On Running MCP Server

Overview

The On Running MCP server bridges the gap between an AI assistant and the On Running e‑commerce platform. By exposing a curated set of tools that mirror the public API, it lets developers query product catalogs, filter by category or demographic, and retrieve rich media such as images and activity tags—all within the conversational context of an assistant. This eliminates the need for custom API wrappers or manual data handling, enabling rapid prototyping of shopping assistants, recommendation engines, and inventory dashboards.

The server’s core functionality revolves around a FastMCP implementation that translates natural‑language prompts into precise API calls. When a user asks for “running socks,” the assistant can invoke with filters for product type and activity, then chain additional tools like or to narrow the results. The modular design means each tool is a single responsibility: one returns raw query data, another extracts names, and yet another fetches image URLs. This separation keeps the MCP lightweight while providing developers with fine‑grained control over the data pipeline.

Key capabilities include:

• Dynamic querying: accepts optional filters for type, subtype, gender, and activity, enabling context‑aware searches that adapt to user intent.
• Data extraction: Tools such as , , and transform raw API responses into actionable information, ready for display or further processing.
• Media retrieval: returns high‑resolution image links, allowing visual components to be rendered directly in a chat interface or web app.
• Metadata discovery: Tools that list available product types, subtypes, and gender options empower the assistant to ask clarifying questions or auto‑populate dropdowns in a UI.

In practice, this server is ideal for building shopping assistants that can recommend gear based on user goals (e.g., marathon training), for inventory monitoring tools that keep track of new releases or stock levels, and for marketing analytics dashboards that surface product activity trends. By integrating seamlessly with existing MCP workflows, developers can plug this server into Claude Desktop or other AI‑powered platforms and immediately gain structured access to On Running’s catalog without writing boilerplate code.

The standout advantage lies in its contextual richness: the assistant can ask follow‑up questions (“Do you prefer men’s or women’s socks?”), filter results on the fly, and present images—all within a single conversational thread. This tight coupling between query logic and UI output makes the On Running MCP server a powerful tool for any developer looking to embed e‑commerce intelligence into conversational AI experiences.