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Strava MCP Server: Real-Time Sync & Precision Analytics - MCP Implementation

Strava MCP Server: Real-Time Sync & Precision Analytics

Strava MCP Server: Your Mirror of elite performance—sync, track, and master every stride in real-time. Precision engineered for athletes who demand flawless analytics." )

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About Strava MCP Server

What is Strava MCP Server: Real-Time Sync & Precision Analytics?

Strava MCP Server acts as a bridge between language models and Strava’s vast fitness data ecosystem. Built on the Model Context Protocol (MCP), this server enables seamless interaction with activities, athlete stats, and social features via real-time API integration. It empowers models to analyze workout metrics, visualize routes, track achievements, and engage with Strava’s social features—all while keeping data synced and up-to-date.

How to Use Strava MCP Server: Real-Time Sync & Precision Analytics?

Getting started is straightforward: clone the repository, set up a Python environment, and configure API credentials. Once connected, the server supports two primary workflows:

  • MCP Integration: Deploy the server as a context module for language models, enabling direct queries like “Show my weekly mileage trends” or “Compare today’s pace to last week.”
  • HTTP API Access: Use endpoints to fetch data programmatically, such as retrieving activity summaries or map visualizations for external apps.

Check the Strava API docs for authentication details.

Strava MCP Server Features

Key Features of Strava MCP Server: Real-Time Sync & Precision Analytics?

Core capabilities include:

  • Real-Time Sync: Automatically pulls fresh activity data every 5 minutes, ensuring models work with the latest metrics.
  • Precision Analytics: Computes advanced stats like normalized power, training load, and elevation profiles via custom algorithms.
  • Rich Visualizations: Generates interactive maps, pace charts, and elevation graphs using embedded templates.
  • Multi-Model Support: Works with any MCP-compliant AI system, from chatbots to analytical engines.

Use Cases of Strava MCP Server: Real-Time Sync & Precision Analytics?

Common scenarios include:

  • Performance Analysis: Coaches can ask “Show my athlete’s FTP progression over 3 months” for tailored training plans.
  • Activity Comparison: Compare two runs side-by-side using pace, cadence, and heart rate data.
  • Social Insights: Automatically generate summaries of group ride stats or club challenges.
  • Integration Hub: Power third-party apps needing access to Strava’s data without direct API calls.

Strava MCP Server FAQ

FAQ from Strava MCP Server: Real-Time Sync & Precision Analytics?

Q: Does this work with OpenAI models?
A: Yes, as long as the model supports MCP contexts. Add the server’s endpoint to your API call parameters.

Q: Can I customize visualizations?
A: Absolutely. Edit templates in the /src/templates.py file to adjust chart styles or map markers.

Q: How secure is my data?
A: Credentials are stored in environment variables, and the server enforces strict API rate limiting. See the security docs for details.

Q: What languages are supported?
A: The API handles any language, but natural language processing depends on the model used. The server itself is built with Python 3.9+.

Content

Strava MCP Server

A Model Context Protocol (MCP) server that provides access to the Strava API. This server enables language models to interact with Strava data, including activities, athlete information, and more.

Features

  • 🏃‍♂️ Activity tracking and analysis
  • 📊 Athlete statistics
  • 🗺️ Route visualization
  • 🏆 Achievement tracking
  • 🤝 Social features (kudos, comments)

Prerequisites

  • Python 3.12+
  • Strava API credentials
  • pip (Python package installer)

Installation

  1. Clone the repository:
git clone https://github.com/yourusername/strava_mcp.git
cd strava_mcp
  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt

Configuration

  1. Create a config/.env file with your Strava API credentials:
STRAVA_CLIENT_ID=your_client_id
STRAVA_CLIENT_SECRET=your_client_secret
STRAVA_REFRESH_TOKEN=your_refresh_token
  1. To obtain Strava API credentials:
    * Go to https://www.strava.com/settings/api
    * Create a new application
    * Note down the Client ID and Client Secret
    * Follow the OAuth 2.0 flow to get your refresh token

Usage

Using with Claude

Once connected, you can interact with your Strava data through Claude in various ways:

Activity Queries

  • "Show me my recent activities"
  • "Get details about my last run"
  • "What was my longest ride this month?"
  • "Show me activities where I set personal records"
  • "Display the route map for my latest activity"

Performance Analysis

  • "What's my average running pace this year?"
  • "Compare my cycling performance between last month and this month"
  • "Show me my heart rate zones from yesterday's workout"
  • "What's my total elevation gain for all activities?"
  • "Calculate my weekly mileage for running"

Social Interactions

  • "Who gave kudos on my latest activity?"
  • "Show me comments on my marathon run"
  • "List all my club activities"
  • "Find activities I did with friends"

Achievement Tracking

  • "List all my segment achievements"
  • "Show my personal records on local segments"
  • "What achievements did I earn this week?"
  • "Display my progress on yearly goals"

Data Available Through Claude

  1. Activity Details:
* Distance, duration, pace
* Route maps and elevation profiles
* Heart rate, power, and cadence data
* Splits and lap information
* Weather conditions during activity
  1. Athlete Statistics:
* Year-to-date and all-time totals
* Personal records and achievements
* Training load and fitness trends
* Equipment usage and maintenance
  1. Social Data:
* Kudos and comments
* Club activities and leaderboards
* Friend activities and challenges
* Segment efforts and rankings
  1. Route Information:
* Detailed maps with elevation data
* Segment analysis
* Popular routes and segments
* Route planning and analysis

As an MCP Server

Update your Claude Desktop configuration:

{
    "mcpServers": {
        "Strava": {
            "command": "python",
            "args": ["src/strava_server.py"],
            "cwd": "/path/to/strava_mcp",
            "env": {
                "STRAVA_CLIENT_ID": "your_client_id",
                "STRAVA_CLIENT_SECRET": "your_client_secret",
                "STRAVA_REFRESH_TOKEN": "your_refresh_token"
            }
        }
    }
}

As an HTTP Server

  1. Start the server:
./run_server.sh
  1. Access the API at http://localhost:8000

Available endpoints:

  • GET /activities/recent - List recent activities
  • GET /activities/{id} - Get activity details
  • GET /activities/{id}/map - Get activity map visualization
  • GET /athlete/stats - Get athlete statistics

Development

Project Structure

strava_mcp/
├── src/
│   ├── strava_server.py      # MCP server implementation
│   ├── strava_http_server.py # HTTP API server
│   ├── map_utils.py          # Map visualization utilities
│   └── templates.py          # HTML templates
├── config/
│   └── .env                  # Environment variables (not in git)
├── requirements.txt          # Python dependencies
└── run_server.sh            # Server startup script

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

Security

  • Never commit .env files or API credentials
  • The .gitignore file is configured to prevent sensitive data from being committed
  • Use environment variables for all sensitive configuration

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • Strava API Documentation
  • Model Context Protocol (MCP) Specification
  • Contributors and maintainers

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