MCP Server Strava

This project is an integration tool between the Strava API and the Model Context Protocol (MCP) SDK, used to analyze training data and provide personalized recommendations. It supports functions such as training activity analysis, automatic token update, and API request rate limit.

Health and wellness Developer tools #Training Analysis #API Integration #Sports Data #Intelligent Recommendation .Python

rating : 2 points

downloads : 11

update time : 2025-04-29

What is the Strava and MCP Integration Service?

This service connects your Strava sports data to the intelligent analysis system (MCP), automatically analyzes your training performance, and provides personalized improvement suggestions. The system reads your sports records, heart rate data, etc., to help you better understand your training effects.

How to use the Strava and MCP Integration Service?

Simply connect your Strava account, and the system will automatically synchronize your sports data. You can view detailed analysis reports, get training suggestions, and track your progress.

Use Cases

Suitable for sports enthusiasts such as runners and cyclists who hope to scientifically analyze training data and optimize training plans.

Main Features

Training Activity AnalysisDetailed analysis of data such as distance, duration, pace, and heart rate zones for each training session.

Personalized Training RecommendationsProvide improvement suggestions based on your training history and performance.

Automatic Token RefreshThe system automatically maintains the connection with Strava without manual token updates.

Advantages and Limitations

Advantages

Scientifically analyze training data to help optimize training plans

Automatically synchronize Strava data without manual input

Provide intuitive training reports and suggestions

Limitations

A Strava Premium account is required to obtain complete heart rate data

There is a limit on the API request frequency (100 requests per 15 minutes)

Currently only supports analysis of running and cycling activities

How to Use

Install Software

Use the uv tool to install the necessary software packages.

Configure the Strava Application

Create an application in the Strava developer settings and obtain the API key.

Run the Authorization Script

Obtain an access token to connect your Strava account.

Start the Service

Use the MCP CLI to start the analysis server.

Usage Examples

Analyze Running PerformanceView a detailed analysis of your last run, including pace, heart rate zone distribution, etc.

Weekly Training SummaryGet a summary report of all training activities this week.

Frequently Asked Questions

Do I need a Strava Premium account?

How often is the data updated?

What sports types are supported?

Related Resources

Strava Developer Documentation

Strava official API documentation

MCP SDK Documentation

Model Context Protocol SDK usage guide

Example Code Repository

The GitHub repository for this project

🚀 Complete Guide to Integrating Strava API with Model Context Protocol (MCP) SDK

This guide details how to integrate the Strava API with the Model Context Protocol (MCP) SDK. Through this integration, you can access and analyze sports data from the Strava platform and leverage the powerful features of MCP for further processing.

✨ Features

Data Retrieval: Obtain detailed sports data analysis from Strava.
Resource Management: Manage and configure various resources in sports data.
Performance Analysis: Conduct in - depth analysis of sports performance using the MCP SDK.

📦 Installation Requirements

Python Version: Python 3.8 or higher is required.
Dependencies:
- mcp-sdk: The latest stable version of the MCP SDK.
- strava-api-python: The official Python library for the Strava API.
- requests: A basic library for handling HTTP requests.

📦 Installation Guide

Install the MCP SDK:
```
pip install mcp-sdk
```
Install the Strava API Library:
```
pip install strava-api-python
```

Configuration Steps

Register an Application: Create a new application in the Strava Developer Console and obtain the client ID and secret.

Set Environment Variables:

export STRAVA_CLIENT_ID=your_client_id
export STRAVA_CLIENT_SECRET=your_client_secret

💻 Usage Examples

API Resources Overview

Type	Name	Description
Data	`activity`	Retrieve data for a single activity.
Collection	`activities`	Retrieve data for multiple activities.
Configuration	`athlete`	Retrieve detailed information about the current user.

Tool Example

from strava import StravaApi
from mcp_sdk import MCPClient

# Initialize the Strava API client
strava = StravaApi(client_id=STRAVA_CLIENT_ID, client_secret=STRAVA_CLIENT_SECRET)

# Get activity data
activities = strava.get_activities()

# Use the MCP SDK for analysis
mcp = MCPClient()
analysis_results = mcp.analyze(activities)

🔧 Technical Details

CI/CD and Security

Code Coverage: 72%
Test Pass Rate: 15/20
Code Style: Use Ruff for formatting and checking.

GitHub Actions Checks

Type	Tool	Description
Check	Ruff	Code formatting and quality issue detection.
Test	Pytest	Unit and integration testing.
Coverage	Pytest - cov	Code coverage reporting.

Security Measures

Token Protection:
- Add the .env file to .gitignore.
- Use GitHub Secrets to manage CI/CD keys.
Rate Limiting: Limit to 100 requests every 15 minutes.

Contribution Guidelines

Fork the Repository
Install Dependencies:
```
uv pip install -e ".[dev]"
```
Create a Feature Branch:
```
git checkout -b feature/name
```

Verify Changes:

ruff format .
ruff check .
pytest --cov=src

Support and Contact

GitHub Issues: Create a new issue
Telegram: @greg_kisel

📄 License

MIT

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