Strava MCP Server: Real-Time Athlete Activity Data via Model Context Protocol

Overview: What is Strava MCP Server?

The Strava MCP Server integrates directly into the Model Context Protocol (MCP) ecosystem, enabling AI applications and conversational agents like Claude Desktop to access real-time athlete activity data from the Strava API. By leveraging MCP, developers can create seamless workflows that combine predictive analytics, natural language processing, and live sports data to enhance user experiences across various platforms.

🔧 Core Features & MCP Capabilities

The Strava MCP Server supports essential activities queries and provides consistent data formats, ensuring AI applications receive accurate and easily readable responses. It covers key functionalities such as retrieving recent activities, specifying date ranges for query filters, obtaining detailed activity information, and fetching past activities based on time frames. This server is designed to be lightweight yet powerful, making it suitable for both personal and professional use cases.

⚙️ MCP Architecture & Protocol Implementation

Strava API Authentication

To authenticate with the Strava API, developers must follow a structured process:

1. Create an Application:
   • Access the Strava API Settings and create an application to obtain your client ID and secret.
   • Set the authorization callback domain to localhost.
2. Obtain Refresh Tokens:
   • Utilize a provided script (get_strava_token.py), run it with python get_strava_token.py, and follow the prompts to authorize.
3. Set Environment Variables:
   • Define environment variables required by the server, such as STRAVA_CLIENT_ID, STRAVA_CLIENT_SECRET, and STRAVA_REFRESH_TOKEN.

Tool Queries

The Strava MCP Server exposes several tools for querying athlete activity data:

• get_activities(limit): Retrieves recent activities.
• get_activities_by_date_range(start_date, end_date, limit): Fetches activities within a specified date range.
• get_activity_by_id(activity_id): Provides detailed information about a specific activity.
• get_recent_activities(days, limit): Lists activities from the past X days.

🚀 Getting Started with Installation

For integration with AI applications like Claude Desktop:

1. Update Configuration Files:
   • For Claude for Desktop, modify your claude_desktop_config.json by adding the following server configuration:

{
    "mcpServers": {
        "strava": {
            "command": "uvx",
            "args": [
                "strava-mcp-server"
            ],
            "env": {
                "STRAVA_CLIENT_ID": "YOUR_CLIENT_ID",
                "STRAVA_CLIENT_SECRET": "YOUR_CLIENT_SECRET",
                "STRAVA_REFRESH_TOKEN": "YOUR_REFRESH_TOKEN"
            }
        }
    }
}

💡 Key Use Cases in AI Workflows

Real-Time Activity Data for Predictive Analytics

Developers can use the Strava MCP Server to provide real-time predictive insights into user behaviors. For example, an e-commerce platform could analyze a user's recent running activities and recommend relevant apparel or accessories.

graph TD
    A[Real-Time Analytics] --> B[Predictive Insights]
    B --> C[Suggested Products]

Personalized Recommendation Systems

AI applications can leverage sport-specific data to generate personalized recommendations. For instance, a chatbot could suggest new routes based on an athlete's preferences and historical performance metrics.

graph TD
    A[Chatbot] --> B[User Preferences & History]
    B --> C[Suggested Routes]

🔌 Integration with MCP Clients

The Strava MCP Server is compatible with the following MCP clients:

Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

This section outlines the performance and compatibility of the Strava MCP Server, ensuring it meets the needs of various AI applications.

Data Latency

The server ensures low-latency operations by caching API responses locally unless explicit updates are requested. This reduces dependence on external services and provides more reliable data retrieval times.

Network Requirements

The server performs optimally with a steady internet connection but can operate offline or behind firewalls if the cache is updated regularly.

🛠️ Advanced Configuration & Security

To enhance security, ensure that environment variables storing sensitive information are encrypted when stored in configuration files. Additionally, use secure methods for handling refresh tokens and client secrets to avoid exposing them during runtime.

Example Configuration Code Sample

{
    "mcpServers": {
        "strava": {
            "command": "uvx",
            "args": [
                "strava-mcp-server"
            ],
            "env": {
                "STRAVA_CLIENT_ID": "<your-client-id>",
                "STRAVA_CLIENT_SECRET": "<your-client-secret>",
                "STRAVA_REFRESH_TOKEN": "<your-refresh-token>"
            }
        }
    }
}

❓ Frequently Asked Questions (FAQ)

Q: What are the common errors encountered during integration?

A: Common errors include invalid date formats, API authentication issues, and network connectivity problems. The server provides human-readable error messages to aid troubleshooting.

Q: How does the Strava MCP Server handle API rate limits?

A: The server manages API connections using a token-based system, ensuring compliance with Strava’s rate limits without imposing delays on users.

Q: Can developers extend this server for custom tools and queries?

A: Yes, developers can expand the server’s functionality by integrating additional third-party APIs or tools to create tailored workflows.

Q: How does the Strava MCP Server ensure data privacy?

A: The Strava MCP Server stores only necessary data and follows strict privacy policies. It encrypts sensitive tokens and uses secure methods for handling authentication credentials.

👨‍💻 Development & Contribution Guidelines

Contributions are welcomed! Developers can contribute to this project by reporting bugs, improving documentation, or adding new features. Follow the contribution guidelines for more details.

🌐 MCP Ecosystem & Resources

For further information on the Model Context Protocol and related projects, visit:

• Model Context Protocol (MCP)

By integrating the Strava MCP Server into your AI applications, you can leverage real-time athlete data to create more engaging and personalized experiences for users.