2,500+ MCP servers ready to use
Vinkius
MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED
Strava Training

Strava Training MCP Server

Built by Vinkius GDPR ToolsFree for Subscribers

Analyze Strava activities, segments, streams (HR, power, GPS), zones, laps, and athlete stats.

Vinkius supports streamable HTTP and SSE.

AI AgentVinkius
High Security·Kill Switch·Plug and Play
Strava Training
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

What is the Strava Training MCP Server?

The Strava Training MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Strava Training via 12 tools. Analyze Strava activities, segments, streams (HR, power, GPS), zones, laps, and athlete stats. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.

Built-in capabilities (12)

get_activityget_activity_lapsget_activity_streamsget_activity_zonesget_athlete_statsget_athlete_zonesget_gearget_segmentget_segment_effortget_segment_effort_streamsget_segment_streamslist_segment_efforts

Tools for your AI Agents to operate Strava Training

Ask your AI agent "Show my athlete statistics." and get the answer without opening a single dashboard. With 12 tools connected to real Strava Training data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.

Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.

Why teams choose Vinkius

One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.

Build your own MCP Server with our secure development framework →

Vinkius works with every AI agent you already use

…and any MCP-compatible client

CursorClaudeOpenAIVS CodeCopilotGoogleLovableMistralAWSCursorClaudeOpenAIVS CodeCopilotGoogleLovableMistralAWS

Strava Training MCP Server capabilities

12 tools
get_activity

The activityId is the numeric ID from Strava activity URLs (e.g., strava.com/activities/12345678 → 12345678). Use this for deep analysis of any workout, ride, or run. Get detailed information about a specific Strava activity

get_activity_laps

Each lap includes distance, moving time, average speed, elevation gain, and pace. GPS devices and Strava auto-split activities into laps (typically ~1km or ~1mi). Use this to analyze pace consistency, identify fast/slow sections, and compare splits within a single activity. Get lap/split data for a Strava activity

get_activity_streams

The "types" parameter is comma-separated stream types: "time", "distance", "latlng", "altitude", "velocity_smooth", "heartrate", "cadence", "watts", "temp", "moving", "grade_smooth". Example: "heartrate,watts,velocity_smooth" for HR, power, and speed data. Each stream returns an array of values with corresponding timestamps. Use this for detailed performance analysis, visualization, or export. Get raw time-series data streams from a Strava activity (GPS, heart rate, power, cadence, altitude, speed, etc)

get_activity_zones

Requires activity ID. This data helps understand training intensity and whether the workout targeted the correct zones. Only available for activities with heart rate or power data. Summit/subscription feature. Get heart rate and power zone distribution for a Strava activity

get_athlete_stats

Use the athlete's Strava numeric ID. Returns recent_run_totals, recent_ride_totals, all_run_totals, all_ride_totals. Great for performance overview and progress tracking. Get consolidated activity statistics for any Strava athlete

get_athlete_zones

Requires profile:read_all scope. Use this to understand training zones for zone-based analysis of activities and efforts. Get the authenticated athlete's custom heart rate and power zones

get_gear

The gear ID is found in activity data or athlete profile. Use this to track equipment mileage, plan maintenance, or analyze performance with specific gear. Get details about a piece of equipment (bike, shoes) tracked in Strava

get_segment

The segment ID is found in Strava segment URLs. Use this to discover segment characteristics before attempting it or to compare segments. Get details of a Strava segment including distance, elevation, grade, and leaderboards

get_segment_effort

Includes elapsed time, distance, average speed, heart rate, power, start date, and activity reference. The effort ID is found in segment effort listings or activity details. Use this to analyze specific KOM/QOM attempts and compare efforts on the same segment. Get details of a specific segment effort (KOM/QOM/PR attempt)

get_segment_effort_streams

Same format as activity streams but limited to the segment portion. The "types" parameter is comma-separated: "time", "distance", "latlng", "altitude", "velocity_smooth", "heartrate", "cadence", "watts". Use this for granular analysis of segment performance. Get time-series data streams for a specific segment effort

get_segment_streams

Useful for previewing a segment's difficulty profile before attempting it. The "types" parameter accepts "distance", "altitude", "grade_smooth". Use this to understand elevation changes and steepness patterns along a segment. Get time-series data streams for a Strava segment (elevation profile, grade, etc)

list_segment_efforts

Filter by athlete_id (required), optionally segment_id to get efforts on a specific segment, and date range with start_date_local and end_date_local (ISO 8601 format). Use this to find PRs, analyze progress on segments over time, or compare multiple efforts on the same segment. List all segment efforts for an athlete, optionally filtered by segment and date range

What the Strava Training MCP Server unlocks

Connect Strava Training to any AI agent and unlock deep performance analysis from your Strava data — activity details, time-series streams, heart rate/power zones, segment efforts, lap splits, and lifetime athlete statistics.

What you can do

  • Activity Details — Full metrics: distance, time, elevation, HR, power, speed, weather, gear
  • Activity Streams — Raw GPS, heart rate, power, cadence, altitude, speed, temperature, grade data
  • Activity Zones — Heart rate and power zone distribution for training intensity analysis
  • Activity Laps — Auto-split lap data with pace, distance, and elevation per split
  • Segment Efforts — Find, compare, and analyze all efforts on any segment with detailed metrics
  • Segment Streams — Elevation and grade profiles along segments for previewing difficulty
  • Segment Details — Distance, elevation, grade, effort count, and personal records
  • Athlete Statistics — Lifetime and recent totals for runs, rides, and all activities
  • Athlete Zones — Personal heart rate and power zone configurations
  • Gear Tracking — Equipment mileage, models, and primary gear assignments

How it works

1. Subscribe to this server 2. Enter your Strava Access Token (OAuth2) 3. Start analyzing training data from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • Endurance Athletes — analyze training volume, zone distribution, and segment PRs to optimize training plans
  • Coaches — review athlete streams, zone data, and segment efforts to provide data-driven coaching
  • Cyclists — analyze power data, segment efforts, and gear mileage for performance and maintenance planning
  • Runners — track pace consistency through lap splits, elevation profiles, and heart rate trends

Frequently asked questions about the Strava Training MCP Server

01

What data streams are available for activities?

Available streams include: time, distance, latlng (GPS coordinates), altitude, velocity_smooth (speed), heartrate, cadence, watts (power), temp (temperature), moving (moving flag), and grade_smooth (incline %). Request specific streams with comma-separated types, e.g., "heartrate,watts,velocity_smooth". Not all streams are available for every activity — it depends on the recording device.

02

What are activity zones and how are they calculated?

Activity zones show the time spent in each heart rate zone (Z1-Z5) or power zone during an activity. Zones are based on the athlete's personal zone configuration (set in Strava settings). Zone analysis reveals training intensity — whether a workout was aerobic (Z1-Z2), threshold (Z3), or anaerobic (Z4-Z5). This data requires a heart rate monitor or power meter.

03

How do I find my athlete ID on Strava?

Your athlete ID is the numeric portion of your Strava profile URL. For example, from https://www.strava.com/athletes/12345678, your athlete ID is 12345678. You can also get it from the get_athlete tool in the Strava Planning or Strava Social MCP servers.

More in this category

You might also like

Give your AI agents the power of Strava Training MCP Server

Production-grade Strava Training MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.