mcp-strava
Related Servers
Alternatives to mcp-strava
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
- 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
- AlicenseNot gradedqualityCmaintenanceConnects Claude to Strava data for natural language queries about rides, stats, and activities.19 npmMIT
- FlicenseNot gradedqualityDmaintenanceExposes a single tool for Claude to fetch recent Strava activities, including detailed metrics and lap breakdowns, for automatic training summaries.-
- FlicenseAqualityDmaintenanceConnects Claude to your Strava fitness data, enabling natural language queries about your training history such as activities, stats, and routes.8-
- 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 has a clearly distinct purpose, covering activities, zones, athlete info, segments, and listing. Descriptions provide detailed distinctions, e.g., get_activity_zones vs get_athlete_zones are for activity-specific vs athlete-configured zones.
Almost all tools follow a consistent verb_noun pattern with snake_case (get_*, list_*). 'ping' is a slight outlier but is a common test tool and does not confuse the overall pattern.
8 tools is appropriate for a Strava integration, covering core read operations for activities, athletes, zones, and segments without being overly numerous or sparse.
The tool set covers main read operations, but there is a notable gap: get_activity_detail mentions using get_activity_streams for time-series data, yet that tool is not present. This creates a dead-end for agents. Missing write operations like upload or update activities, but those may be out of scope.