Workout MCP Server

The Workout MCP Server is a purpose‑built Model Context Protocol service that equips AI assistants with the ability to interrogate and analyze cycling workout data. By exposing a concise set of tools, it allows language models to retrieve historical sessions, compute advanced training metrics such as Chronic Training Load (CTL), Acute Training Load (ATL), and Training Stress Balance (TSB), and deliver actionable insights into an athlete’s performance trajectory. This capability is particularly valuable for developers building coaching applications, fitness dashboards, or personalized training recommendations that rely on real‑time data analysis.

What Problem Does It Solve?

Traditional fitness platforms expose raw metrics (distance, power, heart rate) but rarely provide an API that translates those numbers into meaningful training loads. Coaches and athletes must manually calculate CTL, ATL, and TSB to assess readiness and fatigue—a process that is both time‑consuming and error‑prone. The Workout MCP Server abstracts this complexity by offering pre‑computed, exponentially weighted moving averages that align with established sports science models. Developers can therefore focus on higher‑level logic (e.g., scheduling workouts, generating feedback) while the server handles the heavy lifting of data aggregation and metric calculation.

Core Features & Capabilities

• Historical Retrieval: and provide quick access to recent training data, facilitating trend analysis or week‑in‑review summaries. allows precise lookup of a single session.
• Metric Computation: Dedicated tools compute CTL (42‑day EWM of TSS), ATL (7‑day EWM), and TSB (difference between CTL and ATL). These metrics are foundational for periodization, injury prevention, and performance forecasting.
• Structured Data: Each workout record contains identifiers, timestamps, duration, distance, power, TSS, and type, enabling granular filtering and contextual analysis.
• Seamless Integration: The server is designed to be launched via standard MCP client configurations, such as Claude Desktop or VSCode’s MCP extension, ensuring minimal friction for developers familiar with MCP workflows.

Real‑World Use Cases

• Coaching Platforms: AI assistants can automatically generate weekly reports, highlight areas of fatigue, and suggest recovery strategies.
• Personal Training Apps: Users receive real‑time feedback on their form (TSB) after each ride, helping them avoid overtraining.
• Sports Analytics: Teams can feed the server into larger data pipelines, correlating training loads with race performance metrics.
• Research Tools: Academics studying exercise physiology can query historical data and compute training loads without building custom scripts.

Unique Advantages

Unlike generic fitness APIs, the Workout MCP Server delivers domain‑specific analytics out of the box. Its modular toolset aligns with Model Context Protocol standards, allowing LLMs to request precise data slices or computed metrics without bespoke code. This tight coupling between data retrieval and analysis reduces latency, eliminates duplicate calculations across services, and guarantees consistency in metric definitions. For developers building AI‑powered coaching solutions, the server offers a turnkey, scientifically validated foundation that accelerates development and enhances user experience.