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.

Strava Social MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Strava Social
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Strava Social through the Vinkius — pass the Edge URL in the `mcps` parameter and every Strava Social tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Strava Social Specialist",
    goal="Help users interact with Strava Social effectively",
    backstory=(
        "You are an expert at leveraging Strava Social tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Strava Social "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* 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

About Strava Social MCP Server

Connect Strava Social to any AI agent and explore the social side of Strava — activity feeds, kudos, comments, club memberships, and segment discovery.

When paired with CrewAI, Strava Social becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Strava Social tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Activity Feed — Browse your recent activities with full details, kudos counts, and comment counts
Activity Comments — Read all comments on any activity with author names and text
Activity Kudos — See who liked/supports your activities with full athlete profiles
Athlete Profile — Get your Strava profile details including location, follower counts, and equipment
Club Membership — List all clubs you belong to with member counts and sport types
Club Details — Explore any club's description, location, and community focus
Club Members — Browse club membership to find training partners and local athletes
Club Activities — See what club members have been doing recently
Starred Segments — Review all your favorited segments with PR times and characteristics
Segment Discovery — Explore segments in any geographic area by bounding box, filterable by type and difficulty

The Strava Social MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Strava Social to CrewAI via MCP

Follow these steps to integrate the Strava Social MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Strava Social

Why Use CrewAI with the Strava Social MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Strava Social through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Strava Social + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Strava Social MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Strava Social for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Strava Social, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Strava Social tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Strava Social against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Strava Social MCP Tools for CrewAI (10)

These 10 tools become available when you connect Strava Social to CrewAI via MCP:

explore_segments

g., "-74.00,40.70,-73.95,40.75" for Manhattan). Optional filters: activity_type ("running" or "riding"), min_cat/max_cat (category 0-5, where 0 is hardest/steepest). Returns segments with name, distance, elevation, grade, and climb category. Use this to discover new training routes, find popular segments in an area, or plan rides/runs in a new city. Explore and discover Strava segments in a geographic area

get_activity_comments

Each comment includes athlete name, text, and creation date. The activityId is the numeric ID from Strava activity URLs. Use this to see community engagement on a workout, read feedback, or track conversation around a specific activity. Get all comments on a specific Strava activity

get_activity_kudos

Each entry includes athlete name, profile picture, and city. The activityId is the numeric ID from Strava. Use this to see who supported an activity, understand social engagement, or track training partners' interactions. Get the list of athletes who gave kudos (likes) to a specific activity

get_athlete

Use this to understand the athlete's identity, location, and equipment setup. Get the authenticated athlete's profile information

get_club

The clubId is found in Strava club URLs. Use this to explore club details before joining or to understand a club's focus and community. Get detailed information about a specific Strava club

list_activities

Activities are sorted by most recent first. Optional filters: "before" (epoch timestamp, defaults to now), "after" (epoch timestamp for date range), "page" and "per_page" (pagination, max 200 per page, max 2000 total). Each activity includes: name, type, distance, moving_time, elevation, kudos_count, comment_count, start_date, and basic stats. Use this to get the activity feed, analyze recent workouts, or review training history. Epoch timestamps can be generated from dates. List the authenticated athlete's activities with optional date filtering and pagination

list_athlete_clubs

Each club includes name, member count, city, country, sport type (cycling/running/triathlon), and privacy status. Use this to discover club memberships, find training groups, or understand community affiliations. List all clubs the authenticated athlete belongs to

list_club_activities

Each entry includes athlete name, activity name, type, distance, and date. Paginated (30 per page). The clubId is from Strava club URLs. Use this to stay updated on club training activity, discover what members are doing, or find group workout opportunities. Get recent activities from members of a Strava club

list_club_members

Paginated (30 per page). The clubId is from Strava club URLs. Use this to discover training partners in a club, find athletes in your area, or explore club community composition. List members of a specific Strava club

list_starred_segments

Each segment includes: name, distance, elevation gain, average grade, activity type, city, country, and the athlete's PR time if any. Use this to review favorite segments, plan training routes, or track progress on key segments over time. List all segments starred (favorited) by the authenticated athlete

Example Prompts for Strava Social in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Strava Social immediately.

"Show my recent activities."

"Explore cycling segments in Manhattan, NYC."

"Show comments on my latest activity."

Troubleshooting Strava Social MCP Server with CrewAI

Common issues when connecting Strava Social to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Strava Social + CrewAI FAQ

Common questions about integrating Strava Social MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.