This College Football Data MCP server, developed by Chris Leonard, provides AI assistants with access to comprehensive college football statistics via the College Football Data API. Built with Python and leveraging libraries like httpx and pydantic, it offers a natural language interface for querying game results, team records, player stats, rankings, and advanced metrics. The server implements robust error handling, rate limiting, and caching to optimize API usage. By abstracting the complexities of data retrieval and analysis, it enables AI systems to generate insights on team performance, analyze historical trends, and compare statistics across seasons. This implementation is particularly valuable for sports analysts, researchers, and fans seeking in-depth college football data analysis, facilitating use cases such as game prediction, player evaluation, and historical performance comparisons.
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Highlight AIRetrieve game data from the College Football Data API.
Get team records for the specified teams.
Access team game statistics for specific teams.
Query play-by-play data for a specific game or season.
Analyze drive information for specific games.
View individual play statistics for a game.
Check team rankings across different polls.
See win probabilities for games before they start.
Access detailed game statistics and analytics.
