About
A Model Context Protocol server that delivers comprehensive information on professional cyclists, race results, teams, and historical events from FirstCycling. It enables data-driven rider performance tracking, race research, journalism, and educational content.
Capabilities

The FirstCycling MCP Server bridges the gap between AI assistants and the rich world of professional cycling data. By exposing a curated set of tools that query the FirstCycling API, it lets developers and journalists retrieve detailed rider biographies, race results, team histories, and statistical insights—all within a single conversational context. This eliminates the need to manually scrape websites or manage separate data pipelines, enabling instant access to up‑to‑date cycling information directly from Claude or any MCP‑compatible client.
At its core, the server provides a suite of high‑level tools such as , , and . These functions return structured JSON containing biographical details, performance summaries, and race participation histories. The API is built on top of a web‑scraping layer that pulls data from the FirstCycling website, ensuring that the information reflects current standings and recent race outcomes. Developers can therefore construct complex queries—like comparing Grand Tour performances of two riders or generating a start list for an upcoming race—without writing custom scrapers.
Key capabilities include:
- Comprehensive rider profiles: nationality, birthdate, physical stats, current team, and UCI ranking trends.
- Granular race results: Grand Tours, Monument classics, stage profiles, and victory tables.
- Historical context: age records for race winners, career progression timelines, and team affiliation changes.
- Analytical tools: performance tracking over seasons, specialization analysis (e.g., classics vs. stage races), and comparative studies between riders.
Real‑world scenarios are abundant: sports journalists can automatically draft rider profiles or race previews; data scientists can ingest structured results into machine‑learning models to predict future performances; educators can generate interactive lessons on the evolution of professional cycling. In an AI workflow, a user might ask Claude, “Show me Tadej Pogačar’s Tour de France results from 2020 to 2023,” and the assistant will invoke , parse the response, and present a concise summary—all without leaving the chat interface.
What sets this server apart is its focus on domain‑specific depth. While generic sports APIs offer broad coverage, FirstCycling’s data includes niche metrics such as monument classic standings and detailed stage profiles that are rarely available elsewhere. Coupled with the MCP’s flexible tool invocation, developers gain a powerful, low‑friction gateway to authoritative cycling statistics, making it an indispensable asset for any application that needs timely, accurate professional cycling information.
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