"How to check console logs and network activity" 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.

Enables read-only access to Sleeper fantasy sports data, including users, leagues, rosters, matchups, transactions, drafts, players, trending activity, playoff brackets, traded picks, and sport state via MCP tools.

Enables AI assistants to directly access and analyze Strava activity data, including runs, rides, and swims, through natural language queries.

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.

Provides AI assistants with commissioner-focused tools for fantasy sports leagues, enabling league overviews, standings, transactions, recaps, and manager activity across providers like Yahoo Fantasy.

Enables AI assistants to query workouts, activity history, training load, VO2Max progression, recovery metrics, and social feeds from Sports Tracker.

Enables MCP clients to interact with the Strava REST API through tools generated from Strava's official Swagger spec, supporting OAuth, activity and athlete data retrieval, and configurable write/delete operations.

An MCP server that integrates with the Playtomic API to find nearby padel courts, check availability, compare prices, and set alerts for preferred slots.

Provides Formula 1 data via the Ergast API, enabling users to check current standings, get race results, view the season schedule, and look up driver profiles.

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.

Running-first MCP server for COROS that allows authoring, editing, and scheduling running workouts, plus accessing sleep, HRV, training load, and activity exports.

Enables agents to read and analyze personal Strava cardio data, including activity summaries, detailed activities, laps, heart-rate and power zones, streams, and aggregate statistics, through the official Strava API with read-only permissions.

Enables AI agents to discover, browse, and trade structured sports tokens on the SPRTX network via JSON-RPC 2.0 over Streamable HTTP.

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

Enables AI assistants to access your Xingzhe cycling data through the Model Context Protocol, allowing you to review rides, compare cadence across sessions, and look back at months of cycling activity.

An MCP server that connects Claude to intervals.icu training data, enabling advanced physiological analysis, activity and wellness management, and conversational insights using proprietary metrics like CCI and HRV correction.

MCP server for analyzing running data from Intervals.icu, offering tools for athlete profiles, activity details, fitness trends, and running summaries.

Enables AI agents to verify World Cup match evidence, check settlement readiness, purchase proof packets, and anchor results on Injective EVM testnet.

Automates tennis court bookings on San Francisco Recreation websites using browser automation, with tools to check availability, initiate booking with SMS verification, and complete bookings.

Enables AI assistants to retrieve and display Strava activity data including workout details, distance, time, and dates. Currently uses sample data but can be extended for real Strava API integration.