"Strava" matching MCP servers:

A read-only local MCP server for querying your own Strava data from Codex, providing tools for activities, athlete profile, stats, gear, and more.

Privacy-first MCP server for Strava activities, streams, routes and training data.

Enables Claude to access your personal Strava data read-only, including activities, splits, heart-rate and pace streams, zones, and lifetime totals, so it can coach from real training without manual exports.

Enables AI agents to interact with the Strava API to retrieve athlete statistics and activity data. It provides tools for listing recent activities and fetching detailed information for specific workout sessions.

Connects Claude or any MCP client to the Strava API v3, enabling access to fitness data such as activities, athletes, clubs, segments, routes, and streams.

Enables querying and analyzing sports activities from Strava and Garmin Connect, including sleep and recovery data.

MCP server for local fitness-data extraction and analysis from Garmin Connect, Intervals.icu, and Strava. Provides read-only analytical tools over DuckDB and targeted Strava enrichment.

Provides read-only access to Oura ring biometrics via the Oura API, enabling Claude to query daily summaries, sleep, readiness, stress, workouts, baselines, and heart rate data. Designed to complement a Strava connector for joint analysis of training and recovery.

Strata is a native MCP server that gives agents verified intelligence across 22 AI ecosystems. Tools: * get_best_practices * get_latest_news * get_top_integrations * search_ecosystem * find_mcp_servers * Claude-validated content, injection-safe by design * Refreshed twice daily via automated pipeline * Works with Claude Code, Cursor, Windsurf, Cline * Free tier available Site: https://usestrat

Enables MCP-compatible agents to access live Strata markets and Sonar pricing for real-time quotes and market data.

An AI-powered running coach that connects Claude to your Strava data, providing personalized training plans, progress tracking, and coaching feedback through a web dashboard.

Enables AI agents to manage hierarchical memory with Markdown-based storage, tiered architecture (L0-L3), and hybrid retrieval for transparent and persistent context.

Enables AI agents to analyze LookML repositories for dead code, PDT costs, schema drift, and migration impact, using offline-first analysis of Looker and BigQuery metadata.