Strava Social MCP for AI. Track group progress and find local training segments.
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Strava Social connects your AI agent to the full social graph of Strava. It lets you read activity feeds, track kudos, analyze comments, find local training segments by bounding box, and manage club memberships—all without logging into a browser.
What your AI can do
List activities
Retrieves the authenticated user's activity feed with basic stats, allowing filtering by date range.
Get athlete
Gets profile details for an athlete, including their location and follower count.
Get club
Fetches detailed information about a specific Strava club, like its description and focus.
List your recent workouts, receiving name, type, distance, elevation gain, and current kudos/comment counts.
Retrieve all comments left on a specific activity, including the author's name and text.
List your club memberships or browse members of a club to find local athletes with shared interests.
Search for cycling or running segments within any geographic area using coordinates, filtered by difficulty and type.
Fetch an individual athlete's profile stats (location, followers) or get detailed information about a specific club.
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Strava Social MCP Server: 10 Tools for Fitness Tracking
Use this collection of tools to read Strava's social graph. You can analyze workout history, discover local segments, or check team engagement from a single prompt.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Strava Social on VinkiusList Activities
Retrieves the authenticated user's activity feed with basic stats, allowing filtering by date range.
Get Athlete
Gets profile details for an athlete, including their location and follower count.
Get Club
Fetches detailed information about a specific Strava club, like its description and...
List Club Activities
Pulls recent activities from members within a specific Strava club.
List Club Members
Browses the roster of a given club, providing names and basic profile details for...
List Athlete Clubs
Lists every club the logged-in athlete is a member of, showing member counts and sport types.
Get Activity Comments
Retrieves all text comments written by other athletes about a specific activity ID.
Explore Segments
Finds cycling or running segments within specified coordinates, returning details...
Get Activity Kudos
Lists the names and profiles of athletes who gave kudos (likes) to a specific...
List Starred Segments
Lists all segments you have favorited (starred), including your personal best times...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Strava Social integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with Strava Social, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Strava Social. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Checking Strava's feed shouldn't require endless scrolling and tab-switching.
Right now, if you want to know who supported your last marathon effort or what segments are popular near your office, you have to log into the website. You scroll through activity by activity. You click on each one to find comments. Then you search for local maps and filter them by difficulty manually. It's a huge time sink.
With this MCP server, your agent handles it all. Instead of clicking, you ask: 'Who liked my last ride?' The agent runs `get_activity_kudos` and gives you a clean list. Or, 'What are the top climbs nearby?' The agent uses `explore_segments` and returns structured data immediately.
The `list_club_activities` tool lets you see group activity instantly.
Before, tracking a club meant going to the Strava site, clicking into the club page, and then manually scrolling through every single member's feed. You could only get a snapshot of what was happening at that moment, and it felt overwhelming.
Now, you ask your agent: 'Show me all activities from the cycling club this week.' The agent executes `list_club_activities` and gives you one consolidated, structured list. It cuts out the noise and delivers only the actionable data.
What your AI can actually do with this
Strava Social connects your AI agent directly to Strava's entire social network. You treat it like a massive data source, pulling activity feeds, kudos counts, comments, and segment details without ever having to open a browser tab or log in yourself. It’s pure utility for your workflow.
Tracking Your Activity History
When you run list_activities, you get the full rundown of your recent workouts. This isn't just a list; it gives you basic stats, activity type, distance, and elevation gain for every entry. You'll also see real-time counts for kudos received and comments left on each activity, giving you an immediate sense of community engagement.
For deeper dives into your achievements, list_starred_segments pulls up every segment you’ve favorited (or 'starred'). It gives you a clear breakdown of those routes, including your personal best times—your PRs—for the segments. This is how you track consistent performance on specific, known climbs or stretches.
Analyzing Community Feedback and Support
Want to know who liked your ride? Call get_activity_kudos, and it lists every athlete who hit that 'kudos' button for a specific workout; you get their names and profile details. Need the full story? Run get_activity_comments on an activity ID, and you retrieve all text comments left by other athletes about it—you know exactly who wrote what.
Discovering Routes and Local Training Spots
Finding a good route shouldn't be guesswork. Use explore_segments to search for cycling or running segments within any geographic area using bounding box coordinates. This tool returns all the necessary details, including distance, grade, and climb category. If you need to know what’s popular near your house, this is it.
Social Connections: Clubs and Training Partners
Your social life on Strava runs through its clubs. list_athlete_clubs shows every single club the logged-in athlete belongs to, giving member counts and defining the primary sport type for each group. If you want details on one specific squad, get_club fetches all the info—like the club's description or focus—for that particular entity.
When it comes time to find training partners, you can browse a whole roster using list_club_members. You give it a club, and it provides names and basic profile details for everyone in it. Want to see what’s happening right now with your crew? list_club_activities pulls recent activities from members within that specific club.
You can also get the nitty-gritty on any individual athlete using get_athlete. This gives you their full profile stats, including location and how many followers they've racked up. It’s a complete picture of who’s out there.
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Here's how it actually works
The bottom line is you treat Strava not as a website, but as an API endpoint for social fitness data.
Subscribe to the Strava Social server and provide your OAuth2 Access Token.
Pass the necessary identifiers (like an activityId or bounding box) from your AI client's prompt.
Your agent executes the required tool (list_activities, explore_segments, etc.) and returns structured data on your workout history, local segments, or club members.
Who is this actually for?
Cycling coaches and performance analysts who need to monitor group progress; software developers building fitness apps that require real-time social tracking; or power users who want their AI agent to handle all the manual 'checking' of activity feeds, club rosters, and local routes.
Runs list_club_activities to monitor team engagement and uses explore_segments to suggest new training routes in the area.
Integrates tools like get_activity_kudos into a dashboard to build social engagement metrics without constant polling.
Uses list_club_members and list_athlete_clubs to vet potential training partners or manage membership records for a local group.
What Changes When You Connect
See who's been active: Use list_activities to pull recent workouts, getting stats like distance, elevation, and how many kudos or comments they earned. It gives a full picture of the week's effort.
Monitor group progress: Run list_club_activities to see what every member of your club is doing right now. This replaces manually checking dozens of profiles for updates.
Find new routes easily: Use explore_segments with just coordinates (a bounding box). You don't need to visit a map; the agent returns top climbs and popular segments immediately.
Track social engagement: Check get_activity_kudos after a big ride. Instead of scrolling through names, your agent tells you exactly who supported it, helping track community impact.
Understand your network: Run list_athlete_clubs to see all your affiliations at once. You can then use get_club to know the specific focus and mission of any group.
See it in action
Planning a trip to a new city
You're moving to Portland, OR, and need local running routes. You ask your agent: 'Find the hardest 10k segments near downtown.' The agent runs explore_segments with the coordinates, giving you immediate results for top climbs and popular tracks, so you don't waste time mapping it out manually.
Checking team motivation after a race
The team captain wants to know which members are still active. They ask the agent: 'Show me what people in our club did last week.' The agent uses list_club_activities, pulling all recent workouts from every member, allowing the coach to spot who's dropping off or who needs a challenge.
Summarizing workout performance
You just finished a challenging ride. You ask your agent: 'What did people think of my ride?' The agent runs get_activity_comments, pulling all the feedback, so you can quickly see if teammates noticed the high elevation gain or complimented your pacing.
Finding new training buddies
You want to find someone with similar goals in a specific club. You ask: 'List five members of the 'Early Birds' club who run long distances.' The agent runs list_club_members and filters the results, giving you names and details for potential running partners.
The honest tradeoffs
Treating Strava like a simple feed
Trying to manually gather data by checking profile after profile or clicking through multiple tabs on the website, leading to inconsistent data and lost time.
Run list_activities first for an aggregate view. Then use specific tools like get_activity_kudos or get_activity_comments to get structured details without navigating a single web page.
Assuming all data is available
Expecting the agent to know your PR time for every segment without specific input.
Use list_starred_segments. This tool specifically pulls segments you marked as favorites, ensuring the API has the context of which routes matter most to you.
Ignoring geographical boundaries
Asking for 'popular running spots' without giving any location data.
Always use explore_segments and provide a bounding box (e.g., '-74.0, 40.7, -73.95, 40.75') to limit the search to a precise area.
When It Fits, When It Doesn't
Use this server if your primary need involves social tracking or route discovery tied to fitness performance data. You want to know who did what, or where the best routes are, and you need that information aggregated automatically.
Don't use it if: 1) All you need is raw GPS telemetry (use a GIS tool). 2) Your goal is purely account management (you just need to change your password—don't touch this server).
This server excels at the 'social layer.' If you want to know who liked the activity (get_activity_kudos) or what specific segments are popular in an area (explore_segments), this is it. But if you only need a simple list of your past mileages, list_activities handles that fine without needing the social context.
Questions you might have
How do I find new running routes using explore_segments? +
You run explore_segments and pass in a bounding box (coordinates) for your desired area. You can also filter the search by setting the activity type to 'running' or 'riding'.
What does get_activity_kudos actually tell me? +
get_activity_kudos tells you exactly which athletes supported your workout. It returns names and profile details, so you know who was paying attention.
Can I see all my clubs using list_athlete_clubs? +
Yes. list_athlete_clubs gives a clean roster of every club you belong to, showing the name and the total member count for each one.
How can I get comments on an activity using get_activity_comments? +
You must provide the numeric activityId from Strava. The tool then returns every comment, including who wrote it and when they posted it.
Does list_club_members show current members only? +
Yes, list_club_members is paginated and provides a roster of current club members. This helps you find specific training partners in your local area.
How do I filter my activity history by a specific date range using list_activities? +
You must provide epoch timestamps for the 'before' and 'after' parameters. This limits the results precisely to your desired date window. Remember that you need to convert your start and end dates into Unix timestamps before calling this function.
What exact data points does get_athlete return about my profile? +
It returns core identity details, location information, follower counts, and equipment setup. Think of it as a complete snapshot of your public Strava profile at the time of the call.
Can I use explore_segments to find segments with high difficulty or steep grades? +
Yes, you filter by min_cat and max_cat. Use a low number for min_cat (like 0) if you want the steepest or most challenging segments. This helps narrow down dangerous climbs.
Can I see who liked my activities? +
Yes! Use the get_activity_kudos tool with any activity ID. It returns the full list of athletes who gave kudos to that activity, including their names, cities, and profile pictures. This helps you understand who's following and supporting your training.
How can I discover popular segments in a new city? +
Use the explore_segments tool with a bounding box of the area you're interested in. The bounds format is "southwest_lng,southwest_lat,northeast_lng,northeast_lat". For example, "-74.00,40.70,-73.95,40.75" covers Manhattan. You can filter by activity_type ("riding" or "running") and difficulty category (0-5, where 0 is hardest).
Can I see what my club members have been doing? +
Yes! Use list_club_activities with the club ID. It returns the 30 most recent activities from club members with athlete names, activity types, distances, and dates. Paginate through results to see more. This is great for staying connected with your training group's activities.
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