Strava MCP Server

The Strava MCP Server bridges the gap between conversational AI assistants and the rich activity data that athletes generate on Strava. By exposing a set of well‑defined tools, it lets language models pull real‑time or historical workout information without the developer writing custom API wrappers. This is especially valuable for developers building coaching apps, fitness analytics dashboards, or personal productivity bots that need to understand a user’s recent rides, runs, or climbs.

At its core, the server offers four query tools that map directly to common Strava endpoints. Developers can ask for a list of recent activities, fetch detailed information on a single workout by its ID, or retrieve all rides within a specific date range. The responses are normalised into a consistent JSON schema that includes key metrics such as distance, elapsed time, speed, elevation gain, calories, and GPS coordinates. By standardising units (meters, seconds, ISO dates) the server removes ambiguity that often plagues raw API data, enabling downstream models to reason about performance metrics without additional parsing logic.

Use cases abound. A coaching assistant can ask, “Show me my top 5 rides from last month” and immediately receive a ranked list with distances, average speeds, and elevation profiles. A habit‑tracking bot could monitor weekly mileage trends by querying recent activities over the past seven days. Even a travel planner might integrate Strava data to suggest scenic routes for upcoming trips. Because the server handles OAuth refresh tokens internally, developers can focus on building conversational flows rather than token management.

Integration into AI workflows is seamless. Once the MCP server is running, any model that supports the Model Context Protocol can invoke the tools via simple JSON calls. The server’s authentication layer requires only three environment variables, and the configuration snippet for Claude Desktop demonstrates how to plug it into existing setups. For web‑based assistants, the same local server can be accessed through an MCP extension, keeping all data on the user’s machine and preserving privacy.

In summary, the Strava MCP Server turns raw athlete data into actionable knowledge for AI assistants. Its well‑documented, unit‑consistent responses and straightforward authentication make it a ready‑to‑use component for developers who want to enrich conversations with real, up‑to‑date fitness insights.