Integrate Strava Training with Claude, Cursor, Chatbots & AI Agents MCP Server

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

GDPR Free for Subscribers

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

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Get activity on Strava Training

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

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Get activity laps on Strava Training

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

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Get activity streams on Strava Training

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)

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Get activity zones on Strava Training

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

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Get athlete stats on Strava Training

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

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Get athlete zones on Strava Training

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

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Get gear on Strava Training

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

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Get segment on Strava Training

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

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Get segment effort on Strava Training

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)

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Get segment effort streams on Strava Training

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

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Get segment streams on Strava Training

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)

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List segment efforts on Strava Training

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

Security & Code Integrity Audit

Every tool in the Strava Training MCP Server is continuously audited by the Vinkius Security Engine. We guarantee zero-trust payload isolation, strict data boundaries, and deterministic execution for enterprise-grade AI agents.

A+Score: 98.33

How Vinkius protects your data

Can I audit what my AI agents are doing with this integration?

Yes, Vinkius provides an immutable, HMAC-chained audit log. Every tool execution, payload, and response is tracked in real-time on your dashboard, giving you complete visibility into your agent's actions.

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.

What happens if the underlying API rate limits my agent?

Our edge infrastructure automatically handles backoffs, queueing, and throttling. If an AI agent sends too many erratic requests, Vinkius manages the rate limits gracefully, ensuring your backend doesn't crash.

What if the AI ends up reading customer data or confidential information?

We have a built-in digital "bodyguard" called DLP (Data Loss Prevention). If a tool fetches data and the response contains social security numbers, credit cards, or personal customer info, Vinkius magically blocks and erases that information before it is delivered to the AI. The AI works only with what is strictly necessary, and your sensitive data never leaks.

Supported Use Cases for Strava Training

Integrate Strava Training to provide your custom AI agents with direct read and write access to the capabilities listed below.

LLM Orchestration for performance metrics

Build automated workflows involving performance metrics by connecting Strava Training. It provides Claude and ChatGPT with direct API hooks into your data analytics ecosystem.

Autonomous heart rate zones Strategies

The Strava Training toolkit provides secure access to heart rate zones functions. It enables conversational agents to manage data analytics settings deterministically.