rich Fantasy Premier League data access including player search, player comparisons, gameweek data, team info, and fixture analysis. Requires Python 3.10+. Exposes FPL data as MCP tools for Claude and compatible clients.
Wearables & Health
TypeScript MCP server that integrates with Fitbit's API to provide access to personal health data including weight measurements, sleep logs, exercise logs, activity summaries, heart rate, food/nutrition logging, and Active Zone Minutes. Uses OAuth 2.0 authentication. Available as mcp-fitbit npm package. 12 tools, 26+ GitHub stars.
Wearables & Health
Integrates with Garmin Connect to expose fitness and health data through natural language interactions. Provides 96+ tools covering activities, health/wellness metrics, training data, workout management, device info, gear tracking, weight logging, nutrition data, and more. Supports Docker deployment. 311+ GitHub stars.
Sports Betting & Odds
Spring Boot REST API for managing sports matches and their betting odds with business validations.
Baseball (MLB)
MCP server providing structured access to Major League Baseball advanced statistics including Statcast, FanGraphs, Baseball Reference, and official MLB Stats API data. Enables AI agents to query detailed baseball analytics, player performance metrics, and historical data through a standardized MCP interface.
Basketball
Model Context Protocol server for NBA player statistics built with FastMCP and sourced from basketball-reference.com.
Esports
Official OP.GG MCP server for esports data. OP.GG is a leading esports data platform covering League of Legends, CS2, Valorant, and other competitive titles. Provides player rankings, match history, champion/agent statistics, and competitive meta analysis through a standardized MCP interface.
Esports
MCP server integrating with the OpenDota API to provide real-time Dota 2 statistics, match data, and player information for AI agents. Endpoints include player lookup by account ID, recent matches, detailed match data, win/loss stats, and most-played heroes. Uses free OpenDota public API — no API key required.
Sports Betting & Odds
Premier real-time sports betting odds API processing 1M+ odds per second from 200+ sportsbooks. Covers pre-match, in-play, futures, player props, and same-game parlays. Sub-800ms delivery. Sports include NFL, NBA, MLB, NHL, soccer, tennis, cricket, golf, and esports. Features automated bet settlement, real-time line movement alerts, and injury data. Returns structured JSON or XML.
Sports Betting & Odds
Serverless Python data pipeline that parses API feeds for real-time market inefficiencies and triggers automated webhook alerts.
Multi-Sport
Web API for sports statistics with hierarchy: sports → leagues/tournaments → teams → players → matches, plus betting odds management.
Wearables & Health
MCP server facilitating seamless integration between Strava APIs and Claude for Desktop. Enables AI agents to retrieve and analyze athlete fitness data including running, cycling, and other endurance activities, segments, routes, and club data. Requires Strava API key.
Multi-Sport
Budget-friendly multi-sport API on RapidAPI with 25+ endpoints covering 20+ sports and 5100+ leagues. Covers soccer, basketball, ice hockey, American football, handball, baseball, and more. Positioned as the cheapest sports API for hobby and business projects. Also offers a companion Sports Bet Odds API on RapidAPI.
🔗 Multi-Sport
Fast and reliable multi-sport livescore API covering soccer, basketball, hockey, baseball, and American football. Provides livescore feeds, lineups, standings, live stats, and fixtures from major worldwide leagues in an easy-to-integrate JSON format. Also available on RapidAPI.
Multi-Sport
Browse and subscribe to APIs on APILayer’s marketplace across categories (including Sports).
Basketball
APILayer marketplace listing for balldontlie NBA data API.
American Football (NFL)
Smithery-listed MCP server for querying balldontlie sports data across major leagues.
🔗 Sports Betting & Odds
Live sports scores and betting data API with broad sport coverage including soccer, cricket, rugby union, American football, tennis, baseball, ice hockey, basketball, rugby league, Australian rules, handball, futsal, volleyball, table tennis, badminton, and esports. Focuses on live score feeds combined with odds data.
Soccer / Football
Postman-hosted documentation/collection for College Football Data API.
Multi-Sport
PulseMCP listing for an MCP server providing ESPN sports data across many leagues.
Sports Media
SDK + platform API concept for capturing, syncing, and distributing short-form sports highlights/instant replay clips from live events into apps and fan experiences.
Soccer / Football
Curated list of football-related APIs and workspaces on the Postman API Network.
Soccer / Football
Postman collection/documentation for football-data.org v4 endpoints.
Sports Betting & Odds
Provides live bookmaker odds data for multiple sports/leagues, accessed via RapidAPI.
🏈 American Football (NFL)
Python MCP server for real-time NFL stats via SportRadar API. Provides game stats, team rosters, schedules, player stats, tournament info, and transaction data. Installable via pip as mcp-sports-server. Currently NFL-only with a plugin architecture designed for expansion to additional sports.
🔗 College Sports (NCAA)
Free API that returns JSON from ncaa.com paths (scores, stats, rankings, standings, schedules, history, logos, and game details like box score / play-by-play).
Multi-Sport
Explore page for discovering public APIs and workspaces on the Postman API Network.
Multi-Sport
Curated collection page aggregating sports data API listings on RapidAPI.
Ticketing & Venues
Integrates with SeatGeek’s event discovery platform to find events and generate recommendations with performer/venue/location filtering.
Soccer / Football
Provides live, finished, and pending soccer match information via RapidAPI.
🔗 Sports Betting & Odds
Sports API provider offering real-time data, odds, stats, and scores across 40+ sports including cricket and smaller leagues. Designed for building betting, fantasy, and analytics applications. Transparent pricing with no hidden fees. Positions as an alternative to SportsDataIO, OpticOdds, and similar platforms.
🔗 Multi-Sport
Multi-language RESTful APIs and WebSockets for sports data, designed for ultra-low latency and hyper-granular stats. Covers esports (CS2, LoL, Dota2, Valorant, FIFA), plus other major sports. Provides events, livescores, players, teams, predictions, odds, leagues, seasons, stats, lineups, fixtures, injuries, TV channels, news, and standings via both REST and WebSocket.
Sports Betting & Odds
Public Postman workspace for Sportmonks APIs and collections.
Multi-Sport
Postman-hosted documentation for a sports API, usable via Postman collections.
Scores & Stats
PulseMCP listing for nexgendata sports MCP server.
American Football (NFL)
Smithery-listed MCP server for retrieving major US league scores and stats (via Apify).
Sports Betting & Odds
Provides NFL in-game real-time statistics and related fantasy/odds endpoints via RapidAPI.
Sports Betting & Odds
Postman workspace and documentation for The Odds API (sports betting odds).
Sports Betting & Odds
APILayer marketplace listing for TheRundown, providing odds/scores/stats for sports betting use cases.
Ticketing & Venues
Connects to Ticketmaster Discovery API to find upcoming events by artist, venue, location, or genre with ticket availability information.
Ticketing & Venues
Connects to Ticketmaster APIs to search and discover upcoming events by location, date range, and keywords, returning detailed event info and ticket links.
🔗 Fantasy Sports
Integrates with Sleeper Fantasy Football API to access user profiles, league data, rosters, matchups, draft info, player stats, and trending analytics.
🔗 Sports Betting & Odds
AI-powered sports betting picks, odds, injury reports, and line movement analysis for NBA, NHL, and NCAAB.
📖 Multi-Sport
**By Andy Abramson, CEO, Comunicano · April 2026** --- The sports technology infrastructure market has reached an inflection point. What was once a fragmented collection of proprietary data feeds, closed vendor ecosystems, and hand-rolled integrations has evolved into a rich, interconnected layer of APIs, MCP Servers, and open datasets that any developer — from a solo indie builder to a Fortune 500 engineering team — can access and build upon today. The Developers Locker Room now catalogs **332 tools** across 187 REST and GraphQL APIs, 119 MCP Servers, 10 Directories, 9 Editorial resources, and 7 open Datasets, spanning 32 categories and 28 sport groups. That number has grown from 161 at launch to 332 in fewer than twelve months — a rate that reflects both the genuine explosion of sports-tech infrastructure and the maturation of developer tooling across the industry. This guide is a practitioner's map of that landscape. It covers the five resource types that define the modern sports-tech stack, the explosive growth of MCP Servers as an AI-native integration pattern, the open datasets gap, authentication patterns, pricing models, and what to watch in the second half of 2026. --- ## The Five Layers of the Sports-Tech Stack Modern sports-tech infrastructure is not a single category — it is a stack. Understanding the five distinct layers helps developers choose the right tool for each job and avoid the common mistake of treating every integration as a REST API problem. ### REST and GraphQL APIs With **187 APIs** cataloged, the traditional REST and GraphQL layer remains the backbone of sports-tech integration. These range from real-time score feeds and betting odds providers to venue operations platforms, athlete biometric systems, and broadcast metadata services. The breadth is striking: a developer building a fantasy sports app, a stadium operations dashboard, or a sports betting analytics tool will find purpose-built APIs for each use case. The most densely populated categories reflect where commercial demand is highest. **Multi-Sport** leads with 46 entries — aggregators and cross-sport platforms that abstract away the complexity of dealing with individual league data contracts. **Venue Operations** follows with 39 entries, a category that has grown significantly as smart-stadium infrastructure (access control, concessions, ticketing, Wi-Fi analytics) has moved from proprietary hardware to API-first platforms. **Broadcasting and Streaming** (37 entries) and **Sports Betting and Odds** (35 entries) round out the top four, both reflecting industries where real-time data is a direct revenue input rather than a nice-to-have. **Soccer/Football** is the largest single-sport category at 28 entries — a function of the sport's global reach and the corresponding depth of its data ecosystem, from Opta and StatsBomb to dozens of regional league APIs. **Scores and Stats** (23 entries) and **Fantasy Sports** (18 entries) follow, the latter driven by the DFS industry's insatiable appetite for player-level performance data. ### MCP Servers: The AI-Native Integration Layer The most significant structural shift in sports-tech infrastructure over the past eighteen months has been the emergence of **Model Context Protocol (MCP) Servers** as a first-class integration pattern. The Developers Locker Room now catalogs **119 MCP Servers** — more than any other resource type after traditional APIs — and that number is growing faster than any other category. MCP Servers are not simply APIs with a new name. They represent a fundamentally different integration philosophy: rather than requiring a developer to write client code that calls an endpoint, parses a response, and manages state, an MCP Server exposes structured tools that an AI agent or large language model can invoke directly. The protocol standardizes how context is passed, how tools are described, and how results are returned — dramatically reducing the integration surface area for AI-native applications. For sports developers, this matters enormously. A developer building an AI-powered game-day assistant no longer needs to write custom code to query a score API, format the response, and inject it into a prompt. An MCP Server for that score provider handles the entire interaction through a single, standardized interface. The same pattern applies to injury reports, betting lines, venue capacity data, and broadcast schedules. The 119 MCP Servers cataloged span every major sport and use case. Multi-Sport aggregators dominate, followed by dedicated servers for the NFL, NBA, MLB, NHL, and soccer. Venue operations and athlete performance are emerging categories where MCP adoption is accelerating, driven by the growing use of AI copilots in stadium control rooms and sports medicine facilities. ### Open Datasets: The Training Data Layer The **7 open datasets** cataloged at Developers Locker Room represent a category that is simultaneously the most underserved and the most strategically important in the sports-tech ecosystem. These are free, downloadable collections of historical event data, player statistics, and game logs — requiring no authentication, no API key, and no commercial agreement. The current catalog includes Cricsheet (ball-by-ball cricket data), nflverse (the R-native NFL data ecosystem), OpenFootball (historical soccer results), Basketball-Reference exports, FBref (football statistics), SCORE Network (multi-sport academic data), and StatsBomb Open Data (detailed soccer event data). Each of these has become a foundational resource for AI model training, academic sports analytics research, and the development of new commercial products. The gap between what is available and what is needed is significant. The major North American leagues — NFL, NBA, MLB, NHL — do not publish open historical datasets at the granularity that AI model training requires. Developers working on predictive models, injury risk systems, or generative sports content are forced to either license expensive commercial feeds or work with the limited open data that exists. This is the single biggest infrastructure gap in the sports-tech stack, and it represents a significant opportunity for any league or data provider willing to move first. ### Directories: The Meta-Layer The **10 directories** cataloged are curated aggregators and meta-resources — sites and projects that themselves catalog sports data resources. These are valuable precisely because the sports-tech landscape is fragmented: no single source covers everything, and developers benefit from knowing which aggregators are authoritative for which domains. Directories serve a different function than APIs or datasets. They are discovery tools, not integration tools. A developer starting a new project will often begin with a directory to understand the landscape before committing to a specific provider. The Developers Locker Room itself functions as a directory of directories in this sense — a meta-layer that surfaces the best aggregators alongside the primary sources. ### Editorial: The Knowledge Layer The **9 editorial resources** cataloged — including this guide — represent the knowledge layer of the sports-tech stack: guides, tutorials, roundups, and in-depth analyses that help developers make informed integration decisions. Good editorial content is not marketing copy; it is practitioner knowledge that saves hours of research and prevents costly integration mistakes. The editorial category is the newest and smallest in the catalog, but it is growing. As the sports-tech ecosystem matures, the demand for authoritative, opinionated guidance — which API to use for NHL real-time data, which MCP Server has the best documentation, which open dataset is most suitable for soccer xG modeling — will only increase. --- ## Authentication Patterns: What to Expect One of the most practically useful things a developer can know before evaluating a sports-tech API is its authentication model. The catalog reveals four dominant patterns, each with different implications for integration complexity, security posture, and operational overhead. **API Key authentication** is the most common pattern across the catalog. It is simple to implement, easy to rotate, and well-understood by every developer. The downside is that API keys are often long-lived credentials that require careful secret management — particularly in client-side or mobile applications where embedding a key in the binary is a security risk. **OAuth 2.0** is the standard for APIs that require user-level authorization — fantasy sports platforms, athlete-facing performance tools, and any service that accesses personal data. The integration complexity is higher, but the security model is significantly stronger, and the pattern is well-supported by every major language ecosystem. **JWT (JSON Web Token)** authentication appears in a growing number of sports-tech APIs, particularly those built on modern serverless infrastructure. JWTs are stateless, which makes them well-suited for high-throughput real-time data feeds where session management overhead would be prohibitive. **Open access** — no authentication required — is the model for all seven open datasets in the catalog and a small number of public APIs. This is the ideal model for developer onboarding and AI training data, but it is commercially untenable for real-time or proprietary data. --- ## Pricing Models: The Commercial Landscape The sports-tech API market has not converged on a single pricing model, and the diversity of approaches reflects the diversity of use cases and customer segments. **Freemium** is the dominant model for developer-facing APIs. A free tier with rate limits allows developers to build and test integrations without a commercial commitment, with paid tiers unlocking higher throughput, additional endpoints, or premium data. This model has driven adoption across the catalog and is the primary reason the sports-tech API ecosystem has grown as quickly as it has. **Subscription** pricing — a fixed monthly or annual fee for a defined access level — is common among enterprise-grade data providers, particularly in the betting odds and broadcast metadata categories where data freshness and reliability carry direct commercial value. **Usage-based** pricing, where developers pay per API call or per data record, is growing in prevalence as cloud-native billing infrastructure has made metered models easier to implement and explain. This model aligns provider revenue with developer success, which makes it attractive for both parties. **Free** access — no charge at any tier — applies to the open datasets and a subset of community-maintained APIs. These resources are typically funded by academic institutions, sports governing bodies, or open-source communities rather than commercial entities. --- ## The Growth Trajectory: From 161 to 332 in Under a Month The Developers Locker Room launched in mid-March 2026 with 161 cataloged resources. The growth to 332 by April 2026 — a 106% increase in under six weeks — reflects several converging trends. | Sprint | Total Catalogued | Net Added | |---|---|---| | Launch (Sprint 75) | 161 | — | | Sprint 76 | 180 | +19 | | Sprint 77 | 200 | +20 | | Sprint 78 | 220 | +20 | | Sprint 79 | 235 | +15 | | Sprint 80 | 242 | +7 | | Sprint 81 | 254 | +12 | | Sprint 82 | 261 | +7 | | Sprint 83 | 272 | +11 | | Sprint 84 | 293 | +21 | | Sprint 87 | 325 | +32 | | Sprint 102 (April 2026) | **332** | **+7** | The acceleration in Sprint 87 — 32 additions in a single sprint — reflects the addition of MCP Servers as a tracked resource type. Before MCP Servers were cataloged, the directory was primarily an API index. Adding MCP Servers effectively doubled the addressable catalog space overnight, and the 119 MCP Servers now in the directory represent a category that did not meaningfully exist in the sports-tech ecosystem eighteen months ago. The addition of open datasets in Sprint 91 added another dimension to the catalog — one that is growing more slowly in absolute terms but is disproportionately valuable for AI and research use cases. --- ## What to Watch in the Second Half of 2026 Several trends are worth tracking closely as the sports-tech API ecosystem continues to evolve. **MCP Server proliferation will accelerate.** Every major sports data provider is evaluating or actively building MCP Server support. The pattern has proven itself in adjacent domains — developer tools, enterprise software, financial data — and the sports-tech market is following the same adoption curve. By the end of 2026, it is reasonable to expect the MCP Server count in the catalog to exceed 200. **League-owned data platforms are maturing.** The NFL, NBA, MLB, and NHL have all made significant investments in their own data infrastructure over the past three years. The question is whether they will open that infrastructure to third-party developers through public APIs or keep it proprietary. The pressure from the betting, fantasy, and media industries to open access is significant, and at least one major league is likely to announce a public API program before the end of the year. **AI-native sports applications are moving from prototype to production.** The combination of MCP Servers, large language models, and real-time sports data feeds has made it possible to build AI-powered sports applications — game-day assistants, injury risk tools, broadcast commentary aids, betting analytics copilots — that would have required a team of engineers and months of integration work two years ago. The infrastructure is now mature enough for production deployment, and the first wave of commercially successful AI-native sports applications is beginning to emerge. **Open dataset gaps will create commercial opportunities.** The absence of high-quality, open historical data for the major North American leagues is a structural gap that will not be filled by the leagues themselves in the near term. This creates an opportunity for commercial data providers to offer affordable, developer-friendly historical datasets that bridge the gap between the open-access world and the enterprise licensing model. The first provider to offer a credible, affordable, developer-friendly historical dataset for NFL play-by-play data at the granularity of nflverse will capture significant market share. **Venue operations APIs will converge.** The venue operations category — currently 39 entries, the second largest in the catalog — is characterized by fragmentation. Dozens of point solutions cover access control, concessions, parking, Wi-Fi, and fan engagement separately. The next phase of venue technology will be integration: platforms that aggregate these point solutions behind a single API surface. Several well-funded startups are building in this space, and the first credible venue operations aggregator API is likely to emerge in 2026. --- ## How to Use the Developers Locker Room The Developers Locker Room is designed to be the first stop for any developer evaluating sports-tech integrations. The catalog is searchable by name, description, category, and tags, and filterable by resource type (API, MCP Server, Directory, Editorial, Dataset), sport group, and MCP support status. Each entry includes a description, website link, documentation URL, authentication type, pricing model, supported data formats, and sport coverage. MCP Server entries include the server endpoint and protocol version. Dataset entries include download links and data format specifications. The **Categories** section provides dedicated landing pages for each of the 32 sport categories, with all relevant resources surfaced in a single view. The **MCP Servers** page provides a dedicated index of all 119 AI-native integrations, filterable by sport and use case. The **Datasets** page surfaces all 7 open datasets with download links and use case guidance. The catalog is updated continuously. New entries are added as they are discovered or submitted by the community. The **What's New** badge flags recently added entries, and the live stat counter in the site header reflects the current catalog size in real time. --- ## Submit a Resource The Developers Locker Room is a community resource, and its value grows with every addition. If you maintain a sports-tech API, MCP Server, open dataset, or directory that is not yet in the catalog, submit it at [devlocker.dev/submit](https://devlocker.dev/submit). Submissions are reviewed and added within 48 hours. --- *Andy Abramson is CEO of Comunicano, a technology communications firm with 64 exits in 23 years totaling over $9.5 billion. He is a former sports marketing executive with the Philadelphia Flyers, Philadelphia Wings, Denver Nuggets, The Upper Deck Company, and Foote, Cone and Belding's Impact division.* *Developers Locker Room is an independent research and curation project. All data is sourced from public repositories, developer documentation, and original research.*
🔗 Wearables & Health
Analyze training activities, build training plans, and sync workouts to sports watches.
🔗 Multi-Sport
Python-based MCP server implementing the Model Context Protocol for football (soccer) statistics and live match data using the API-Football service. Provides fixtures, live scores, standings, player stats, and historical data across global leagues.
🏀 Basketball
The most comprehensive freely accessible historical basketball database covering NBA and WNBA player statistics, team records, game logs, and advanced metrics from 1946 to present. Includes box scores, play-by-play, salary data, draft history, and award records. Widely used by sports analysts, data scientists, and AI researchers.
🏏 Cricket
Freely available structured ball-by-ball data for international and T20 League cricket matches, including IPL, BBL, PSL, and all major ICC tournaments. Available in JSON, YAML, and CSV formats. Covers men's and women's internationals from 2005 onwards. No registration or API key required — direct download from cricsheet.org.
🔗 Multi-Sport
MCP server for ESPN sports data; exposes tools for live NFL/NHL/NBA standings, scores, schedules, and team information to MCP-compatible clients.
🔗 Fan Engagement
MCP server providing access to the FantasyPros API for sports data, news, expert consensus rankings, and projections. Supports NFL, MLB, NBA, and NHL. Tools include get_sport_news (with category filtering: injury, recap, transaction, rumor, breaking), player information by ID, consensus rankings by position and scoring type, and player projections.
⚽ Soccer / Football
Comprehensive football (soccer) statistics database covering player, team, and league stats across the top 5 European leagues and major international competitions. Powered by StatsBomb data for advanced metrics including expected goals (xG), progressive passes, and pressures. Covers men's and women's competitions from 1888 onwards.
🔗 Baseball
MCP server for advanced baseball analytics, aggregating data from Statcast, FanGraphs, Baseball Reference, and the MLB Stats API. Supports AI-driven analysis of Statcast pitch/exit velocity data, advanced metrics, and cross-source stat comparisons.
⚾ Baseball (MLB)
A Model Context Protocol (MCP) server that provides comprehensive access to MLB statistics and baseball data via a FastMCP-based interface. Supports player stats, team data, schedules, standings, and historical game records through the official MLB Stats API.
🔗 Basketball
Provides detailed NBA player statistics from basketball-reference.com via specialized tools (career/season/advanced metrics, game logs, awards voting, trend analysis, all-time rankings).
🏈 American Football (NFL)
Comprehensive open NFL datasets maintained by the nflverse community. Includes play-by-play, player stats, schedules, rosters, draft picks, and advanced metrics. Available as CSV and Parquet files with a Python library (nfl_data_py). Covers 1999 to present, updated weekly during the NFL season.
🏒 Ice Hockey (NHL)
NHL API client, MCP server, and CLI written in Go. Integrates with NHL data sources to provide real-time game updates, player stats, and league standings for sports analysis and fantasy hockey applications. Based on the official NHL API.
⚽ Soccer / Football
Free, open public domain football (soccer) data in JSON format covering the English Premier League, Bundesliga, Primera División, Serie A, Ligue 1, and more. Includes match schedules, results, team and player data, and stadium information. No API key, no registration, no cost.
🔗 Multi-Sport
PulseMCP listing for Pipeworx sports MCP server and pack.
🔗 Multi-Sport
Complete documentation of ESPN's undocumented public API endpoints across 20+ sports (NFL, NBA, MLB, NHL). Includes live curl examples, routing guides, and a Django REST API service for live scores, standings, and news. Optimized for AI/LLM use.
🔗 Multi-Sport
Access live ESPN sports data across 17 sports and 139 leagues — scores, stats, rosters, betting odds, and standings without authentication. Uses public ESPN APIs to expose comprehensive multi-sport data to AI agents.
🔗 Motorsports
Open-source agent SKILL.md definitions for live sports data and prediction markets. Covers PGA/LPGA/DP World Tour golf, ATP/WTA tennis, Football (soccer), Formula 1, and prediction market integrations (Kalshi, Polymarket). Zero API keys needed — pulls from public ESPN endpoints.
🔗 Basketball
MCP server exposing the free SportScore sports-data API as tool calls for Claude, Cursor, and Zed. Provides live scores, match details, standings, top scorers, brackets, and player stats across football, basketball, cricket, and tennis. Free public API, CORS-open, no API key required.
🔗 Wearables & Health
Connects TrainingPeaks to AI assistants via cookie-based auth — no API approval needed. Query workouts, CTL/ATL/TSB fitness data, and power PRs via natural language. Supports cycling, running, and triathlon athletes.
🔗 Soccer / Football
AI companion for FIFA World Cup 2026. 18 tools covering matches, teams, venues, city guides, fan zones, visa info, head-to-head records, standings, odds, and knockout bracket. Works with Claude, ChatGPT, Cursor, and Telegram. No API key or external dependencies required — all data ships with the package.