Sports Betting Mcp

io.github.seang1121Published by GitHub account seang1121v1.0.0Updated Oct 24, 2026

AI sports betting picks, odds, injuries & line movement. 1,353+ picks, 59.6% win rate.

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Overview

AI-generated overview

Gives an AI assistant sports betting picks, live odds, injury reports, line movement, and model performance stats for NBA, NHL, NCAAB, and MLB.

What you can do
Connects an MCP client to a sports betting analysis API and exposes about 12 tools. Tools cover today's picks and the top pick, pending and completed picks, per-game 12-agent analysis, live moneyline/spread/totals odds, alerts on line moves and injuries, win rates by sport and bet type, model stats, a user leaderboard, and a system health check. Every call returns structured data the assistant can rank, filter, or explain.
When to use it
Use it when you want an assistant to pull daily betting picks, odds, and market signals for the four covered leagues, or to check a published pick record before acting. It suits users who already follow sports betting and want the data inside their AI client rather than a separate site.
Requirements
Runs locally as a Python package (pip install sports-betting-mcp, Python 3.10+) over stdio, so a local Python runtime is needed. Requires an API key in the SPORTS_BETTING_API_KEY environment variable, obtained from the provider's site. The optional SPORTS_BETTING_API_URL variable sets the API base URL and defaults to a localhost address. Network access to the provider's API is required.
Before you install
The API key is a credential and is passed in the SPORTS_BETTING_API_KEY environment variable; keep it out of shared configs. The server reports betting picks and odds, which is gambling-related information and not financial advice; acting on it risks losing money. Some endpoints can log picks or register webhooks, which writes data to the provider's service. The default API URL points at localhost, so it must be set to the provider's host to work.

Installation

In SourceWeft

  1. Open Sports Betting Mcp in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

sports-betting-mcp

The first MCP server for sports betting. Give any AI agent live access to picks, odds, game analysis, and performance tracking across NBA, NHL, NCAAB, and MLB.

[Status] [Python] [PyPI] [License] [MCP] [Sports]

mcp-name: io.github.seang1121/sports-betting-mcp

What It Does

Connects any MCP-compatible AI agent to a live sports betting analysis system. Every pick is logged before tip-off and resolved against final scores. Nothing is cherry-picked.

MetricValue
Sports CoveredNBA, NHL, NCAAB, MLB
Bet TypesMoneyline, Spread, Totals
Pick Source12-agent consensus model
Total Picks3,859+ resolved
Platform Users30
Tools12 MCP tools
AuthAPI key (X-API-Key header)

Results by Sport

SportPicksWinsWin Rate
NBA1,26776260.1%
NHL1,14865657.1%
NCAAB1,14954947.8%
MLB28310938.5% (launched Apr 2026)

All results are queryable in real-time via the get_win_rate tool. Numbers update after every game.

Quick Start

Install

bash
pip install sports-betting-mcp

Add to Your MCP Client

Drop this into your MCP config (Claude Desktop, Cursor, Windsurf, Claude Code, etc.):

json
{  "mcpServers": {    "sports-betting": {      "command": "sports-betting-mcp",      "env": {        "SPORTS_BETTING_API_URL": "https://sportsbettingaianalyzer.com",        "SPORTS_BETTING_API_KEY": "your_api_key"      }    }  }}

Environment Variables

VariableRequiredDescription
SPORTS_BETTING_API_KEYYesYour API key for authentication
SPORTS_BETTING_API_URLNoAPI base URL (defaults to http://localhost:5000)

Works With

Any client that supports the Model Context Protocol:

ClientStatus
Claude DesktopFully supported
CursorFully supported
WindsurfFully supported
Claude Code (CLI)Fully supported
Any MCP ClientFully supported via stdio transport

Available Tools

12 tools. Every call returns structured data that AI agents can reason over, display, or act on.

Picks & Analysis

ToolWhat It Does
get_todays_picksAll AI picks with confidence scores and edge breakdowns, filterable by sport
get_top_pickSingle highest-confidence pick of the day
get_pending_picksCurrently unresolved picks that are still in play
get_completed_picksRecently resolved picks with W/L results -- verify the track record
analyze_gameFull 12-agent analysis on any game: consensus pick + edge breakdown

Odds & Market Data

ToolWhat It Does
get_live_oddsLive moneyline, spread, and totals for today's games
get_alertsActive alerts from the multi-agent system (line moves, injury impacts)

Performance & Stats

ToolWhat It Does
get_win_rateWin rate with breakdown by sport and bet type
get_model_statsModel performance: total picks, last-20 win rate, confidence tier breakdown
get_leaderboardUser rankings by win rate
get_system_statusHealth check -- uptime, database, scheduler status

How the Analysis Works

Each game runs through a multi-agent pipeline:

  1. 12 specialized agents evaluate the game independently -- covering momentum, matchups, injuries, rest, travel, public betting percentages, sharp money indicators, historical trends, and more.
  2. A consensus engine synthesizes all 12 opinions into a single pick with a confidence score.
  3. Edge calculation compares the model's implied probability against the current market line.
  4. Picks are logged before tip-off and resolved against final scores. No retroactive edits.

The confidence score and edge breakdown are included in every pick response, so your AI agent can filter, rank, or explain the reasoning behind any recommendation.

Full API — 22 Endpoints

The API has two tiers: Free (any API key) and Pro (approved access).

Free Tier (available to all API key holders)

EndpointMethodWhat It Does
/xk/pGETToday's AI picks with confidence scores
/xk/o/{sport}GETLive odds from FanDuel/BetMGM
/xk/wGETWin rate breakdown by sport and bet type
/xk/iGETActive injury report from Covers.com
/xk/mGETSignificant line movement (sharp money signals)
/xk/gamesGETToday's full schedule with odds snapshot and AI pick flags
/xk/statsGETModel performance by sport, bet type, confidence tier
/xk/lbGETLeaderboard — AI vs human win rates
/xk/newsGETSports news feed from RSS sources
/xk/qGETCurrently pending (unresolved) picks

Pro Tier (requires approved access)

EndpointMethodWhat It Does
/xk/fullGETFull structured picks — complete edge breakdowns, positive/negative edges, reasoning, scorecard, opposing side with odds, line movement, quality flags
/xk/analyzePOSTOn-demand 12-agent analysis for any game — send team names, get full consensus
/xk/resultsGETResolved picks with W/L, actual scores, profit, top contributing edges
/xk/clvGETClosing Line Value analysis — did the line move in our favor?
/xk/searchGETSearch all picks by team name with W/L record
/xk/logPOSTLog a pick (supports flip flag, pick source tracking)
/xk/log/batchPOSTBatch log up to 20 picks with per-pick success/duplicate/error status
/xk/historyGETYour pick history — pending, resolved, win rate
/xk/slipPOSTGenerate Nimrod bet slip image
/xk/webhookPOST/GET/DELETERegister webhooks for pick events
/xk/fullGETIncludes fade/flip data for every pick — opposing side, line, odds

Pro Pick Payload

Every pick from /xk/full includes:

json
{  "ai_pick": "Lakers -5.5",  "ai_verdict": "STRONG BET",  "ai_probability": "65%",  "ai_reasoning": "Lakers defense ranks top 5, opponent on B2B...",  "edges": [13 individual edge factors],  "top_edges": [top 5 positive],  "negative_edges": [top 3 negative],  "scorecard": [3 model scores],  "fade_pick": "Celtics +5.5",  "fade_team": "Celtics",  "fade_odds": "+105",  "fade_note": "Betting AGAINST the AI — take Celtics +5.5",  "line_moved": true,  "flags": ["line_moved"],  "data_quality": "good"}

Pick Source Tracking (for learning)

When logging picks, include pick_source to track decision types:

  • model_agree — following the AI's pick
  • flip — fading/betting against the AI
  • manual_override — custom pick

This enables win rate comparison between model-agree vs flip picks over time.

API Authentication

Authenticate with X-API-Key header. Get a free key at sportsbettingaianalyzer.com. Pro access requires admin approval.

Tech Stack

ComponentTechnology
RuntimePython 3.10+
ProtocolMCP (Model Context Protocol)
Transportstdio
BuildHatchling
DistributionPyPI (sports-betting-mcp)
BackendFlask + SQLite
Analysis12-agent consensus pipeline
SportsNBA, NHL, NCAAB, MLB

Related Projects

License

MIT

Source: README.md at commit e0fa825

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.0.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts