Most MCP servers are thin API wrappers. I wanted the flagship tools to actually run a model, so SportIQ has three "brains" on top of the data tools:



\- \`football\_simulate\_bracket\` — 10k-run Monte Carlo over the real 48-team World Cup 2026 format (Poisson xG + Elo)



\- \`cricket\_build\_dream11\_team\` — an exact ILP (PuLP/CBC) for the optimal fantasy XI



\- \`f1\_predict\_pit\_strategy\` — tyre-degradation fits on OpenF1 telemetry



MIT-licensed, Python 3.11 / FastMCP, \~500 tests with recorded cassettes (no live calls in CI), and a graceful fallback chain across 8+ flaky sports APIs so it degrades to cached data instead of dying.



Works with any MCP client. No install to try it — paste into [claude.ai/ChatGPT](http://claude.ai/ChatGPT) as a custom connector:



[https://sportiq-mcp-329580761892.us-central1.run.app/mcp](https://sportiq-mcp-329580761892.us-central1.run.app/mcp)



Local: \`uvx sportiq-mcp\`



Repo: [github.com/Ninjabeam20/SportIQ-MCP](http://github.com/Ninjabeam20/SportIQ-MCP) — would love feedback on the architecture / FallbackChain pattern.



Open-sourced an MCP server that gives any AI assistant 44 sports tools — and 3 of them run real models (Monte Carlo, ILP solver, tyre-deg model)