Strava API integration with Model Context Protocol (MCP) SDK

Codecs Python License: MIT Ruff

Integration for training analysis and recommendations based on Strava data using the Model Context Protocol SDK.

🚀 Features

Analysis of workouts from Strava
Training Recommendations
Automatic token update
Rate limiting for API requests

📋 Requirements

Python 3.10+
Claude Desktop
Strava account
uv (recommended)

⚙️ Installation

# Клонируем репозиторий git clone https://github.com/rbctmz/mcp-server-strava.git cd mcp-server-strava # Установка через uv (рекомендуется) curl -LsSf https://astral.sh/uv/install.sh | sh uv pip install . # Установка в режиме разработки uv pip install -e ".[dev]"

Installing MCP SDK

uv add "mcp[cli]"

🔧 Setting

Setting up Strava API

Go to the API settings page
Create an application:
- Application Name: MCP Strava Integration
- Category: Training Analysis
- Website: http://localhost
- Authorization Callback Domain: localhost

Setting up the environment

Create a file with environment variables:
cp .env-template .env
Get access tokens:
python scripts/auth.py
Check the setting:
mcp dev src/server.py curl -X GET "http://localhost:8000/activities"

📚 API and examples

Resources and Tools

Type	Name	Description
Resource	`strava://activities`	List of activities
Resource	`strava://activities/{id}`	Activity details
Resource	`strava://athlete/zones`	Training zones
Resource	`strava://athlete/clubs`	Athlete's clubs
Resource	`strava://gear/{gear_id}`	Equipment Information
Tool	`analyze_activity(activity_id)`	Training Analysis
Tool	`analyze_training_load(activities)`	Load analysis
Tool	`get_activity_recommendations()`	Recommendations

Examples of use

from mcp import ClientSession # Получение активностей async with ClientSession() as session: activities = await session.read_resource("strava://activities") activity = await session.read_resource("strava://activities/12345678") # Анализ тренировки result = analyze_activity(activity_id="12345678") """ { "type": "Run", "distance": 5000, "moving_time": 1800, "analysis": { "pace": 5.5, # мин/км "effort": "Средняя" } } """ # Анализ нагрузки summary = analyze_training_load(activities) """ { "activities_count": 10, "total_distance": 50.5, # км "total_time": 5.2, # часы "heart_rate_zones": { "easy": 4, # ЧСС < 120 "medium": 4, # ЧСС 120-150 "hard": 2 # ЧСС > 150 } } """ # Получение тренировочных зон async with ClientSession() as session: zones = await session.read_resource("strava://athlete/zones") """ { "heart_rate": { "custom_zones": true, "zones": [ {"min": 0, "max": 120, "name": "Z1 - Recovery"}, {"min": 120, "max": 150, "name": "Z2 - Endurance"}, {"min": 150, "max": 170, "name": "Z3 - Tempo"}, {"min": 170, "max": 185, "name": "Z4 - Threshold"}, {"min": 185, "max": -1, "name": "Z5 - Anaerobic"} ] }, "power": { "zones": [ {"min": 0, "max": 180}, {"min": 181, "max": 250}, {"min": 251, "max": 300}, {"min": 301, "max": 350}, {"min": 351, "max": -1} ] } } """

🛠 Development

CI/CD and Security

Checks in GitHub Actions

Type	Tool	Description
Linting	ruff	Code formatting and analysis
Tests	pytest	Unit and integration tests
Coating	pytest-cov	Code Coverage Report

Security and Secrets

Token protection:
- .env to .gitignore
- GitHub Secrets for CI/CD
- Rate limiting: 100 requests/15 min
Setting up secrets:
# В GitHub: Settings → Secrets → Actions STRAVA_CLIENT_ID=<client_id> STRAVA_CLIENT_SECRET=<client_secret> STRAVA_REFRESH_TOKEN=<refresh_token>