概览
What is Workout MCP Server?
A Model Context Protocol (MCP) server that provides cycling workout analytics tools, enabling LLMs to analyze fitness data, calculate training metrics, and provide insights into athletic performance.
How to use Workout MCP Server?
Install via uvx workout_mcp_server (recommended) or pip install workout_mcp_server and run with python -m workout_mcp_server. Configure with Claude Desktop or VS Code MCP Client by adding the server configuration to the appropriate JSON file. Use the provided tools to retrieve workouts and compute fitness, fatigue, and form scores.
Key features of Workout MCP Server
- Retrieves workout history (last 50 or last 7 workouts)
- Looks up specific workouts by unique ID
- Computes CTL (Fitness) via 42-day weighted average of TSS
- Computes ATL (Fatigue) via 7-day weighted average of TSS
- Computes TSB (Form) as difference between fitness and fatigue
- Provides structured workout data including TSS, power, distance, and type
Use cases of Workout MCP Server
- Analyze training load and recovery status for cyclists
- Provide insights on workout history and performance trends
- Assist athletes in planning training cycles based on metrics
- Enable AI assistants to answer fitness-related queries
FAQ from Workout MCP Server
What fitness metrics does the server compute?
It computes CTL (Fitness), ATL (Fatigue), and TSB (Form) using exponentially weighted moving averages of Training Stress Score (TSS).
How do I install and run the server?
Use uvx workout_mcp_server with uv (no installation needed) or run pip install workout_mcp_server then python -m workout_mcp_server.
What data does a workout contain?
Each workout includes id, date, duration_minutes, distance_km, avg_power_watts, tss, and workout_type. The dataset is built-in (50 workouts).
How do I configure it with Claude Desktop?
Add the appropriate JSON block to your claude_desktop_config.json under mcpServers, using either uvx or python -m depending on your installation method.
Are there any dependencies or runtime requirements?
Python is required; uv is optional but recommended. No external data source or authentication is needed—workouts come from a built-in dataset.
