Sports Probabilities MCP — NFL, MLB & Soccer Odds for AI Agents

Ask your AI assistant what a team's odds actually are, and get a number that came from a simulation rather than a guess.

This is a Model Context Protocol (MCP) server. Connect it to Claude, ChatGPT, Cursor or any other MCP client and you can ask, in plain language:

• "What are the Chiefs' playoff odds right now?"
• "Rank the Premier League by title probability, not by points."
• "Where does your model disagree most with Kalshi on NFL playoff qualifiers?"
• "Re-run the NFL model trusting current records more and show me who moves."

Behind it sit three published Monte Carlo Actors that replay every remaining game on the real schedule, tens of thousands of times. This server is the conversational front door to them, so the numbers you get here are the same numbers those Actors produce — there is no second, divergent model hiding in here.

No API key for any sports data provider. No scraping on your side. Just standings, schedules and probabilities.

What it covers

Sport	Teams	Probabilities
NFL	all 32	playoffs, division, wild card, No. 1 seed
MLB	all 30	postseason, division, wild card, top seed
Soccer	17 leagues	title, top four, continental qualification, playoff, relegation

Soccer leagues: Premier League, EFL Championship, LaLiga, Serie A, Bundesliga, Ligue 1, Eredivisie, Primeira Liga, Süper Lig, Belgian Pro League, Scottish Premiership, Brasileirão, Liga MX, Liga Profesional (Argentina), Primera A (Colombia), MLS, Austrian Bundesliga. Any other ESPN soccer slug works too.

Tools

Tool	What it answers
get_team_probabilities	One team: every probability, current record, projected finish, and where it ranks.
get_league_probabilities	The whole league ordered by probability instead of by points or record.
compare_model_vs_market	Where the model disagrees with live Kalshi prices, with net edge after fees and a quarter-Kelly stake.
run_scenario	A fresh simulation under your own model assumptions, showing who moves and by how much.
get_probability_history	How a team's probability moved across the season, from an archived run history.

Model vs market

compare_model_vs_market pulls live contract prices from Kalshi's public API — KXNFLPLAYOFF for the NFL and KXMLBPLAYOFFS for MLB — and for every team reports:

• the model probability and the market's implied probability, and the gap between them;
• which side of the contract is better value, YES or NO;
• the fee, using Kalshi's taker schedule (a parabola in the contract price, largest at 50 cents);
• expected value per contract net of that fee, and the break-even probability;
• a quarter-Kelly stake, clipped by a per-position ceiling and then by a portfolio ceiling, so the suggested positions actually fit inside one bankroll.

For soccer, or to compare against a bookmaker instead of an exchange, pass your own prices in marketProbabilities and the same arithmetic runs on those.

This is research output, not betting advice. Probabilities are estimates from a model, market prices move, and nothing here knows anything about your circumstances.

Connecting it

Add the Actor's Standby URL to your MCP client, with an Apify API token as a bearer token:

{
  "mcpServers": {
    "sports-probabilities": {
      "url": "https://commodus67--sports-probabilities-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_API_TOKEN"
      }
    }
  }
}

The transport is Streamable HTTP and the server is stateless, so any instance can answer any request.

Running it as an ordinary Actor

Starting it as a normal run — not in Standby — answers one question and writes it to the dataset. It is a quick way to check a build, and it is useful on a schedule:

{ "sport": "nfl", "limit": 40 }

{ "sport": "soccer", "league": "Premier League", "metric": "relegation" }

How fresh the numbers are

Simulations are cached for a few hours and shared across every instance of the server, because probabilities barely move between games. Every answer carries a source block telling you when the model actually ran, how many simulations it used, and whether the answer came from cache. A run_scenario call always runs a fresh simulation.

Keywords

sports probabilities API, NFL playoff odds, MLB playoff odds, soccer title odds, Monte Carlo simulation, prediction markets, Kalshi, model vs market edge, Kelly criterion, MCP server, AI agent tools, sports analytics, betting odds research, relegation odds, wild card odds.