gg.machina/world-cup-intelligence

up checked 2026-11-11T02:38:36.472Z

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Publisher-provided title

Machina World Cup Intelligence

Publisher-provided description

World Cup 2026 match, team, player and market context from Machina Sports, the sports AI platform.

Summary by lookupd

Provides World Cup 2026 fixture data, team and player context, match forecasts, prediction-market prices and fan sentiment for agents to…

Install

Claude Code, remote (sse)

claude mcp add --transport sse world-cup-intelligence https://worldcup-mcp.org.machina.gg/sse

Codex, remote (sse)

codex mcp add world-cup-intelligence --url https://worldcup-mcp.org.machina.gg/sse

Cursor, remote (sse) → .cursor/mcp.json

{
  "mcpServers": {
    "world-cup-intelligence": {
      "type": "sse",
      "url": "https://worldcup-mcp.org.machina.gg/sse"
    }
  }
}

Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.

Tools (16 tools)

Publisher-provided text

• worldcup_health Whoami/smoke check — verifies your API key and the product surface. Cost: FREE (0 credits). Call this first to validate setup before spending cr
• worldcup_get_schedule Tournament fixtures with their event_urn — the discovery primitive every per-fixture tool builds on. Cost: 1 credit (data). Example: worldcup_ge
• worldcup_resolve Resolve ANY provider id or free-text name (team, player, fixture) to the canonical Machina entity + URN via the identity crosswalk. Cost: 1 cred
• worldcup_get_event_context Enriched single-fixture context: form, group standings slice, injuries, optional research extras. Cost: 1 credit (data). Example: worldcup_get_e
• worldcup_search_markets Search Kalshi + Polymarket World Cup markets in one normalized shape. Cost: 3 credits (market). Example: worldcup_search_markets(team="Brazil")
• worldcup_get_market_state Live state of ONE market: price, order-book depth, optional candles/trades history. Cost: 3 credits (market). Get market_id from worldcup_search
• worldcup_get_match_forecast Dixon-Coles model forecast for one fixture (1X2 probabilities, model-vs-market gaps). Cost: 12 credits (intelligence). Get event_urn from worldc
• worldcup_backtest_forecasts Historical accuracy report of the model over played fixtures (calibration + CLV ledger). Cost: 12 credits (intelligence). Example: worldcup_back
• worldcup_explain_market_move Grounded explanation of WHY a market moved: price history + live research, source-cited. Cost: 12 credits (intelligence). Get market_id from wor
• worldcup_find_market_edges Scan open markets for model-vs-market gaps (informational edge candidates across the tournament). Cost: 18 credits (edge). Informational only —
• worldcup_get_signal Per-fixture betting signal: fuses the model forecast with the best market price into recommendation + edge% + EV + fractional-Kelly stake. Cost:
• worldcup_market_watch Tournament-wide market-watch card: biggest market movers and informational edges across World Cup markets. Cost: 40 credits (skill card). ~20 mi
• worldcup_fan_pulse Social/news fan-sentiment card (buzz, narratives, breaking news) via live X/web search. Tournament-wide by default, or scoped to a team or fixtu
• worldcup_match_preview Grounded match preview card: form, key men, group context, market snapshot — source-cited, ready to publish. Cost: 40 credits (skill card). Exam
• worldcup_match_recap Post-match recap card, authored once the final whistle blows — result, narrative, market settlement. Cost: 40 credits (skill card). Example: wor
• worldcup_player_spotlight Grounded player card: performance context + narrative, source- cited. Cost: 40 credits (skill card). Example: worldcup_player_spotlight(player="

Details

• Version: 0.1.1
• Transports: sse
• Uptime (7 days): 100%
• Median latency: 648 ms
• First indexed: 2026-11-01T09:02:34.156Z
• Last seen in registry: 2026-11-01T09:02:34.166Z
• Website: https://machina.gg/world-cup-api

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