MCP server

Prediction Markets Quant

com.predictionmarketspicks/quant

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Free, no key: NFL + NBA prop boards, NHL model, Kalshi vs Polymarket gaps, Fed odds, EV/Kelly.

Website Source

At a glance

Type

MCP server

Publisher

predictionmarketspicks.com (verified by the MCP Registry)

Status

Working at last check

Tools

39

Token cost

About 10,189 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http quant https://predictionmarketspicks.com/api/mcp/mcp?via=registry

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.quant]
url = "https://predictionmarketspicks.com/api/mcp/mcp?via=registry"

Gemini CLI

{"mcpServers":{"quant":{"httpUrl":"https://predictionmarketspicks.com/api/mcp/mcp?via=registry"}}}

Cursor

{"mcpServers":{"quant":{"url":"https://predictionmarketspicks.com/api/mcp/mcp?via=registry"}}}

VS Code

{"servers":{"quant":{"type":"http","url":"https://predictionmarketspicks.com/api/mcp/mcp?via=registry"}}}

Antigravity

agy mcp add quant https://predictionmarketspicks.com/api/mcp/mcp?via=registry

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"quant","server_url":"https://predictionmarketspicks.com/api/mcp/mcp?via=registry","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"quant","server_url":"https://predictionmarketspicks.com/api/mcp/mcp?via=registry"}

Tools

• bayes_update ~400 tokens
  Update a prior probability with one or more pieces of evidence using Bayes theorem. Given a prior and a list of evidence items (each with P(evidence | true) and P(evidence | false)), returns the posterior probability and the per-step chain. Use for "update my estimate with new information", "posteri
• combo_edge ~568 tokens
  Use for "price a same-game NFL combo" and "is this combo worth it". Pass nflGame ("NYG-LAR", "Giants vs Rams") to list that game's Kalshi legs — moneyline, spreads, totals, player props — then legIds to price your pick with the correlation done for you. Or pass legPrices + trueWinProbability for a f
• edge_alerts ~300 tokens
  Use for "any edge on Kalshi", "where is the edge today" and "weather trade signals". Our model alerts on Kalshi — weather, bitcoin/silver/gold/oil, mispricings — with feed, tier, side, price, model probability, edge and link. Real time with Pro; free is delayed 24h without the thesis. One identical,
• explain_game ~202 tokens
  Why our model prices one NFL game where it does: Kalshi price and market line, our win probability and its anchor, the model's lean in points, each team's offense/defense/special-teams rating, the QB gate, and the published position or why none. Use for "why do you price <team> vs <team> there", "ex
• explain_ladder ~296 tokens
  Why The Ladder reads one NFL strike ladder (a game spread, a season win total or a player prop) the way it does: the read in words, where Kalshi's ladder centres beside our projection, the widest published gap, and each strike with Kalshi's price and our number (30–70% only). Use for "explain the Ch
• explain_prop ~240 tokens
  Why we publish a call on one NFL player prop this week: the contract, the YES/NO call and Kalshi's price, our tier (and calibrated odds where measured), our projection vs the threshold, published or held at WATCH and why, and what the model does not account for. Use for "why do you like <player>'s o
• ladder_arb ~314 tokens
  Use for "is any CFB ladder mispriced" and "where can I make a market on Kalshi". Kalshi college football and NFL spread/total strikes priced out of order: locked arbitrage, crossed-mid inversions and wide books worth making a market in, with both tickers and books, gross edge, volume and tier. Pro a
• nfl_edge ~309 tokens
  Use for "which NFL games are mispriced on Kalshi" and "NFL win total, MVP or Super Bowl edges". Our NFL model vs live Kalshi prices — this week's game positions, season win totals, MVP and championship odds: model probability, price, edge and side. tickers[] checks a watchlist (futures, mvp, champio
• nfl_ladder ~308 tokens
  Use for "where does the model disagree with Kalshi on the Rams spread" and "biggest ladder gaps this week". Every NFL strike ladder — spreads, season win totals, player props — with Kalshi's price at each strike beside our distribution and a SHAPE / LOCATION / PRICED / PARTIAL verdict. Our number is
• nfl_power_ratings ~247 tokens
  The PredictionMarketsPicks NFL power ratings — PWR for all 32 teams: points per game above an average team on a neutral field, where PWR = Off PR + Def PR + ST PR. Includes each team's rank and tier. Use for "best NFL teams by power rating", "NFL power rankings 2026", "is Baltimore overrated", "how
• nfl_prop_board ~415 tokens
  Use for "where is the best price on <player> receiving yards" and "is Kalshi cheaper than the books". Best price on every NFL prop this week: Kalshi (fee included) vs five books (DraftKings, FanDuel, BetRivers, Fanatics, ESPN) and the Novig and ProphetX exchanges — the best price per side, how much
• nfl_prop_edge ~244 tokens
  Use for "best NFL props today" and "receiving yards prop value". Our projection vs the Kalshi "X or more" prop line: the contract, the YES/NO call, our odds and Kalshi's price for that side, the projection and the edge, with WATCH rows labelled. Live in season. Pro — sign in with PredictionMarketsPi
• nfl_win_probability ~373 tokens
  Use for "who wins Chiefs vs 49ers" and "what does a -7 spread mean". Pass homeTeam + awayTeam (or game, or one team) for our QB-gated model line (raw power ratings if unscheduled), or a spread (home favored negative). Returns win, cover and over/under probability, the projected score and our own tot
• weather_board ~175 tokens
  Use for "Kalshi NYC high temperature odds". Every open Kalshi daily-high strike in 13 cities: YES price, settlement station and source, the strict-comparison rule, close time, and the NWS forecast high beside it. Pass city ("nyc", "KXHIGHCHI"). Market data only.

Browse

• Weather
• News
• Stocks and markets
• Crypto and blockchains

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