claudex-strava-mcp
Related Servers
Alternatives to claudex-strava-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityDmaintenanceConnects Claude to your Strava account so you can query your activities, stats, routes, and segments using natural language.277 npm1MIT
- AlicenseAqualityDmaintenanceConnects Claude to your Strava account for analyzing training, predicting race times, and generating periodized training plans via natural language.13182 npmISC
- AlicenseBqualityDmaintenanceConnects Claude to the Strava API to provide direct access to fitness data, including athlete statistics, detailed activity logs, and time-series performance metrics. It enables users to analyze training progress, compare workouts, and retrieve specific segment details through natural language queries.8182 npmISC
- FlicenseNot gradedqualityDmaintenanceEnables Claude to access and analyze your Strava activities through OAuth authentication. Supports retrieving activity lists and detailed workout data for fitness tracking and analysis.2-
- AlicenseNot gradedqualityCmaintenanceConnects Claude to Strava data for natural language queries about rides, stats, and activities.11 npmMIT
- AlicenseAqualityDmaintenanceConnects Strava training data to Claude, enabling personalized coaching through analysis of training load, workout planning, gear maintenance, and power metrics.1023 PyPIMIT
TDQS
Scored across 8 tools
Each tool targets a distinct resource/action: athlete profile, stats, activities (list and detail), activity laps, activity zones, routes (list and detail). No two tools overlap in purpose, and descriptions clearly differentiate list-level summaries from detail-level data.
All tool names follow a consistent verb_noun pattern: 'get_' for single resources and 'list_' for collections. CamelCase is not used, and the pattern is uniformly applied across all eight tools.
Eight tools is well-scoped for a Strava data retrieval server. Each tool covers a distinct aspect of athlete, activity, or route data, and the count is neither too small for utility nor too large for coherence.
The domain appears to be read-only athlete data retrieval. Core areas are covered: profile, stats, activities, activity details (laps, zones), and routes. Minor gaps exist, such as activity streams or segment efforts, but these are not essential for the apparent primary purpose.